Drug Therapy

On Nov. 22, 2014, 8 years after he came back from Iraq with “crippling” posttraumatic stress disorder (PTSD), Jonathan Lubecky took his first dose of the psychedelic compound methylenedioxymethamphetamine (MDMA). Lubecky, a Marine, Army, and National Guard veteran, described his path to MDMA therapy in in the New Horizons in Health podcast.

After 5 suicide attempts and “the hundreds of times I thought about it or stood on a bridge or had a plan,” he felt he had run out of options. Then, in a counseling session, a psychiatric intern slid a piece of paper across the table to him. It read “Google MDMA PTSD.”

Luckily for Lubecky, a space in a clinical trial opened up, in which he had 8 hours of talk therapy with specially trained therapists, combined with MDMA. “MDMA is a tool that opens up the mind, body and spirit,” he said, “so you can heal and process all those memories and traumas that are causing yourissues. It puts you in a middle place where you can talk about trauma without having panic attacks, without your body betraying you, and look at it from a different perspective.” said he added, “It’s like doing therapy while being hugged by everyone who loves you in a bathtub full of puppies licking your face.” In 2023, 9 years after that first dose, Lubecky said, “I’ve been PTSD free longer than I had it.”

And now, in 2025, the research into psychedelic therapy for veterans like Lubecky is taking another step forward according to a report by Military.com. Nine VA facilities, in the Bronx, Los Angeles, Omaha, Palo Alto, Portland (Oregon), San Diego, San Francisco, West Haven, and White River Junction, are participating in long-term studies to test the safety and clinical impact of psychedelic compounds for PTSD, treatment-resistant depression, and anxiety disorders. 

Early trials from Johns Hopkins University, the Multidisciplinary Association for Psychedelic Studies (MAPS), and others found significant symptom reductions for some participants with chronic PTSD. MAPP2, the multisite phase 3 study that extended the findings of MAPP1, found that MDMA-assisted therapy significantly improved PTSD symptoms and functional impairment, compared with placebo-assisted therapy. Notably, of the 52 participants (including 16 veterans) 45 (86%) achieved a clinically meaningful benefit, and 37 (71%) no longer met criteria for PTSD by study end. Despite the promising findings, a US Food and Drug Administration (FDA) advisory panel recommended against approving the treatment.

In 2024 the VA issued a request for applications for proposals from its network of VA researchers and academic institutions to gather “definitive scientific evidence” on the potential efficacy and safety of psychedelic compounds, such as MDMA and psilocybin, when used in conjunction with psychotherapy. It would be the first time since the 1960s that the VA had funded research on such compounds. 

Funding proposals for such research have cycled in and out of Congress for years, but have gathered more steam in the last few years. The 2024 National Defense Authorization Act directed the US Department of Defense to establish a process for funding clinical research into the use of certain psychedelic substances to treat PTSD and traumatic brain injury. In April 2024, Representatives Lou Correa (D-CA) and Jack Bergman (R-MI), cochairs of the Psychedelics Advancing Therapies (PATH) caucus, introduced the Innovative Therapies Centers of Excellence Act of 2025, bipartisan legislation that would increase federally funded research on innovative therapies to treat veterans with PTSD, substance use disorder, and depression. It would also, if enacted, direct the VA to create ≥ 5 dedicated centers of excellence to study the therapeutic uses of psychedelic substances. The bill has also been endorsed by the American Legion, Veterans of Foreign Wars, Iraq and Afghanistan Veterans of America, Disabled American Veterans, and the Wounded Warrior Project.

The current administration has two strong high-level supporters of psychedelics research: VA Secretary Doug Collins and US Department of Health and Human Service Secretary Robert F. Kennedy Jr. Sec. Kennedy has castigated the FDA for what he calls “aggressive suppression” of alternative and complementary treatments, including psychedelics. This, although the FDA granted breakthrough therapy status for MDMA for treating PTSD and psilocybin for treating depression in 2018 and 2019, respectively, as well a pivotal draft guidance in 2023 for the development of psychedelic drugs for psychiatric disorders, substance use disorders, and various medical conditions.

Collins, citing an “eye-opening” discussion with Kennedy, enthusiastically backs the research into psychedelics. In a May 2025 hearing that was mainly a series of testy exchanges about his proposed budget slashing, he emphasized the importance of keeping and expanding VA programs and studies on psychedelic treatments, something he has been advocating for since the beginning of his appointment. “We want to make sure we’re not closing off any outlet for a veteran who could be helped by these programs,” he said. 

Taking the intern’s advice to look into MDMA, Jonathan Lubecky said, was one of the best decisions he’d ever made. But “it’s not the MDMA that fixes you,” he said. “It’s the therapy. It’s the therapist working with you and you doing the hard work.”

On Nov. 22, 2014, 8 years after he came back from Iraq with “crippling” posttraumatic stress disorder (PTSD), Jonathan Lubecky took his first dose of the psychedelic compound methylenedioxymethamphetamine (MDMA). Lubecky, a Marine, Army, and National Guard veteran, described his path to MDMA therapy in in the New Horizons in Health podcast.

After 5 suicide attempts and “the hundreds of times I thought about it or stood on a bridge or had a plan,” he felt he had run out of options. Then, in a counseling session, a psychiatric intern slid a piece of paper across the table to him. It read “Google MDMA PTSD.”

Luckily for Lubecky, a space in a clinical trial opened up, in which he had 8 hours of talk therapy with specially trained therapists, combined with MDMA. “MDMA is a tool that opens up the mind, body and spirit,” he said, “so you can heal and process all those memories and traumas that are causing yourissues. It puts you in a middle place where you can talk about trauma without having panic attacks, without your body betraying you, and look at it from a different perspective.” said he added, “It’s like doing therapy while being hugged by everyone who loves you in a bathtub full of puppies licking your face.” In 2023, 9 years after that first dose, Lubecky said, “I’ve been PTSD free longer than I had it.”

And now, in 2025, the research into psychedelic therapy for veterans like Lubecky is taking another step forward according to a report by Military.com. Nine VA facilities, in the Bronx, Los Angeles, Omaha, Palo Alto, Portland (Oregon), San Diego, San Francisco, West Haven, and White River Junction, are participating in long-term studies to test the safety and clinical impact of psychedelic compounds for PTSD, treatment-resistant depression, and anxiety disorders. 

Early trials from Johns Hopkins University, the Multidisciplinary Association for Psychedelic Studies (MAPS), and others found significant symptom reductions for some participants with chronic PTSD. MAPP2, the multisite phase 3 study that extended the findings of MAPP1, found that MDMA-assisted therapy significantly improved PTSD symptoms and functional impairment, compared with placebo-assisted therapy. Notably, of the 52 participants (including 16 veterans) 45 (86%) achieved a clinically meaningful benefit, and 37 (71%) no longer met criteria for PTSD by study end. Despite the promising findings, a US Food and Drug Administration (FDA) advisory panel recommended against approving the treatment.

In 2024 the VA issued a request for applications for proposals from its network of VA researchers and academic institutions to gather “definitive scientific evidence” on the potential efficacy and safety of psychedelic compounds, such as MDMA and psilocybin, when used in conjunction with psychotherapy. It would be the first time since the 1960s that the VA had funded research on such compounds. 

Funding proposals for such research have cycled in and out of Congress for years, but have gathered more steam in the last few years. The 2024 National Defense Authorization Act directed the US Department of Defense to establish a process for funding clinical research into the use of certain psychedelic substances to treat PTSD and traumatic brain injury. In April 2024, Representatives Lou Correa (D-CA) and Jack Bergman (R-MI), cochairs of the Psychedelics Advancing Therapies (PATH) caucus, introduced the Innovative Therapies Centers of Excellence Act of 2025, bipartisan legislation that would increase federally funded research on innovative therapies to treat veterans with PTSD, substance use disorder, and depression. It would also, if enacted, direct the VA to create ≥ 5 dedicated centers of excellence to study the therapeutic uses of psychedelic substances. The bill has also been endorsed by the American Legion, Veterans of Foreign Wars, Iraq and Afghanistan Veterans of America, Disabled American Veterans, and the Wounded Warrior Project.

The current administration has two strong high-level supporters of psychedelics research: VA Secretary Doug Collins and US Department of Health and Human Service Secretary Robert F. Kennedy Jr. Sec. Kennedy has castigated the FDA for what he calls “aggressive suppression” of alternative and complementary treatments, including psychedelics. This, although the FDA granted breakthrough therapy status for MDMA for treating PTSD and psilocybin for treating depression in 2018 and 2019, respectively, as well a pivotal draft guidance in 2023 for the development of psychedelic drugs for psychiatric disorders, substance use disorders, and various medical conditions.

Collins, citing an “eye-opening” discussion with Kennedy, enthusiastically backs the research into psychedelics. In a May 2025 hearing that was mainly a series of testy exchanges about his proposed budget slashing, he emphasized the importance of keeping and expanding VA programs and studies on psychedelic treatments, something he has been advocating for since the beginning of his appointment. “We want to make sure we’re not closing off any outlet for a veteran who could be helped by these programs,” he said. 

Taking the intern’s advice to look into MDMA, Jonathan Lubecky said, was one of the best decisions he’d ever made. But “it’s not the MDMA that fixes you,” he said. “It’s the therapy. It’s the therapist working with you and you doing the hard work.”

On Nov. 22, 2014, 8 years after he came back from Iraq with “crippling” posttraumatic stress disorder (PTSD), Jonathan Lubecky took his first dose of the psychedelic compound methylenedioxymethamphetamine (MDMA). Lubecky, a Marine, Army, and National Guard veteran, described his path to MDMA therapy in in the New Horizons in Health podcast.

After 5 suicide attempts and “the hundreds of times I thought about it or stood on a bridge or had a plan,” he felt he had run out of options. Then, in a counseling session, a psychiatric intern slid a piece of paper across the table to him. It read “Google MDMA PTSD.”

Luckily for Lubecky, a space in a clinical trial opened up, in which he had 8 hours of talk therapy with specially trained therapists, combined with MDMA. “MDMA is a tool that opens up the mind, body and spirit,” he said, “so you can heal and process all those memories and traumas that are causing yourissues. It puts you in a middle place where you can talk about trauma without having panic attacks, without your body betraying you, and look at it from a different perspective.” said he added, “It’s like doing therapy while being hugged by everyone who loves you in a bathtub full of puppies licking your face.” In 2023, 9 years after that first dose, Lubecky said, “I’ve been PTSD free longer than I had it.”

And now, in 2025, the research into psychedelic therapy for veterans like Lubecky is taking another step forward according to a report by Military.com. Nine VA facilities, in the Bronx, Los Angeles, Omaha, Palo Alto, Portland (Oregon), San Diego, San Francisco, West Haven, and White River Junction, are participating in long-term studies to test the safety and clinical impact of psychedelic compounds for PTSD, treatment-resistant depression, and anxiety disorders. 

Early trials from Johns Hopkins University, the Multidisciplinary Association for Psychedelic Studies (MAPS), and others found significant symptom reductions for some participants with chronic PTSD. MAPP2, the multisite phase 3 study that extended the findings of MAPP1, found that MDMA-assisted therapy significantly improved PTSD symptoms and functional impairment, compared with placebo-assisted therapy. Notably, of the 52 participants (including 16 veterans) 45 (86%) achieved a clinically meaningful benefit, and 37 (71%) no longer met criteria for PTSD by study end. Despite the promising findings, a US Food and Drug Administration (FDA) advisory panel recommended against approving the treatment.

In 2024 the VA issued a request for applications for proposals from its network of VA researchers and academic institutions to gather “definitive scientific evidence” on the potential efficacy and safety of psychedelic compounds, such as MDMA and psilocybin, when used in conjunction with psychotherapy. It would be the first time since the 1960s that the VA had funded research on such compounds. 

Funding proposals for such research have cycled in and out of Congress for years, but have gathered more steam in the last few years. The 2024 National Defense Authorization Act directed the US Department of Defense to establish a process for funding clinical research into the use of certain psychedelic substances to treat PTSD and traumatic brain injury. In April 2024, Representatives Lou Correa (D-CA) and Jack Bergman (R-MI), cochairs of the Psychedelics Advancing Therapies (PATH) caucus, introduced the Innovative Therapies Centers of Excellence Act of 2025, bipartisan legislation that would increase federally funded research on innovative therapies to treat veterans with PTSD, substance use disorder, and depression. It would also, if enacted, direct the VA to create ≥ 5 dedicated centers of excellence to study the therapeutic uses of psychedelic substances. The bill has also been endorsed by the American Legion, Veterans of Foreign Wars, Iraq and Afghanistan Veterans of America, Disabled American Veterans, and the Wounded Warrior Project.

The current administration has two strong high-level supporters of psychedelics research: VA Secretary Doug Collins and US Department of Health and Human Service Secretary Robert F. Kennedy Jr. Sec. Kennedy has castigated the FDA for what he calls “aggressive suppression” of alternative and complementary treatments, including psychedelics. This, although the FDA granted breakthrough therapy status for MDMA for treating PTSD and psilocybin for treating depression in 2018 and 2019, respectively, as well a pivotal draft guidance in 2023 for the development of psychedelic drugs for psychiatric disorders, substance use disorders, and various medical conditions.

Collins, citing an “eye-opening” discussion with Kennedy, enthusiastically backs the research into psychedelics. In a May 2025 hearing that was mainly a series of testy exchanges about his proposed budget slashing, he emphasized the importance of keeping and expanding VA programs and studies on psychedelic treatments, something he has been advocating for since the beginning of his appointment. “We want to make sure we’re not closing off any outlet for a veteran who could be helped by these programs,” he said. 

Taking the intern’s advice to look into MDMA, Jonathan Lubecky said, was one of the best decisions he’d ever made. But “it’s not the MDMA that fixes you,” he said. “It’s the therapy. It’s the therapist working with you and you doing the hard work.”

Cancer patients experience depression at rates > 5 times that of the general population.^1-11 Despite an increase in palliative care use, depression rates continued to rise.^2-4 Between 5% to 16% of outpatients, 4% to 14% of inpatients, and up to 49% of patients receiving palliative care experience depression.⁵ This issue also impacts families and caregivers.¹ A 2021 meta-analysis found that 23% of active military personnel and 20% of veterans experience depression.¹¹

Antidepressants approved by the US Food and Drug Administration (FDA) target the serotonin, norepinephrine, or dopamine systems and include boxed warnings about an increased risk of suicidal thoughts in adults aged 18 to 24 years.^12,13 These medications are categorized into several classes: monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), norepinephrine-dopamine reuptake inhibitors (NDRIs), selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), serotonin receptor modulators (SRMs), serotonin-melatonin receptor antagonists (SMRAs), and N—methyl-D-aspartate receptor antagonists (NMDARAs).^14,15 The first FDA-approved antidepressants, iproniazid (an MAOI) and imipramine (a TCA) laid the foundation for the development of newer classes like SSRIs and SNRIs.^15-17

Older antidepressants such as MAOIs and TCAs are used less due to their adverse effects (AEs) and drug interactions. MAOIs, such as iproniazid, selegiline, moclobemide, tranylcypromine, isocarboxazid, and phenelzine, have numerous AEs and drug interactions, making them unsuitable for first- or second-line treatment of depression.^14,18-21 TCAs such as doxepin, amitriptyline, nortriptyline, imipramine, desipramine, clomipramine, trimipramine, protriptyline, maprotiline, and amoxapine have a narrow therapeutic index requiring careful monitoring for signs of toxicity such as QRS widening, tremors, or confusion. Despite the issues, TCAs are generally classified as second-line agents for major depressive disorder (MDD). TCAs have off-label uses for migraine prophylaxis, treatment of obsessive-compulsive disorder (OCD), insomnia, and chronic pain management first-line.^14,22-29

Newer antidepressants, including TeCAs and NDRIs, are typically more effective, but also come with safety concerns. TeCAs like mirtazapine interact with several medications, including MAOIs, serotonin-increasing drugs, alcohol, cannabidiol, and marijuana. Mirtazapine is FDA-approved for the treatment of moderate to severe depression in adults. It is also used off-label to treat insomnia, panic disorder, posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), headaches, and migraines. Compared to other antidepressants, mirtazapine is effective for all stages of depression and addresses a broad range of related symptoms.^14,30-34 NDRIs, such as bupropion, also interact with various medications, including MAOIs, other antidepressants, stimulants, and alcohol. Bupropion is FDA-approved for smoking cessation and to treat depression and SAD. It is also used off-label for depression- related bipolar disorder or sexual dysfunction, attention-deficit/hyperactivity disorder (ADHD), and obesity.^14,35-42

SSRIs, SNRIs, and SRMs should be used with caution. SSRIs such as sertraline, citalopram, escitalopram, fluoxetine, paroxetine, and fluvoxamine are first-line treatments for depression and various psychiatric disorders due to their safety and efficacy. Common AEs of SSRIs include sexual dysfunction, sleep disturbances, weight changes, and gastrointestinal (GI) issues. SSRIs can prolong the QT interval, posing a risk of life-threatening arrhythmia, and may interact with other medications, necessitating treatment adjustments. The FDA approved SSRIs for MDD, GAD, bulimia nervosa, bipolar depression, OCD, panic disorder, premenstrual dysphoric disorder, treatment-resistant depression, PTSD, and SAD. Off-label uses include binge eating disorder, body dysmorphic disorder, fibromyalgia, premature ejaculation, paraphilias, autism, Raynaud phenomenon, and vasomotor symptoms associated with menopause. Among SSRIs, sertraline and escitalopram are noted for their effectiveness and tolerability.^14,43-53

SNRIs, including duloxetine, venlafaxine, desvenlafaxine, milnacipran, and levomilnacipran, may increase bleeding risk, especially when taken with blood thinners. They can also elevate blood pressure, which may worsen if combined with stimulants. SNRIs may interact with other medications that affect serotonin levels, increasing the risk of serotonin syndrome when taken with triptans, pain medications, or other antidepressants.¹⁴ Desvenlafaxine has been approved by the FDA (but not by the European Medicines Agency).^54-56 Duloxetine is FDA-approved for the treatment of depression, neuropathic pain, anxiety disorders, fibromyalgia, and musculoskeletal disorders. It is used off-label to treat chemotherapy-induced peripheral neuropathy and stress urinary incontinence.^57-61 Venlafaxine is FDA-approved for depression, SAD, and panic disorder, and is prescribed off-label to treat ADHD, neuropathy, fibromyalgia, cataplexy, and PTSD, either alone or in combination with other medications.^62,63 Milnacipran is not approved for MDD; levomilnacipran received approval in 2013.⁶⁴

SRMs such as trazodone, nefazodone, vilazodone, and vortioxetine also function as serotonin reuptake inhibitors.^14,15 Trazodone is FDA-approved for MDD. It has been used off-label to treat anxiety, Alzheimer disease, substance misuse, bulimia nervosa, insomnia, fibromyalgia, and PTSD when first-line SSRIs are ineffective. A notable AE of trazodone is orthostatic hypotension, which can lead to dizziness and increase the risk of falls, especially in geriatric patients.^65-70 Nefazodone was discontinued in Europe in 2003 due to rare cases of liver toxicity but remains available in the US.^71-74 Vilazodone and vortioxetine are FDA-approved.

The latest classes of antidepressants include SMRAs and NMDARAs.¹⁴ Agomelatine, an SMRA, was approved in Europe in 2009 but rejected by the FDA in 2011 due to liver toxicity.⁷⁵ NMDARAs like esketamine and a combination of dextromethorphan and bupropion received FDA approval in 2019 and 2022, respectively.^76,77

This retrospective study analyzes noncancer drugs used during systemic chemotherapy based on a dataset of 14 antineoplastic agents. It sought to identify the most dispensed noncancer drug groups, discuss findings, compare patients with and without antidepressant prescriptions, and examine trends in antidepressant use from 2002 to 2023. This analysis expands on prior research.^78-81

Methods

The Walter Reed National Military Medical Center Institutional Review Board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and Military Health System (MHS) data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

Data Sources

The JPC DoD Cancer Registry Program contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in CAPER represents an ambulatory encounter at a military treatment facility (MTF). CAPER records are available from 2003 to 2024. PDTS records are available from 2002 to 2004. Each observation in PDTS represents a prescription filled for an MHS beneficiary, excluding those filled at international civilian pharmacies and inpatient pharmacy prescriptions.

This cross-sectional analysis requested data extraction for specific cancer drugs from the DoD Cancer Registry, focusing on treatment details, diagnosis dates, patient demographics, and physicians’ comments on AEs. After identifying patients, CAPER was used to identify additional health conditions. PDTS was used to compile a list of prescription medications filled during systemic cancer treatment or < 2 years postdiagnosis.

The 2016 Surveillance, Epidemiology, and End Results Program Coding and Staging Manual and International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^82,83 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the subgroup divided by the total number of patients or data variables. To compare the mean number of dispensed antidepressants to those without antidepressants, a 2-tailed, 2-sample z test was used to calculate the P value and determine statistical significance (P < .05) using socscistatistics.com.

Data were extracted 3 times between 2021 and 2023. The initial 2021 protocol focused on erlotinib and gefitinib. A modified protocol in 2022 added paclitaxel, cisplatin, docetaxel, pemetrexed, and crizotinib; further modification in 2023 included 8 new antineoplastic agents and 2 anticoagulants. Sotorasib has not been prescribed in the MHS, and JPC lacks records for noncancer drugs. The 2023 dataset comprised 2210 patients with cancer treated with 14 antineoplastic agents; 2104 had documented diagnoses and 2113 had recorded prescriptions. Data for erlotinib, gefitinib, and paclitaxel have been published previously.^78,79

Results

Of 2113 patients with recorded prescriptions, 1297 patients (61.4%) received 109 cancer drugs, including 96 antineoplastics, 7 disease-modifying antirheumatic agents, 4 biologic response modifiers, and 2 calcitonin gene-related peptides. Fourteen antineoplastic agents had complete data from JPC, while others were noted for combination therapies or treatment switches from the PDTS (Table 1). Seventy-six cancer drugs were prescribed with antidepressants in 489 patients (eAppendix).

The JPC provided 2242 entries for 2210 patients, ranging in age from 2 months to 88 years (mean, 56 years), documenting treatment from September 1988 to January 2023. Thirty-two patients had duplicate entries due to multiple cancer locations or occurrences. Of the 2242 patients, 1541 (68.7%) were aged > 50 years, 975 patients (43.5%) had cancers that were stage III or IV, and 1267 (56.5%) had cancers that were stage 0, I, II, or not applicable/unknown. There were 51 different types of cancer: breast, lung, testicular, endometrial, and ovarian were most common (n ≥ 100 patients). Forty-two cancer types were documented among 750 patients prescribed antidepressants (Table 2).

The CAPER database recorded 8882 unique diagnoses for 2104 patients, while PDTS noted 1089 unique prescriptions within 273 therapeutic codes for 2113 patients. Nine therapeutic codes (opiate agonists, adrenals, cathartics-laxatives, nonsteroidal anti-inflammatory agents, antihistamines for GI conditions, 5-HT3 receptor antagonists, analgesics and antipyretic miscellanea, antineoplastic agents, and proton-pump inhibitors) and 8 drugs (dexamethasone, prochlorperazine, ondansetron, docusate, acetaminophen, ibuprofen, oxycodone, and polyethylene glycol 3350) were associated with > 1000 patients (≥ 50%). Patients had between 1 and 275 unique health conditions and filled 1 to 108 prescriptions. The mean (SD) number of diagnoses and prescriptions was 50 (28) and 29 (12), respectively. Of the 273 therapeutic codes, 30 groups were analyzed, with others categorized into miscellaneous groups such as lotions, vaccines, and devices. Significant differences in mean number of prescriptions were found for patients taking antidepressants compared to those not (P < .05), except for anticonvulsants and antipsychotics (P = .12 and .09, respectively) (Table 3).

Antidepressants

Of the 2113 patients with recorded prescriptions, 750 (35.5%) were dispensed 17 different antidepressants. Among these 17 antidepressants, 183 (8.7%) patients received duloxetine, 158 (7.5%) received venlafaxine, 118 (5.6%) received trazodone, and 107 (5.1%) received sertraline (Figure 1, Table 4). Of the 750 patients, 509 (67.9%) received 1 antidepressant, 168 (22.4%) received 2, 60 (8.0%) received 3, and 13 (1.7%) received > 3. Combinations varied, but only duloxetine and trazodone were prescribed to > 10 patients.

Antidepressants were prescribed annually at an overall mean (SD) rate of 23% (5%) from 2003 to 2022 (Figure 2). Patients on antidepressants during systemic therapy had a greater number of diagnosed medical conditions and received more prescription medications compared to those not taking antidepressants (P < .001) (Figure 3). The 745 patients taking antidepressants in CAPER data had between 1 and 275 diagnosed medical issues, with a mean (SD) of 55 (31) vs a range of 1 to 209 and a mean (SD) of 46 (26) for the 1359 patients not taking antidepressants. The 750 patients on antidepressants in PDTS data had between 8 and 108 prescriptions dispensed, with a mean (SD) of 32 (12), vs a range of 1 to 65 prescriptions and a mean (SD) of 29 (12) for 1363 patients not taking antidepressants.

Discussion

The JPC DoD Cancer Registry includes information on cancer types, stages, treatment regimens, and physicians’ notes, while noncancer drugs are sourced from the PDTS database. The pharmacy uses a different documentation system, leading to varied classifications.

Database reliance has its drawbacks. For example, megestrol is coded as a cancer drug, although it’s primarily used for endometrial or gynecologic cancers. Many drugs have multiple therapeutic codes assigned to them, including 10 antineoplastic agents: diclofenac, Bacillus Calmette-Guérin (BCG), megestrol acetate, tamoxifen, anastrozole, letrozole, leuprolide, goserelin, degarelix, and fluorouracil. Diclofenac, BCG, and mitomycin have been repurposed for cancer treatment.^84-87 From 2003 to 2023, diclofenac was prescribed to 350 patients for mild-to-moderate pain, with only 2 patients receiving it for cancer in 2018. FDA-approved for bladder cancer in 1990, BCG was prescribed for cancer treatment for 1 patient in 2021 after being used for vaccines between 2003 and 2018. Tamoxifen, used for hormone receptor-positive breast cancer from 2004 to 2017 with 53 patients, switched to estrogen agonist-antagonists from 2017 to 2023 with 123 patients. Only a few of the 168 patients were prescribed tamoxifen using both codes.^88-91 Anastrozole and letrozole were coded as antiestrogens for 7 and 18 patients, respectively, while leuprolide and goserelin were coded as gonadotropins for 59 and 18 patients. Degarelix was coded as antigonadotropins, fluorouracil as skin and mucous membrane agents miscellaneous, and megestrol acetate as progestins for 7, 6, and 3 patients, respectively. Duloxetine was given to 186 patients, primarily for depression from 2005 to 2023, with 7 patients treated for fibromyalgia from 2022 to 2023.

Antidepressants Observed

Tables 1 and 5 provide insight into the FDA approval of 14 antineoplastics and antidepressants and their CYP metabolic pathways.^92-122 In Table 4, the most prescribed antidepressant classes are SNRIs, SRMs, SSRIs, TeCAs, NDRIs, and TCAs. This trend highlights a preference for newer medications with weak CYP inhibition. A total of 349 patients were prescribed SSRIs, 343 SNRIs, 119 SRMs, 109 TCAs, 83 TeCAs, and 79 NDRIs. MAOIs, SMRAs, and NMDARAs were not observed in this dataset. While there are instances of dextromethorphan-bupropion and sertraline-escitalopram being dispensed together, it remains unclear whether these were NMDARA combinations.

Among the 14 specific antineoplastic agents, 10 are metabolized by CYP isoenzymes, primarily CYP3A4. Duloxetine neither inhibits nor is metabolized by CYP3A4, a reason it is often recommended, following venlafaxine.

Both duloxetine and venlafaxine are used off-label for chemotherapy-induced peripheral neuropathy related to paclitaxel and docetaxel. According to the CYP metabolized pathway, duloxetine tends to have more favorable DDIs than venlafaxine. In PDTS data, 371 patients were treated with paclitaxel and 180 with docetaxel, with respective antidepressant prescriptions of 156 and 70. Of the 156 patients dispensed paclitaxel, 62 (40%) were dispensed with duloxetine compared to 43 (28%) with venlafaxine. Of the 70 patients dispensed docetaxel, 23 (33%) received duloxetine vs 24 (34%) with venlafaxine.

Of 85 patients prescribed duloxetine, 75 received it with either paclitaxel or docetaxel (5 received both). Five patients had documented AEs (1 neuropathy related). Of 67 patients prescribed venlafaxine, 66 received it with either paclitaxel or docetaxel. Two patients had documented AEs (1 was neuropathy related, the same patient who received duloxetine). Of the 687 patients treated with paclitaxel and 337 with docetaxel in all databases, 4 experienced neuropathic AEs from both medications.⁷⁹

Antidepressants can increase the risk of bleeding, especially when combined with blood thinners, and may elevate blood pressure, particularly alongside stimulants. Of the 554 patients prescribed 9 different anticoagulants, enoxaparin, apixaban, and rivaroxaban were the most common (each > 100 patients). Among these, 201 patients (36%) received both anticoagulants and antidepressants: duloxetine for 64 patients, venlafaxine for 30, trazodone for 35, and sertraline for 26. There were no data available to assess bleeding rates related to the evaluation of DDIs between these medication classes.

Antidepressants can be prescribed for erectile dysfunction. Of the 148 patients prescribed an antidepressant for erectile dysfunction, duloxetine, trazodone, and mirtazapine were the most common. Antidepressant preferences varied by cancer type. Duloxetine was the only antidepressant used for all types of cancer. Venlafaxine, duloxetine, trazodone, sertraline, and escitalopram were the most prescribed antidepressants for breast cancer, while duloxetine, mirtazapine, citalopram, sertraline, and trazodone were the most prescribed for lung cancer. Sertraline, duloxetine, trazodone, amitriptyline, and escitalopram were most common for testicular cancer. Duloxetine, venlafaxine, trazodone, amitriptyline, and sertraline were the most prescribed for endometrial cancer, while duloxetine, venlafaxine, amitriptyline, citalopram, and sertraline were most prescribed for ovarian cancer.

The broadness of International Statistical Classification of Diseases, Tenth Revision codes made it challenging to identify nondepression diagnoses in the analyzed population. However, if all antidepressants were prescribed to treat depression, service members with cancer exhibited a higher depression rate (35%) than the general population (25%). Of 2104 patients, 191 (9.1%) had mood disorders, and 706 (33.6%) had mental disorders: 346 (49.0%) had 1 diagnosis, and 360 (51.0%) had multiple diagnoses. The percentage of diagnoses varied yearly, with notable drops in 2003, 2007, 2011, 2014, and 2018, and peaks in 2006, 2008, 2013, 2017, and 2022. This fluctuation was influenced by events like the establishment of PDTS in 2002, the 2008 economic recession, a hospital relocation in 2011, the 2014 Ebola outbreak, and the COVID-19 pandemic. Although the number of patients receiving antidepressants increased from 2019 to 2022, the overall percentage of patients receiving them did not significantly change from 2003 to 2022, aligning with previous research.^5,125

Many medications have potential uses beyond what is detailed in the prescribing information. Antidepressants can relieve pain, while pain medications may help with depression. Opioids were once thought to effectively treat depression, but this perspective has changed with a greater understanding of their risks, including misuse.^126-131 Pain is a severe and often unbearable AE of cancer. Of 2113 patients, 92% received opioids; 34% received both opioids and antidepressants; 2% received only antidepressants; and 7% received neither. This study didn’t clarify whether those on opioids alone recognized their depression or if those on both were aware of their dependence. While SSRIs are generally not addictive, they can lead to physical dependence, and any medication can be abused if not managed properly.^132-134

Conclusions

This retrospective study analyzes data from antineoplastic agents used in systemic cancer treatment between 1988 and 2023, with a particular focus on the use of antidepressants. Data on antidepressant prescriptions are incomplete and specific to these agents, which means the findings cannot be generalized to all antidepressants. Hence, the results indicate that patients taking antidepressants had more diagnosed health issues and received more medications compared to patients who were not on these drugs.

This study underscores the need for further research into the effects of antidepressants on cancer treatment, utilizing all data from the DoD Cancer Registry. Future research should explore DDIs between antidepressants and other cancer and noncancer medications, as this study did not assess AE documentation, unlike in studies involving erlotinib, gefitinib, and paclitaxel.^78,79 Further investigation is needed to evaluate the impact of discontinuing antidepressant use during cancer treatment. This comprehensive overview provides insights for clinicians to help them make informed decisions regarding the prescription of antidepressants in the context of cancer treatment.

Cancer patients experience depression at rates > 5 times that of the general population.^1-11 Despite an increase in palliative care use, depression rates continued to rise.^2-4 Between 5% to 16% of outpatients, 4% to 14% of inpatients, and up to 49% of patients receiving palliative care experience depression.⁵ This issue also impacts families and caregivers.¹ A 2021 meta-analysis found that 23% of active military personnel and 20% of veterans experience depression.¹¹

Antidepressants approved by the US Food and Drug Administration (FDA) target the serotonin, norepinephrine, or dopamine systems and include boxed warnings about an increased risk of suicidal thoughts in adults aged 18 to 24 years.^12,13 These medications are categorized into several classes: monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), norepinephrine-dopamine reuptake inhibitors (NDRIs), selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), serotonin receptor modulators (SRMs), serotonin-melatonin receptor antagonists (SMRAs), and N—methyl-D-aspartate receptor antagonists (NMDARAs).^14,15 The first FDA-approved antidepressants, iproniazid (an MAOI) and imipramine (a TCA) laid the foundation for the development of newer classes like SSRIs and SNRIs.^15-17

Older antidepressants such as MAOIs and TCAs are used less due to their adverse effects (AEs) and drug interactions. MAOIs, such as iproniazid, selegiline, moclobemide, tranylcypromine, isocarboxazid, and phenelzine, have numerous AEs and drug interactions, making them unsuitable for first- or second-line treatment of depression.^14,18-21 TCAs such as doxepin, amitriptyline, nortriptyline, imipramine, desipramine, clomipramine, trimipramine, protriptyline, maprotiline, and amoxapine have a narrow therapeutic index requiring careful monitoring for signs of toxicity such as QRS widening, tremors, or confusion. Despite the issues, TCAs are generally classified as second-line agents for major depressive disorder (MDD). TCAs have off-label uses for migraine prophylaxis, treatment of obsessive-compulsive disorder (OCD), insomnia, and chronic pain management first-line.^14,22-29

Newer antidepressants, including TeCAs and NDRIs, are typically more effective, but also come with safety concerns. TeCAs like mirtazapine interact with several medications, including MAOIs, serotonin-increasing drugs, alcohol, cannabidiol, and marijuana. Mirtazapine is FDA-approved for the treatment of moderate to severe depression in adults. It is also used off-label to treat insomnia, panic disorder, posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), headaches, and migraines. Compared to other antidepressants, mirtazapine is effective for all stages of depression and addresses a broad range of related symptoms.^14,30-34 NDRIs, such as bupropion, also interact with various medications, including MAOIs, other antidepressants, stimulants, and alcohol. Bupropion is FDA-approved for smoking cessation and to treat depression and SAD. It is also used off-label for depression- related bipolar disorder or sexual dysfunction, attention-deficit/hyperactivity disorder (ADHD), and obesity.^14,35-42

SSRIs, SNRIs, and SRMs should be used with caution. SSRIs such as sertraline, citalopram, escitalopram, fluoxetine, paroxetine, and fluvoxamine are first-line treatments for depression and various psychiatric disorders due to their safety and efficacy. Common AEs of SSRIs include sexual dysfunction, sleep disturbances, weight changes, and gastrointestinal (GI) issues. SSRIs can prolong the QT interval, posing a risk of life-threatening arrhythmia, and may interact with other medications, necessitating treatment adjustments. The FDA approved SSRIs for MDD, GAD, bulimia nervosa, bipolar depression, OCD, panic disorder, premenstrual dysphoric disorder, treatment-resistant depression, PTSD, and SAD. Off-label uses include binge eating disorder, body dysmorphic disorder, fibromyalgia, premature ejaculation, paraphilias, autism, Raynaud phenomenon, and vasomotor symptoms associated with menopause. Among SSRIs, sertraline and escitalopram are noted for their effectiveness and tolerability.^14,43-53

SNRIs, including duloxetine, venlafaxine, desvenlafaxine, milnacipran, and levomilnacipran, may increase bleeding risk, especially when taken with blood thinners. They can also elevate blood pressure, which may worsen if combined with stimulants. SNRIs may interact with other medications that affect serotonin levels, increasing the risk of serotonin syndrome when taken with triptans, pain medications, or other antidepressants.¹⁴ Desvenlafaxine has been approved by the FDA (but not by the European Medicines Agency).^54-56 Duloxetine is FDA-approved for the treatment of depression, neuropathic pain, anxiety disorders, fibromyalgia, and musculoskeletal disorders. It is used off-label to treat chemotherapy-induced peripheral neuropathy and stress urinary incontinence.^57-61 Venlafaxine is FDA-approved for depression, SAD, and panic disorder, and is prescribed off-label to treat ADHD, neuropathy, fibromyalgia, cataplexy, and PTSD, either alone or in combination with other medications.^62,63 Milnacipran is not approved for MDD; levomilnacipran received approval in 2013.⁶⁴

SRMs such as trazodone, nefazodone, vilazodone, and vortioxetine also function as serotonin reuptake inhibitors.^14,15 Trazodone is FDA-approved for MDD. It has been used off-label to treat anxiety, Alzheimer disease, substance misuse, bulimia nervosa, insomnia, fibromyalgia, and PTSD when first-line SSRIs are ineffective. A notable AE of trazodone is orthostatic hypotension, which can lead to dizziness and increase the risk of falls, especially in geriatric patients.^65-70 Nefazodone was discontinued in Europe in 2003 due to rare cases of liver toxicity but remains available in the US.^71-74 Vilazodone and vortioxetine are FDA-approved.

The latest classes of antidepressants include SMRAs and NMDARAs.¹⁴ Agomelatine, an SMRA, was approved in Europe in 2009 but rejected by the FDA in 2011 due to liver toxicity.⁷⁵ NMDARAs like esketamine and a combination of dextromethorphan and bupropion received FDA approval in 2019 and 2022, respectively.^76,77

This retrospective study analyzes noncancer drugs used during systemic chemotherapy based on a dataset of 14 antineoplastic agents. It sought to identify the most dispensed noncancer drug groups, discuss findings, compare patients with and without antidepressant prescriptions, and examine trends in antidepressant use from 2002 to 2023. This analysis expands on prior research.^78-81

Methods

The Walter Reed National Military Medical Center Institutional Review Board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and Military Health System (MHS) data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

Data Sources

The JPC DoD Cancer Registry Program contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in CAPER represents an ambulatory encounter at a military treatment facility (MTF). CAPER records are available from 2003 to 2024. PDTS records are available from 2002 to 2004. Each observation in PDTS represents a prescription filled for an MHS beneficiary, excluding those filled at international civilian pharmacies and inpatient pharmacy prescriptions.

This cross-sectional analysis requested data extraction for specific cancer drugs from the DoD Cancer Registry, focusing on treatment details, diagnosis dates, patient demographics, and physicians’ comments on AEs. After identifying patients, CAPER was used to identify additional health conditions. PDTS was used to compile a list of prescription medications filled during systemic cancer treatment or < 2 years postdiagnosis.

The 2016 Surveillance, Epidemiology, and End Results Program Coding and Staging Manual and International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^82,83 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the subgroup divided by the total number of patients or data variables. To compare the mean number of dispensed antidepressants to those without antidepressants, a 2-tailed, 2-sample z test was used to calculate the P value and determine statistical significance (P < .05) using socscistatistics.com.

Data were extracted 3 times between 2021 and 2023. The initial 2021 protocol focused on erlotinib and gefitinib. A modified protocol in 2022 added paclitaxel, cisplatin, docetaxel, pemetrexed, and crizotinib; further modification in 2023 included 8 new antineoplastic agents and 2 anticoagulants. Sotorasib has not been prescribed in the MHS, and JPC lacks records for noncancer drugs. The 2023 dataset comprised 2210 patients with cancer treated with 14 antineoplastic agents; 2104 had documented diagnoses and 2113 had recorded prescriptions. Data for erlotinib, gefitinib, and paclitaxel have been published previously.^78,79

Results

Of 2113 patients with recorded prescriptions, 1297 patients (61.4%) received 109 cancer drugs, including 96 antineoplastics, 7 disease-modifying antirheumatic agents, 4 biologic response modifiers, and 2 calcitonin gene-related peptides. Fourteen antineoplastic agents had complete data from JPC, while others were noted for combination therapies or treatment switches from the PDTS (Table 1). Seventy-six cancer drugs were prescribed with antidepressants in 489 patients (eAppendix).

The JPC provided 2242 entries for 2210 patients, ranging in age from 2 months to 88 years (mean, 56 years), documenting treatment from September 1988 to January 2023. Thirty-two patients had duplicate entries due to multiple cancer locations or occurrences. Of the 2242 patients, 1541 (68.7%) were aged > 50 years, 975 patients (43.5%) had cancers that were stage III or IV, and 1267 (56.5%) had cancers that were stage 0, I, II, or not applicable/unknown. There were 51 different types of cancer: breast, lung, testicular, endometrial, and ovarian were most common (n ≥ 100 patients). Forty-two cancer types were documented among 750 patients prescribed antidepressants (Table 2).

The CAPER database recorded 8882 unique diagnoses for 2104 patients, while PDTS noted 1089 unique prescriptions within 273 therapeutic codes for 2113 patients. Nine therapeutic codes (opiate agonists, adrenals, cathartics-laxatives, nonsteroidal anti-inflammatory agents, antihistamines for GI conditions, 5-HT3 receptor antagonists, analgesics and antipyretic miscellanea, antineoplastic agents, and proton-pump inhibitors) and 8 drugs (dexamethasone, prochlorperazine, ondansetron, docusate, acetaminophen, ibuprofen, oxycodone, and polyethylene glycol 3350) were associated with > 1000 patients (≥ 50%). Patients had between 1 and 275 unique health conditions and filled 1 to 108 prescriptions. The mean (SD) number of diagnoses and prescriptions was 50 (28) and 29 (12), respectively. Of the 273 therapeutic codes, 30 groups were analyzed, with others categorized into miscellaneous groups such as lotions, vaccines, and devices. Significant differences in mean number of prescriptions were found for patients taking antidepressants compared to those not (P < .05), except for anticonvulsants and antipsychotics (P = .12 and .09, respectively) (Table 3).

Antidepressants

Of the 2113 patients with recorded prescriptions, 750 (35.5%) were dispensed 17 different antidepressants. Among these 17 antidepressants, 183 (8.7%) patients received duloxetine, 158 (7.5%) received venlafaxine, 118 (5.6%) received trazodone, and 107 (5.1%) received sertraline (Figure 1, Table 4). Of the 750 patients, 509 (67.9%) received 1 antidepressant, 168 (22.4%) received 2, 60 (8.0%) received 3, and 13 (1.7%) received > 3. Combinations varied, but only duloxetine and trazodone were prescribed to > 10 patients.

Antidepressants were prescribed annually at an overall mean (SD) rate of 23% (5%) from 2003 to 2022 (Figure 2). Patients on antidepressants during systemic therapy had a greater number of diagnosed medical conditions and received more prescription medications compared to those not taking antidepressants (P < .001) (Figure 3). The 745 patients taking antidepressants in CAPER data had between 1 and 275 diagnosed medical issues, with a mean (SD) of 55 (31) vs a range of 1 to 209 and a mean (SD) of 46 (26) for the 1359 patients not taking antidepressants. The 750 patients on antidepressants in PDTS data had between 8 and 108 prescriptions dispensed, with a mean (SD) of 32 (12), vs a range of 1 to 65 prescriptions and a mean (SD) of 29 (12) for 1363 patients not taking antidepressants.

Discussion

The JPC DoD Cancer Registry includes information on cancer types, stages, treatment regimens, and physicians’ notes, while noncancer drugs are sourced from the PDTS database. The pharmacy uses a different documentation system, leading to varied classifications.

Database reliance has its drawbacks. For example, megestrol is coded as a cancer drug, although it’s primarily used for endometrial or gynecologic cancers. Many drugs have multiple therapeutic codes assigned to them, including 10 antineoplastic agents: diclofenac, Bacillus Calmette-Guérin (BCG), megestrol acetate, tamoxifen, anastrozole, letrozole, leuprolide, goserelin, degarelix, and fluorouracil. Diclofenac, BCG, and mitomycin have been repurposed for cancer treatment.^84-87 From 2003 to 2023, diclofenac was prescribed to 350 patients for mild-to-moderate pain, with only 2 patients receiving it for cancer in 2018. FDA-approved for bladder cancer in 1990, BCG was prescribed for cancer treatment for 1 patient in 2021 after being used for vaccines between 2003 and 2018. Tamoxifen, used for hormone receptor-positive breast cancer from 2004 to 2017 with 53 patients, switched to estrogen agonist-antagonists from 2017 to 2023 with 123 patients. Only a few of the 168 patients were prescribed tamoxifen using both codes.^88-91 Anastrozole and letrozole were coded as antiestrogens for 7 and 18 patients, respectively, while leuprolide and goserelin were coded as gonadotropins for 59 and 18 patients. Degarelix was coded as antigonadotropins, fluorouracil as skin and mucous membrane agents miscellaneous, and megestrol acetate as progestins for 7, 6, and 3 patients, respectively. Duloxetine was given to 186 patients, primarily for depression from 2005 to 2023, with 7 patients treated for fibromyalgia from 2022 to 2023.

Antidepressants Observed

Tables 1 and 5 provide insight into the FDA approval of 14 antineoplastics and antidepressants and their CYP metabolic pathways.^92-122 In Table 4, the most prescribed antidepressant classes are SNRIs, SRMs, SSRIs, TeCAs, NDRIs, and TCAs. This trend highlights a preference for newer medications with weak CYP inhibition. A total of 349 patients were prescribed SSRIs, 343 SNRIs, 119 SRMs, 109 TCAs, 83 TeCAs, and 79 NDRIs. MAOIs, SMRAs, and NMDARAs were not observed in this dataset. While there are instances of dextromethorphan-bupropion and sertraline-escitalopram being dispensed together, it remains unclear whether these were NMDARA combinations.

Among the 14 specific antineoplastic agents, 10 are metabolized by CYP isoenzymes, primarily CYP3A4. Duloxetine neither inhibits nor is metabolized by CYP3A4, a reason it is often recommended, following venlafaxine.

Both duloxetine and venlafaxine are used off-label for chemotherapy-induced peripheral neuropathy related to paclitaxel and docetaxel. According to the CYP metabolized pathway, duloxetine tends to have more favorable DDIs than venlafaxine. In PDTS data, 371 patients were treated with paclitaxel and 180 with docetaxel, with respective antidepressant prescriptions of 156 and 70. Of the 156 patients dispensed paclitaxel, 62 (40%) were dispensed with duloxetine compared to 43 (28%) with venlafaxine. Of the 70 patients dispensed docetaxel, 23 (33%) received duloxetine vs 24 (34%) with venlafaxine.

Of 85 patients prescribed duloxetine, 75 received it with either paclitaxel or docetaxel (5 received both). Five patients had documented AEs (1 neuropathy related). Of 67 patients prescribed venlafaxine, 66 received it with either paclitaxel or docetaxel. Two patients had documented AEs (1 was neuropathy related, the same patient who received duloxetine). Of the 687 patients treated with paclitaxel and 337 with docetaxel in all databases, 4 experienced neuropathic AEs from both medications.⁷⁹

Antidepressants can increase the risk of bleeding, especially when combined with blood thinners, and may elevate blood pressure, particularly alongside stimulants. Of the 554 patients prescribed 9 different anticoagulants, enoxaparin, apixaban, and rivaroxaban were the most common (each > 100 patients). Among these, 201 patients (36%) received both anticoagulants and antidepressants: duloxetine for 64 patients, venlafaxine for 30, trazodone for 35, and sertraline for 26. There were no data available to assess bleeding rates related to the evaluation of DDIs between these medication classes.

Antidepressants can be prescribed for erectile dysfunction. Of the 148 patients prescribed an antidepressant for erectile dysfunction, duloxetine, trazodone, and mirtazapine were the most common. Antidepressant preferences varied by cancer type. Duloxetine was the only antidepressant used for all types of cancer. Venlafaxine, duloxetine, trazodone, sertraline, and escitalopram were the most prescribed antidepressants for breast cancer, while duloxetine, mirtazapine, citalopram, sertraline, and trazodone were the most prescribed for lung cancer. Sertraline, duloxetine, trazodone, amitriptyline, and escitalopram were most common for testicular cancer. Duloxetine, venlafaxine, trazodone, amitriptyline, and sertraline were the most prescribed for endometrial cancer, while duloxetine, venlafaxine, amitriptyline, citalopram, and sertraline were most prescribed for ovarian cancer.

The broadness of International Statistical Classification of Diseases, Tenth Revision codes made it challenging to identify nondepression diagnoses in the analyzed population. However, if all antidepressants were prescribed to treat depression, service members with cancer exhibited a higher depression rate (35%) than the general population (25%). Of 2104 patients, 191 (9.1%) had mood disorders, and 706 (33.6%) had mental disorders: 346 (49.0%) had 1 diagnosis, and 360 (51.0%) had multiple diagnoses. The percentage of diagnoses varied yearly, with notable drops in 2003, 2007, 2011, 2014, and 2018, and peaks in 2006, 2008, 2013, 2017, and 2022. This fluctuation was influenced by events like the establishment of PDTS in 2002, the 2008 economic recession, a hospital relocation in 2011, the 2014 Ebola outbreak, and the COVID-19 pandemic. Although the number of patients receiving antidepressants increased from 2019 to 2022, the overall percentage of patients receiving them did not significantly change from 2003 to 2022, aligning with previous research.^5,125

Many medications have potential uses beyond what is detailed in the prescribing information. Antidepressants can relieve pain, while pain medications may help with depression. Opioids were once thought to effectively treat depression, but this perspective has changed with a greater understanding of their risks, including misuse.^126-131 Pain is a severe and often unbearable AE of cancer. Of 2113 patients, 92% received opioids; 34% received both opioids and antidepressants; 2% received only antidepressants; and 7% received neither. This study didn’t clarify whether those on opioids alone recognized their depression or if those on both were aware of their dependence. While SSRIs are generally not addictive, they can lead to physical dependence, and any medication can be abused if not managed properly.^132-134

Conclusions

This retrospective study analyzes data from antineoplastic agents used in systemic cancer treatment between 1988 and 2023, with a particular focus on the use of antidepressants. Data on antidepressant prescriptions are incomplete and specific to these agents, which means the findings cannot be generalized to all antidepressants. Hence, the results indicate that patients taking antidepressants had more diagnosed health issues and received more medications compared to patients who were not on these drugs.

This study underscores the need for further research into the effects of antidepressants on cancer treatment, utilizing all data from the DoD Cancer Registry. Future research should explore DDIs between antidepressants and other cancer and noncancer medications, as this study did not assess AE documentation, unlike in studies involving erlotinib, gefitinib, and paclitaxel.^78,79 Further investigation is needed to evaluate the impact of discontinuing antidepressant use during cancer treatment. This comprehensive overview provides insights for clinicians to help them make informed decisions regarding the prescription of antidepressants in the context of cancer treatment.

Cancer patients experience depression at rates > 5 times that of the general population.^1-11 Despite an increase in palliative care use, depression rates continued to rise.^2-4 Between 5% to 16% of outpatients, 4% to 14% of inpatients, and up to 49% of patients receiving palliative care experience depression.⁵ This issue also impacts families and caregivers.¹ A 2021 meta-analysis found that 23% of active military personnel and 20% of veterans experience depression.¹¹

Antidepressants approved by the US Food and Drug Administration (FDA) target the serotonin, norepinephrine, or dopamine systems and include boxed warnings about an increased risk of suicidal thoughts in adults aged 18 to 24 years.^12,13 These medications are categorized into several classes: monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), norepinephrine-dopamine reuptake inhibitors (NDRIs), selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), serotonin receptor modulators (SRMs), serotonin-melatonin receptor antagonists (SMRAs), and N—methyl-D-aspartate receptor antagonists (NMDARAs).^14,15 The first FDA-approved antidepressants, iproniazid (an MAOI) and imipramine (a TCA) laid the foundation for the development of newer classes like SSRIs and SNRIs.^15-17

Older antidepressants such as MAOIs and TCAs are used less due to their adverse effects (AEs) and drug interactions. MAOIs, such as iproniazid, selegiline, moclobemide, tranylcypromine, isocarboxazid, and phenelzine, have numerous AEs and drug interactions, making them unsuitable for first- or second-line treatment of depression.^14,18-21 TCAs such as doxepin, amitriptyline, nortriptyline, imipramine, desipramine, clomipramine, trimipramine, protriptyline, maprotiline, and amoxapine have a narrow therapeutic index requiring careful monitoring for signs of toxicity such as QRS widening, tremors, or confusion. Despite the issues, TCAs are generally classified as second-line agents for major depressive disorder (MDD). TCAs have off-label uses for migraine prophylaxis, treatment of obsessive-compulsive disorder (OCD), insomnia, and chronic pain management first-line.^14,22-29

Newer antidepressants, including TeCAs and NDRIs, are typically more effective, but also come with safety concerns. TeCAs like mirtazapine interact with several medications, including MAOIs, serotonin-increasing drugs, alcohol, cannabidiol, and marijuana. Mirtazapine is FDA-approved for the treatment of moderate to severe depression in adults. It is also used off-label to treat insomnia, panic disorder, posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), headaches, and migraines. Compared to other antidepressants, mirtazapine is effective for all stages of depression and addresses a broad range of related symptoms.^14,30-34 NDRIs, such as bupropion, also interact with various medications, including MAOIs, other antidepressants, stimulants, and alcohol. Bupropion is FDA-approved for smoking cessation and to treat depression and SAD. It is also used off-label for depression- related bipolar disorder or sexual dysfunction, attention-deficit/hyperactivity disorder (ADHD), and obesity.^14,35-42

SSRIs, SNRIs, and SRMs should be used with caution. SSRIs such as sertraline, citalopram, escitalopram, fluoxetine, paroxetine, and fluvoxamine are first-line treatments for depression and various psychiatric disorders due to their safety and efficacy. Common AEs of SSRIs include sexual dysfunction, sleep disturbances, weight changes, and gastrointestinal (GI) issues. SSRIs can prolong the QT interval, posing a risk of life-threatening arrhythmia, and may interact with other medications, necessitating treatment adjustments. The FDA approved SSRIs for MDD, GAD, bulimia nervosa, bipolar depression, OCD, panic disorder, premenstrual dysphoric disorder, treatment-resistant depression, PTSD, and SAD. Off-label uses include binge eating disorder, body dysmorphic disorder, fibromyalgia, premature ejaculation, paraphilias, autism, Raynaud phenomenon, and vasomotor symptoms associated with menopause. Among SSRIs, sertraline and escitalopram are noted for their effectiveness and tolerability.^14,43-53

SNRIs, including duloxetine, venlafaxine, desvenlafaxine, milnacipran, and levomilnacipran, may increase bleeding risk, especially when taken with blood thinners. They can also elevate blood pressure, which may worsen if combined with stimulants. SNRIs may interact with other medications that affect serotonin levels, increasing the risk of serotonin syndrome when taken with triptans, pain medications, or other antidepressants.¹⁴ Desvenlafaxine has been approved by the FDA (but not by the European Medicines Agency).^54-56 Duloxetine is FDA-approved for the treatment of depression, neuropathic pain, anxiety disorders, fibromyalgia, and musculoskeletal disorders. It is used off-label to treat chemotherapy-induced peripheral neuropathy and stress urinary incontinence.^57-61 Venlafaxine is FDA-approved for depression, SAD, and panic disorder, and is prescribed off-label to treat ADHD, neuropathy, fibromyalgia, cataplexy, and PTSD, either alone or in combination with other medications.^62,63 Milnacipran is not approved for MDD; levomilnacipran received approval in 2013.⁶⁴

SRMs such as trazodone, nefazodone, vilazodone, and vortioxetine also function as serotonin reuptake inhibitors.^14,15 Trazodone is FDA-approved for MDD. It has been used off-label to treat anxiety, Alzheimer disease, substance misuse, bulimia nervosa, insomnia, fibromyalgia, and PTSD when first-line SSRIs are ineffective. A notable AE of trazodone is orthostatic hypotension, which can lead to dizziness and increase the risk of falls, especially in geriatric patients.^65-70 Nefazodone was discontinued in Europe in 2003 due to rare cases of liver toxicity but remains available in the US.^71-74 Vilazodone and vortioxetine are FDA-approved.

The latest classes of antidepressants include SMRAs and NMDARAs.¹⁴ Agomelatine, an SMRA, was approved in Europe in 2009 but rejected by the FDA in 2011 due to liver toxicity.⁷⁵ NMDARAs like esketamine and a combination of dextromethorphan and bupropion received FDA approval in 2019 and 2022, respectively.^76,77

This retrospective study analyzes noncancer drugs used during systemic chemotherapy based on a dataset of 14 antineoplastic agents. It sought to identify the most dispensed noncancer drug groups, discuss findings, compare patients with and without antidepressant prescriptions, and examine trends in antidepressant use from 2002 to 2023. This analysis expands on prior research.^78-81

Methods

The Walter Reed National Military Medical Center Institutional Review Board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and Military Health System (MHS) data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

Data Sources

The JPC DoD Cancer Registry Program contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in CAPER represents an ambulatory encounter at a military treatment facility (MTF). CAPER records are available from 2003 to 2024. PDTS records are available from 2002 to 2004. Each observation in PDTS represents a prescription filled for an MHS beneficiary, excluding those filled at international civilian pharmacies and inpatient pharmacy prescriptions.

This cross-sectional analysis requested data extraction for specific cancer drugs from the DoD Cancer Registry, focusing on treatment details, diagnosis dates, patient demographics, and physicians’ comments on AEs. After identifying patients, CAPER was used to identify additional health conditions. PDTS was used to compile a list of prescription medications filled during systemic cancer treatment or < 2 years postdiagnosis.

The 2016 Surveillance, Epidemiology, and End Results Program Coding and Staging Manual and International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^82,83 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the subgroup divided by the total number of patients or data variables. To compare the mean number of dispensed antidepressants to those without antidepressants, a 2-tailed, 2-sample z test was used to calculate the P value and determine statistical significance (P < .05) using socscistatistics.com.

Data were extracted 3 times between 2021 and 2023. The initial 2021 protocol focused on erlotinib and gefitinib. A modified protocol in 2022 added paclitaxel, cisplatin, docetaxel, pemetrexed, and crizotinib; further modification in 2023 included 8 new antineoplastic agents and 2 anticoagulants. Sotorasib has not been prescribed in the MHS, and JPC lacks records for noncancer drugs. The 2023 dataset comprised 2210 patients with cancer treated with 14 antineoplastic agents; 2104 had documented diagnoses and 2113 had recorded prescriptions. Data for erlotinib, gefitinib, and paclitaxel have been published previously.^78,79

Results

Of 2113 patients with recorded prescriptions, 1297 patients (61.4%) received 109 cancer drugs, including 96 antineoplastics, 7 disease-modifying antirheumatic agents, 4 biologic response modifiers, and 2 calcitonin gene-related peptides. Fourteen antineoplastic agents had complete data from JPC, while others were noted for combination therapies or treatment switches from the PDTS (Table 1). Seventy-six cancer drugs were prescribed with antidepressants in 489 patients (eAppendix).

The JPC provided 2242 entries for 2210 patients, ranging in age from 2 months to 88 years (mean, 56 years), documenting treatment from September 1988 to January 2023. Thirty-two patients had duplicate entries due to multiple cancer locations or occurrences. Of the 2242 patients, 1541 (68.7%) were aged > 50 years, 975 patients (43.5%) had cancers that were stage III or IV, and 1267 (56.5%) had cancers that were stage 0, I, II, or not applicable/unknown. There were 51 different types of cancer: breast, lung, testicular, endometrial, and ovarian were most common (n ≥ 100 patients). Forty-two cancer types were documented among 750 patients prescribed antidepressants (Table 2).

The CAPER database recorded 8882 unique diagnoses for 2104 patients, while PDTS noted 1089 unique prescriptions within 273 therapeutic codes for 2113 patients. Nine therapeutic codes (opiate agonists, adrenals, cathartics-laxatives, nonsteroidal anti-inflammatory agents, antihistamines for GI conditions, 5-HT3 receptor antagonists, analgesics and antipyretic miscellanea, antineoplastic agents, and proton-pump inhibitors) and 8 drugs (dexamethasone, prochlorperazine, ondansetron, docusate, acetaminophen, ibuprofen, oxycodone, and polyethylene glycol 3350) were associated with > 1000 patients (≥ 50%). Patients had between 1 and 275 unique health conditions and filled 1 to 108 prescriptions. The mean (SD) number of diagnoses and prescriptions was 50 (28) and 29 (12), respectively. Of the 273 therapeutic codes, 30 groups were analyzed, with others categorized into miscellaneous groups such as lotions, vaccines, and devices. Significant differences in mean number of prescriptions were found for patients taking antidepressants compared to those not (P < .05), except for anticonvulsants and antipsychotics (P = .12 and .09, respectively) (Table 3).

Antidepressants

Of the 2113 patients with recorded prescriptions, 750 (35.5%) were dispensed 17 different antidepressants. Among these 17 antidepressants, 183 (8.7%) patients received duloxetine, 158 (7.5%) received venlafaxine, 118 (5.6%) received trazodone, and 107 (5.1%) received sertraline (Figure 1, Table 4). Of the 750 patients, 509 (67.9%) received 1 antidepressant, 168 (22.4%) received 2, 60 (8.0%) received 3, and 13 (1.7%) received > 3. Combinations varied, but only duloxetine and trazodone were prescribed to > 10 patients.

Antidepressants were prescribed annually at an overall mean (SD) rate of 23% (5%) from 2003 to 2022 (Figure 2). Patients on antidepressants during systemic therapy had a greater number of diagnosed medical conditions and received more prescription medications compared to those not taking antidepressants (P < .001) (Figure 3). The 745 patients taking antidepressants in CAPER data had between 1 and 275 diagnosed medical issues, with a mean (SD) of 55 (31) vs a range of 1 to 209 and a mean (SD) of 46 (26) for the 1359 patients not taking antidepressants. The 750 patients on antidepressants in PDTS data had between 8 and 108 prescriptions dispensed, with a mean (SD) of 32 (12), vs a range of 1 to 65 prescriptions and a mean (SD) of 29 (12) for 1363 patients not taking antidepressants.

Discussion

The JPC DoD Cancer Registry includes information on cancer types, stages, treatment regimens, and physicians’ notes, while noncancer drugs are sourced from the PDTS database. The pharmacy uses a different documentation system, leading to varied classifications.

Database reliance has its drawbacks. For example, megestrol is coded as a cancer drug, although it’s primarily used for endometrial or gynecologic cancers. Many drugs have multiple therapeutic codes assigned to them, including 10 antineoplastic agents: diclofenac, Bacillus Calmette-Guérin (BCG), megestrol acetate, tamoxifen, anastrozole, letrozole, leuprolide, goserelin, degarelix, and fluorouracil. Diclofenac, BCG, and mitomycin have been repurposed for cancer treatment.^84-87 From 2003 to 2023, diclofenac was prescribed to 350 patients for mild-to-moderate pain, with only 2 patients receiving it for cancer in 2018. FDA-approved for bladder cancer in 1990, BCG was prescribed for cancer treatment for 1 patient in 2021 after being used for vaccines between 2003 and 2018. Tamoxifen, used for hormone receptor-positive breast cancer from 2004 to 2017 with 53 patients, switched to estrogen agonist-antagonists from 2017 to 2023 with 123 patients. Only a few of the 168 patients were prescribed tamoxifen using both codes.^88-91 Anastrozole and letrozole were coded as antiestrogens for 7 and 18 patients, respectively, while leuprolide and goserelin were coded as gonadotropins for 59 and 18 patients. Degarelix was coded as antigonadotropins, fluorouracil as skin and mucous membrane agents miscellaneous, and megestrol acetate as progestins for 7, 6, and 3 patients, respectively. Duloxetine was given to 186 patients, primarily for depression from 2005 to 2023, with 7 patients treated for fibromyalgia from 2022 to 2023.

Antidepressants Observed

Tables 1 and 5 provide insight into the FDA approval of 14 antineoplastics and antidepressants and their CYP metabolic pathways.^92-122 In Table 4, the most prescribed antidepressant classes are SNRIs, SRMs, SSRIs, TeCAs, NDRIs, and TCAs. This trend highlights a preference for newer medications with weak CYP inhibition. A total of 349 patients were prescribed SSRIs, 343 SNRIs, 119 SRMs, 109 TCAs, 83 TeCAs, and 79 NDRIs. MAOIs, SMRAs, and NMDARAs were not observed in this dataset. While there are instances of dextromethorphan-bupropion and sertraline-escitalopram being dispensed together, it remains unclear whether these were NMDARA combinations.

Among the 14 specific antineoplastic agents, 10 are metabolized by CYP isoenzymes, primarily CYP3A4. Duloxetine neither inhibits nor is metabolized by CYP3A4, a reason it is often recommended, following venlafaxine.

Both duloxetine and venlafaxine are used off-label for chemotherapy-induced peripheral neuropathy related to paclitaxel and docetaxel. According to the CYP metabolized pathway, duloxetine tends to have more favorable DDIs than venlafaxine. In PDTS data, 371 patients were treated with paclitaxel and 180 with docetaxel, with respective antidepressant prescriptions of 156 and 70. Of the 156 patients dispensed paclitaxel, 62 (40%) were dispensed with duloxetine compared to 43 (28%) with venlafaxine. Of the 70 patients dispensed docetaxel, 23 (33%) received duloxetine vs 24 (34%) with venlafaxine.

Of 85 patients prescribed duloxetine, 75 received it with either paclitaxel or docetaxel (5 received both). Five patients had documented AEs (1 neuropathy related). Of 67 patients prescribed venlafaxine, 66 received it with either paclitaxel or docetaxel. Two patients had documented AEs (1 was neuropathy related, the same patient who received duloxetine). Of the 687 patients treated with paclitaxel and 337 with docetaxel in all databases, 4 experienced neuropathic AEs from both medications.⁷⁹

Antidepressants can increase the risk of bleeding, especially when combined with blood thinners, and may elevate blood pressure, particularly alongside stimulants. Of the 554 patients prescribed 9 different anticoagulants, enoxaparin, apixaban, and rivaroxaban were the most common (each > 100 patients). Among these, 201 patients (36%) received both anticoagulants and antidepressants: duloxetine for 64 patients, venlafaxine for 30, trazodone for 35, and sertraline for 26. There were no data available to assess bleeding rates related to the evaluation of DDIs between these medication classes.

Antidepressants can be prescribed for erectile dysfunction. Of the 148 patients prescribed an antidepressant for erectile dysfunction, duloxetine, trazodone, and mirtazapine were the most common. Antidepressant preferences varied by cancer type. Duloxetine was the only antidepressant used for all types of cancer. Venlafaxine, duloxetine, trazodone, sertraline, and escitalopram were the most prescribed antidepressants for breast cancer, while duloxetine, mirtazapine, citalopram, sertraline, and trazodone were the most prescribed for lung cancer. Sertraline, duloxetine, trazodone, amitriptyline, and escitalopram were most common for testicular cancer. Duloxetine, venlafaxine, trazodone, amitriptyline, and sertraline were the most prescribed for endometrial cancer, while duloxetine, venlafaxine, amitriptyline, citalopram, and sertraline were most prescribed for ovarian cancer.

The broadness of International Statistical Classification of Diseases, Tenth Revision codes made it challenging to identify nondepression diagnoses in the analyzed population. However, if all antidepressants were prescribed to treat depression, service members with cancer exhibited a higher depression rate (35%) than the general population (25%). Of 2104 patients, 191 (9.1%) had mood disorders, and 706 (33.6%) had mental disorders: 346 (49.0%) had 1 diagnosis, and 360 (51.0%) had multiple diagnoses. The percentage of diagnoses varied yearly, with notable drops in 2003, 2007, 2011, 2014, and 2018, and peaks in 2006, 2008, 2013, 2017, and 2022. This fluctuation was influenced by events like the establishment of PDTS in 2002, the 2008 economic recession, a hospital relocation in 2011, the 2014 Ebola outbreak, and the COVID-19 pandemic. Although the number of patients receiving antidepressants increased from 2019 to 2022, the overall percentage of patients receiving them did not significantly change from 2003 to 2022, aligning with previous research.^5,125

Many medications have potential uses beyond what is detailed in the prescribing information. Antidepressants can relieve pain, while pain medications may help with depression. Opioids were once thought to effectively treat depression, but this perspective has changed with a greater understanding of their risks, including misuse.^126-131 Pain is a severe and often unbearable AE of cancer. Of 2113 patients, 92% received opioids; 34% received both opioids and antidepressants; 2% received only antidepressants; and 7% received neither. This study didn’t clarify whether those on opioids alone recognized their depression or if those on both were aware of their dependence. While SSRIs are generally not addictive, they can lead to physical dependence, and any medication can be abused if not managed properly.^132-134

Conclusions

This retrospective study analyzes data from antineoplastic agents used in systemic cancer treatment between 1988 and 2023, with a particular focus on the use of antidepressants. Data on antidepressant prescriptions are incomplete and specific to these agents, which means the findings cannot be generalized to all antidepressants. Hence, the results indicate that patients taking antidepressants had more diagnosed health issues and received more medications compared to patients who were not on these drugs.

This study underscores the need for further research into the effects of antidepressants on cancer treatment, utilizing all data from the DoD Cancer Registry. Future research should explore DDIs between antidepressants and other cancer and noncancer medications, as this study did not assess AE documentation, unlike in studies involving erlotinib, gefitinib, and paclitaxel.^78,79 Further investigation is needed to evaluate the impact of discontinuing antidepressant use during cancer treatment. This comprehensive overview provides insights for clinicians to help them make informed decisions regarding the prescription of antidepressants in the context of cancer treatment.

Since 1999, annual deaths attributed to opioid overdose in the United States have increased from about 10,000 to about 50,000 in 2019.¹ During the COVID-19 pandemic > 74,000 opioid overdose deaths occurred in the US from April 2020 to April 2021.^2,3 Opioid-related overdoses now account for about 75% of all drug-related overdose deaths.¹ In 2017, the cost of opioid overdose deaths and opioid use disorder (OUD) reached $1.02 trillion in the United States and $26 million in Indiana.⁴ The total deaths and costs would likely be higher if it were not for naloxone.

Naloxone hydrochloride was first patented in the 1960s and approved by the US Food and Drug Administration (FDA) in 1971 to treat opioid-related toxicity.¹ It is the most frequently prescribed antidote for opioid toxicity due to its activity as a pure υ-opioid receptor competitive antagonist. Naloxone formulations include intramuscular, intravenous, subcutaneous, and intranasal delivery methods.⁵ According to the Centers for Disease Control and Prevention, clinicians should offer naloxone to patients at high risk for opioid-related adverse events. Risk factors include a history of overdose, opioid dosages of ≥ 50 morphine mg equivalents/day, and concurrent use of opioids with benzodiazepines.⁶

Intranasal naloxone 4 mg has become more accessible following the classification of opioid use as a public health emergency in 2017 and its over-the-counter availability since 2023. Intranasal naloxone 4 mg was approved by the FDA in 2015 for the prevention of opioid overdoses (accidental or intentional), which can be caused by heroin, fentanyl, carfentanil, hydrocodone, oxycodone, methadone, and other substances. ⁷ Fentanyl has most recently been associated with xylazine, a nonopioid tranquilizer linked to increased opioid overdose deaths.⁸ Recent data suggest that 34% of opioid overdose reversals involved ≥ 2 doses of intranasal naloxone 4 mg, which led to FDA approval of an intranasal naloxone 8 mg spray in April 2021.^9-11

Veteran Health Indiana (VHI) has implemented several initiatives to promote naloxone prescribing. Established in 2020, the Opioid Overdose Education and Naloxone Distribution (OEND) program sought to prevent opioid-related deaths through education and product distribution. These criteria included an opioid prescription for ≥ 30 days. In 2021, the Stratification Tool for Opioid Risk Mitigation (STORM) was created to identify patients at high risk of opioid overdose and allowing pharmacists to prescribe naloxone for at-risk patients without restrictions, increasing accessibility.¹²

Recent cases of fentanyl-related overdoses involving stronger fentanyl analogues highlight the need for higher naloxone dosing to prevent overdose. A pharmacokinetic comparison of intranasal naloxone 8 mg vs 4 mg demonstrated maximum plasma concentrations of 10.3 ng/mL and 5.3 ng/mL, respectively. ¹³ Patients may be at an increased risk of precipitated opioid withdrawal when using intranasal naloxone 8 mg over 4 mg; however, some patients may benefit from achieving higher serum concentrations and therefore require larger doses of naloxone.

No clinical trials have demonstrated a difference in reversal rates between naloxone doses. No clinical practice guidelines support a specific naloxone formulation, and limited US Department of Veterans Affairs (VA)-specific guidance exists. VA Naloxone Rescue: Recommendations for Use states that selection of naloxone 8 mg should be based on shared decision-making between the patient and clinician and based on individual risk factors.¹² The purpose of this study is to analyze data to determine if there is a difference in prescribing patterns of intranasal naloxone 4 mg and intranasal naloxone 8 mg.

METHODS

A retrospective chart reviews using the VA Computerized Patient Record System (CPRS) analyzed patients prescribed intranasal naloxone 4 mg or intranasal naloxone 8 mg at VHI. A patient list was generated based on active naloxone prescriptions between April 1, 2022, and April 1, 2023. Data were obtained exclusively through CPRS and patients were not contacted. This study was reviewed and deemed exempt by the Indiana University Health Institutional Review Board and the VHI Research and Development Committee.

Patients were included if they were aged ≥ 18 years and had an active prescription for intranasal naloxone 4 mg or intranasal naloxone 8 mg during the trial period. Patients were excluded if their naloxone prescription was written by a non-VHI clinician, if the dose was not 4 mg or 8 mg, or if the dosage form was other than intranasal spray.

The primary endpoint was the comparison for prescribing patterns for intranasal naloxone 4 mg and intranasal naloxone 8 mg during the study period. Secondary endpoints included total naloxone prescriptions; monthly prescriptions; number of patients with repeated naloxone prescriptions; prescriber type by naloxone dose; clinic type by naloxone dose; and documented indication for naloxone use by dose.

Demographic data collected included baseline age, sex, race, comorbid mental health conditions, and active central nervous system depressant medications on patient profile (ie, opioids, gabapentinoids, benzodiazepines, antidepressants, antipsychotics). Opioid prescriptions that were active or discontinued within the last 3 months were also recorded. Comorbid mental health conditions were collected based on the most recent clinical note before initiating medication.

Prescription-related data included strength of medication prescribed (4 mg, 8 mg, or both), documented use of medication, prescriber name, prescriber discipline, prescription entered by, number of times naloxone was filled or refilled during the study period, indication, clinic location, and clinic name. If > 1 prescription was active during the study period, the number of refills, prescriber name and clinic location of the first prescription in the study period was recorded. Additionally, the indication of OUD was differentiated from substance use disorder (SUD) if the patient was only dependent on opioids, excluding tobacco or alcohol. Patients with SUDs may include opioid dependence in addition to other substance dependence (eg, cannabis, stimulants, gabapentinoids, or benzodiazepines).

Basic descriptive statistics, including mean, ranges, and percentages were used to characterize the study subjects. For nominal data, X² tests were used. A 2-sided 5% significance level was used for all statistical tests.

RESULTS

A total of 1952 active naloxone prescriptions from 1739 patients met the inclusion criteria; none were eliminated based on the exclusion criteria and some were included multiple times because data were collected for each active prescription during the study period. One hundred one patients were randomized and included in the final analysis (Figure). Most patients identified as White (81%), male (90%), and had a mean (SD) age of 60.9 (14.2) years. Common mental health comorbidities included 59 patients with depression, 50 with tobacco use disorder, and 31 with anxiety. Eighty-four patients had opioid and 60 had antidepressants/antianxiety, and 40 had gabapentinoids prescriptions. Forty-three patients had ≥ 3 mental health comorbidities. Thirty-four patients had 2 active central nervous system depressant prescriptions, 30 had 3 active prescriptions, and 9 had ≥ 4 active prescriptions. Most patients (n = 83) had an active or recently discontinued opioid prescription (Table 1).

The 101 patients received 54 prescriptions for naloxone 8 mg and 47 for 4 mg (Table 2). Five patients received prescriptions for both the 4 mg and 8 mg intranasal naloxone formulations. Sixty-six patients had naloxone filled once (66%) during the study period. Intranasal naloxone 4 mg was prescribed to 30 patients by nurse practitioners, 17 patients by physicians, and not prescribed by pharmacists. Intranasal naloxone 8 mg was prescribed to 40 patients by pharmacists, 13 patients by physicians, and 6 patients by nurses. Patients who received prescriptions for both intranasal naloxone 4 mg and 8 mg were most routinely ordered by physicians (n = 3; 60%) in primary care (n = 2; 40%) for chronic opioid use (n = 2; 40%).

Patients access naloxone from many different VHI clinics. Primary care clinics prescribed the 4 mg formulation to 31 patients, 8 mg to 3 patients, and both to 2 patients. The STORM initiative was used for 37 of 106 prescriptions (35%): 4 mg intranasal naloxone was prescribed to 1 patient, 8 mg to 36 patients, and no patients received both formulations. Chronic opioid use was the most common indication (46%) with 30 patients prescribed intranasal naloxone 4 mg, 14 patients prescribed 8 mg, and 2 patients prescribed both. OUD was the indication for 24% of patients: 2 patients prescribed intranasal naloxone 4 mg, 21 patients prescribed 8 mg, and 1 patient prescribed both.

The 106 intranasal naloxone prescriptions were equally distributed across each month from April 1, 2022, to April 1, 2023. Of the 101 patients, 34 had multiple naloxone prescriptions filled during the study period. Pharmacists wrote 40 of 106 naloxone prescriptions (38%), all for the 8 mg formulation. Nurse practitioners prescribed naloxone 4 mg 30 times and 8 mg 6 times for 36 of 106 prescriptions (34%). Physicians prescribed 30 of 106 prescriptions (28%), including intranasal naloxone 4 mg 17 times and 8 mg 13 times.

Statistics were analyzed using a X² test; however, it was determined that the expected frequencies made the tests inappropriate. Differences in prescribing patterns between naloxone doses, prescriber disciplines, source of the prescription, or indications were not statistically significant.

DISCUSSION

Many pharmacists possess a scope of practice under state law and/or institution policy to prescribe naloxone. In this study, pharmacists prescribed the most naloxone prescriptions compared to physicians and nurse practitioners. Initiatives such as OEND and STORM have given pharmacists at VHI an avenue to combat the growing opioid epidemic while expanding their scope of practice. A systematic review of 67 studies found that pharmacist-led OEND programs showed a statistically significant increase in naloxone orders. A statistical significance was likely met given the large sample sizes ranging from 10 to 217,000 individuals, whereas this study only assessed a small portion of patients.¹⁴ This study contributes to the overwhelming amount of data that highlights pharmacists’ impact on overall naloxone distribution.

The STORM initiative and primary care clinics were responsible for large portions of naloxone prescriptions in this study. STORM was used by pharmacists and contributed to more than half of the higher dose naloxone prescriptions. Following a discussion with members of the pain management team, pharmacists involved in STORM prescribing were revealed to exclusively prescribe intranasal naloxone 8 mg as opposed to 4 mg. At the risk of precipitating withdrawal from higher doses of naloxone, it was agreed that this risk was heavily outweighed by the benefit of successful opioid reversal. In this context, it is expected for this avenue of prescribing to influence naloxone prescribing patterns at VHI.

Prescribing in primary care clinics was shown to be equally as substantial. Primary care-based multidisciplinary transition clinics have been reported to be associated with increased access to OUD treatment.¹⁵ Primary care clinics at VHI, or patient aligned care teams (PACT), largely consist of multidisciplinary health care teams. PACT clinicians are heavily involved in transitions of care because one system provides patients with comprehensive acute and chronic care. Continuing to encourage naloxone distribution through primary care and using STORM affords various patient populations access to high-level care.

Notable differences were observed between indications for naloxone use and the corresponding dose. Patients with OUD or SUD were more likely to receive intranasal naloxone 8 mg as opposed to patients receiving intranasal naloxone for chronic opioid use, who were more likely to receive the 4 mg dose. This may be due to a rationale to provide a higher dose of naloxone to combat overdoses in the case of ingesting substances mixed with fentanyl or xylazine.^12,13 Without standard of care guidelines, concerns remain for varying outcomes in opioid overdose prevention within vulnerable populations.

Limitations

Chart data were dependent on documentation, which may have omitted pertinent baseline characteristics and risk factors. Additional data collection could have further assessed a patient’s specific risk factors (eg, opioid dose in morphine equivalents) to draw conclusions to the dose of naloxone prescribed. The sample size was small, and the patient population was largely White and male, which minimized the generalizability of the results.

CONCLUSIONS

This study evaluated the differences in intranasal naloxone prescribing patterns within a veteran population at VHI over 12 months. Findings revealed that most prescriptions were written for intranasal naloxone 8 mg, by a pharmacist, in a primary care setting, and for chronic opioid use. The results revealed evidence of differing naloxone prescribing practices, which emphasize the need for clinical guidelines and better defined recommendations in relation to naloxone dosing.

The most evident gap in patient care could be addressed by urging the VA Pharmacy Benefits Management group to update naloxone recommendations for use to include more concrete dosing recommendations. Furthermore, it would be beneficial to re-educate clinicians on naloxone prescribing to increase awareness of different doses and the importance of equipping patients with the correct amount of naloxone in an emergency. Additional research assessing change in prescribing patterns is warranted as the use of higher dose naloxone becomes more routine.

Since 1999, annual deaths attributed to opioid overdose in the United States have increased from about 10,000 to about 50,000 in 2019.¹ During the COVID-19 pandemic > 74,000 opioid overdose deaths occurred in the US from April 2020 to April 2021.^2,3 Opioid-related overdoses now account for about 75% of all drug-related overdose deaths.¹ In 2017, the cost of opioid overdose deaths and opioid use disorder (OUD) reached $1.02 trillion in the United States and $26 million in Indiana.⁴ The total deaths and costs would likely be higher if it were not for naloxone.

Naloxone hydrochloride was first patented in the 1960s and approved by the US Food and Drug Administration (FDA) in 1971 to treat opioid-related toxicity.¹ It is the most frequently prescribed antidote for opioid toxicity due to its activity as a pure υ-opioid receptor competitive antagonist. Naloxone formulations include intramuscular, intravenous, subcutaneous, and intranasal delivery methods.⁵ According to the Centers for Disease Control and Prevention, clinicians should offer naloxone to patients at high risk for opioid-related adverse events. Risk factors include a history of overdose, opioid dosages of ≥ 50 morphine mg equivalents/day, and concurrent use of opioids with benzodiazepines.⁶

Intranasal naloxone 4 mg has become more accessible following the classification of opioid use as a public health emergency in 2017 and its over-the-counter availability since 2023. Intranasal naloxone 4 mg was approved by the FDA in 2015 for the prevention of opioid overdoses (accidental or intentional), which can be caused by heroin, fentanyl, carfentanil, hydrocodone, oxycodone, methadone, and other substances. ⁷ Fentanyl has most recently been associated with xylazine, a nonopioid tranquilizer linked to increased opioid overdose deaths.⁸ Recent data suggest that 34% of opioid overdose reversals involved ≥ 2 doses of intranasal naloxone 4 mg, which led to FDA approval of an intranasal naloxone 8 mg spray in April 2021.^9-11

Veteran Health Indiana (VHI) has implemented several initiatives to promote naloxone prescribing. Established in 2020, the Opioid Overdose Education and Naloxone Distribution (OEND) program sought to prevent opioid-related deaths through education and product distribution. These criteria included an opioid prescription for ≥ 30 days. In 2021, the Stratification Tool for Opioid Risk Mitigation (STORM) was created to identify patients at high risk of opioid overdose and allowing pharmacists to prescribe naloxone for at-risk patients without restrictions, increasing accessibility.¹²

Recent cases of fentanyl-related overdoses involving stronger fentanyl analogues highlight the need for higher naloxone dosing to prevent overdose. A pharmacokinetic comparison of intranasal naloxone 8 mg vs 4 mg demonstrated maximum plasma concentrations of 10.3 ng/mL and 5.3 ng/mL, respectively. ¹³ Patients may be at an increased risk of precipitated opioid withdrawal when using intranasal naloxone 8 mg over 4 mg; however, some patients may benefit from achieving higher serum concentrations and therefore require larger doses of naloxone.

No clinical trials have demonstrated a difference in reversal rates between naloxone doses. No clinical practice guidelines support a specific naloxone formulation, and limited US Department of Veterans Affairs (VA)-specific guidance exists. VA Naloxone Rescue: Recommendations for Use states that selection of naloxone 8 mg should be based on shared decision-making between the patient and clinician and based on individual risk factors.¹² The purpose of this study is to analyze data to determine if there is a difference in prescribing patterns of intranasal naloxone 4 mg and intranasal naloxone 8 mg.

METHODS

A retrospective chart reviews using the VA Computerized Patient Record System (CPRS) analyzed patients prescribed intranasal naloxone 4 mg or intranasal naloxone 8 mg at VHI. A patient list was generated based on active naloxone prescriptions between April 1, 2022, and April 1, 2023. Data were obtained exclusively through CPRS and patients were not contacted. This study was reviewed and deemed exempt by the Indiana University Health Institutional Review Board and the VHI Research and Development Committee.

Patients were included if they were aged ≥ 18 years and had an active prescription for intranasal naloxone 4 mg or intranasal naloxone 8 mg during the trial period. Patients were excluded if their naloxone prescription was written by a non-VHI clinician, if the dose was not 4 mg or 8 mg, or if the dosage form was other than intranasal spray.

The primary endpoint was the comparison for prescribing patterns for intranasal naloxone 4 mg and intranasal naloxone 8 mg during the study period. Secondary endpoints included total naloxone prescriptions; monthly prescriptions; number of patients with repeated naloxone prescriptions; prescriber type by naloxone dose; clinic type by naloxone dose; and documented indication for naloxone use by dose.

Demographic data collected included baseline age, sex, race, comorbid mental health conditions, and active central nervous system depressant medications on patient profile (ie, opioids, gabapentinoids, benzodiazepines, antidepressants, antipsychotics). Opioid prescriptions that were active or discontinued within the last 3 months were also recorded. Comorbid mental health conditions were collected based on the most recent clinical note before initiating medication.

Prescription-related data included strength of medication prescribed (4 mg, 8 mg, or both), documented use of medication, prescriber name, prescriber discipline, prescription entered by, number of times naloxone was filled or refilled during the study period, indication, clinic location, and clinic name. If > 1 prescription was active during the study period, the number of refills, prescriber name and clinic location of the first prescription in the study period was recorded. Additionally, the indication of OUD was differentiated from substance use disorder (SUD) if the patient was only dependent on opioids, excluding tobacco or alcohol. Patients with SUDs may include opioid dependence in addition to other substance dependence (eg, cannabis, stimulants, gabapentinoids, or benzodiazepines).

Basic descriptive statistics, including mean, ranges, and percentages were used to characterize the study subjects. For nominal data, X² tests were used. A 2-sided 5% significance level was used for all statistical tests.

RESULTS

A total of 1952 active naloxone prescriptions from 1739 patients met the inclusion criteria; none were eliminated based on the exclusion criteria and some were included multiple times because data were collected for each active prescription during the study period. One hundred one patients were randomized and included in the final analysis (Figure). Most patients identified as White (81%), male (90%), and had a mean (SD) age of 60.9 (14.2) years. Common mental health comorbidities included 59 patients with depression, 50 with tobacco use disorder, and 31 with anxiety. Eighty-four patients had opioid and 60 had antidepressants/antianxiety, and 40 had gabapentinoids prescriptions. Forty-three patients had ≥ 3 mental health comorbidities. Thirty-four patients had 2 active central nervous system depressant prescriptions, 30 had 3 active prescriptions, and 9 had ≥ 4 active prescriptions. Most patients (n = 83) had an active or recently discontinued opioid prescription (Table 1).

The 101 patients received 54 prescriptions for naloxone 8 mg and 47 for 4 mg (Table 2). Five patients received prescriptions for both the 4 mg and 8 mg intranasal naloxone formulations. Sixty-six patients had naloxone filled once (66%) during the study period. Intranasal naloxone 4 mg was prescribed to 30 patients by nurse practitioners, 17 patients by physicians, and not prescribed by pharmacists. Intranasal naloxone 8 mg was prescribed to 40 patients by pharmacists, 13 patients by physicians, and 6 patients by nurses. Patients who received prescriptions for both intranasal naloxone 4 mg and 8 mg were most routinely ordered by physicians (n = 3; 60%) in primary care (n = 2; 40%) for chronic opioid use (n = 2; 40%).

Patients access naloxone from many different VHI clinics. Primary care clinics prescribed the 4 mg formulation to 31 patients, 8 mg to 3 patients, and both to 2 patients. The STORM initiative was used for 37 of 106 prescriptions (35%): 4 mg intranasal naloxone was prescribed to 1 patient, 8 mg to 36 patients, and no patients received both formulations. Chronic opioid use was the most common indication (46%) with 30 patients prescribed intranasal naloxone 4 mg, 14 patients prescribed 8 mg, and 2 patients prescribed both. OUD was the indication for 24% of patients: 2 patients prescribed intranasal naloxone 4 mg, 21 patients prescribed 8 mg, and 1 patient prescribed both.

The 106 intranasal naloxone prescriptions were equally distributed across each month from April 1, 2022, to April 1, 2023. Of the 101 patients, 34 had multiple naloxone prescriptions filled during the study period. Pharmacists wrote 40 of 106 naloxone prescriptions (38%), all for the 8 mg formulation. Nurse practitioners prescribed naloxone 4 mg 30 times and 8 mg 6 times for 36 of 106 prescriptions (34%). Physicians prescribed 30 of 106 prescriptions (28%), including intranasal naloxone 4 mg 17 times and 8 mg 13 times.

Statistics were analyzed using a X² test; however, it was determined that the expected frequencies made the tests inappropriate. Differences in prescribing patterns between naloxone doses, prescriber disciplines, source of the prescription, or indications were not statistically significant.

DISCUSSION

Many pharmacists possess a scope of practice under state law and/or institution policy to prescribe naloxone. In this study, pharmacists prescribed the most naloxone prescriptions compared to physicians and nurse practitioners. Initiatives such as OEND and STORM have given pharmacists at VHI an avenue to combat the growing opioid epidemic while expanding their scope of practice. A systematic review of 67 studies found that pharmacist-led OEND programs showed a statistically significant increase in naloxone orders. A statistical significance was likely met given the large sample sizes ranging from 10 to 217,000 individuals, whereas this study only assessed a small portion of patients.¹⁴ This study contributes to the overwhelming amount of data that highlights pharmacists’ impact on overall naloxone distribution.

The STORM initiative and primary care clinics were responsible for large portions of naloxone prescriptions in this study. STORM was used by pharmacists and contributed to more than half of the higher dose naloxone prescriptions. Following a discussion with members of the pain management team, pharmacists involved in STORM prescribing were revealed to exclusively prescribe intranasal naloxone 8 mg as opposed to 4 mg. At the risk of precipitating withdrawal from higher doses of naloxone, it was agreed that this risk was heavily outweighed by the benefit of successful opioid reversal. In this context, it is expected for this avenue of prescribing to influence naloxone prescribing patterns at VHI.

Prescribing in primary care clinics was shown to be equally as substantial. Primary care-based multidisciplinary transition clinics have been reported to be associated with increased access to OUD treatment.¹⁵ Primary care clinics at VHI, or patient aligned care teams (PACT), largely consist of multidisciplinary health care teams. PACT clinicians are heavily involved in transitions of care because one system provides patients with comprehensive acute and chronic care. Continuing to encourage naloxone distribution through primary care and using STORM affords various patient populations access to high-level care.

Notable differences were observed between indications for naloxone use and the corresponding dose. Patients with OUD or SUD were more likely to receive intranasal naloxone 8 mg as opposed to patients receiving intranasal naloxone for chronic opioid use, who were more likely to receive the 4 mg dose. This may be due to a rationale to provide a higher dose of naloxone to combat overdoses in the case of ingesting substances mixed with fentanyl or xylazine.^12,13 Without standard of care guidelines, concerns remain for varying outcomes in opioid overdose prevention within vulnerable populations.

Limitations

Chart data were dependent on documentation, which may have omitted pertinent baseline characteristics and risk factors. Additional data collection could have further assessed a patient’s specific risk factors (eg, opioid dose in morphine equivalents) to draw conclusions to the dose of naloxone prescribed. The sample size was small, and the patient population was largely White and male, which minimized the generalizability of the results.

CONCLUSIONS

This study evaluated the differences in intranasal naloxone prescribing patterns within a veteran population at VHI over 12 months. Findings revealed that most prescriptions were written for intranasal naloxone 8 mg, by a pharmacist, in a primary care setting, and for chronic opioid use. The results revealed evidence of differing naloxone prescribing practices, which emphasize the need for clinical guidelines and better defined recommendations in relation to naloxone dosing.

The most evident gap in patient care could be addressed by urging the VA Pharmacy Benefits Management group to update naloxone recommendations for use to include more concrete dosing recommendations. Furthermore, it would be beneficial to re-educate clinicians on naloxone prescribing to increase awareness of different doses and the importance of equipping patients with the correct amount of naloxone in an emergency. Additional research assessing change in prescribing patterns is warranted as the use of higher dose naloxone becomes more routine.

Since 1999, annual deaths attributed to opioid overdose in the United States have increased from about 10,000 to about 50,000 in 2019.¹ During the COVID-19 pandemic > 74,000 opioid overdose deaths occurred in the US from April 2020 to April 2021.^2,3 Opioid-related overdoses now account for about 75% of all drug-related overdose deaths.¹ In 2017, the cost of opioid overdose deaths and opioid use disorder (OUD) reached $1.02 trillion in the United States and $26 million in Indiana.⁴ The total deaths and costs would likely be higher if it were not for naloxone.

Naloxone hydrochloride was first patented in the 1960s and approved by the US Food and Drug Administration (FDA) in 1971 to treat opioid-related toxicity.¹ It is the most frequently prescribed antidote for opioid toxicity due to its activity as a pure υ-opioid receptor competitive antagonist. Naloxone formulations include intramuscular, intravenous, subcutaneous, and intranasal delivery methods.⁵ According to the Centers for Disease Control and Prevention, clinicians should offer naloxone to patients at high risk for opioid-related adverse events. Risk factors include a history of overdose, opioid dosages of ≥ 50 morphine mg equivalents/day, and concurrent use of opioids with benzodiazepines.⁶

Intranasal naloxone 4 mg has become more accessible following the classification of opioid use as a public health emergency in 2017 and its over-the-counter availability since 2023. Intranasal naloxone 4 mg was approved by the FDA in 2015 for the prevention of opioid overdoses (accidental or intentional), which can be caused by heroin, fentanyl, carfentanil, hydrocodone, oxycodone, methadone, and other substances. ⁷ Fentanyl has most recently been associated with xylazine, a nonopioid tranquilizer linked to increased opioid overdose deaths.⁸ Recent data suggest that 34% of opioid overdose reversals involved ≥ 2 doses of intranasal naloxone 4 mg, which led to FDA approval of an intranasal naloxone 8 mg spray in April 2021.^9-11

Veteran Health Indiana (VHI) has implemented several initiatives to promote naloxone prescribing. Established in 2020, the Opioid Overdose Education and Naloxone Distribution (OEND) program sought to prevent opioid-related deaths through education and product distribution. These criteria included an opioid prescription for ≥ 30 days. In 2021, the Stratification Tool for Opioid Risk Mitigation (STORM) was created to identify patients at high risk of opioid overdose and allowing pharmacists to prescribe naloxone for at-risk patients without restrictions, increasing accessibility.¹²

Recent cases of fentanyl-related overdoses involving stronger fentanyl analogues highlight the need for higher naloxone dosing to prevent overdose. A pharmacokinetic comparison of intranasal naloxone 8 mg vs 4 mg demonstrated maximum plasma concentrations of 10.3 ng/mL and 5.3 ng/mL, respectively. ¹³ Patients may be at an increased risk of precipitated opioid withdrawal when using intranasal naloxone 8 mg over 4 mg; however, some patients may benefit from achieving higher serum concentrations and therefore require larger doses of naloxone.

No clinical trials have demonstrated a difference in reversal rates between naloxone doses. No clinical practice guidelines support a specific naloxone formulation, and limited US Department of Veterans Affairs (VA)-specific guidance exists. VA Naloxone Rescue: Recommendations for Use states that selection of naloxone 8 mg should be based on shared decision-making between the patient and clinician and based on individual risk factors.¹² The purpose of this study is to analyze data to determine if there is a difference in prescribing patterns of intranasal naloxone 4 mg and intranasal naloxone 8 mg.

METHODS

A retrospective chart reviews using the VA Computerized Patient Record System (CPRS) analyzed patients prescribed intranasal naloxone 4 mg or intranasal naloxone 8 mg at VHI. A patient list was generated based on active naloxone prescriptions between April 1, 2022, and April 1, 2023. Data were obtained exclusively through CPRS and patients were not contacted. This study was reviewed and deemed exempt by the Indiana University Health Institutional Review Board and the VHI Research and Development Committee.

Patients were included if they were aged ≥ 18 years and had an active prescription for intranasal naloxone 4 mg or intranasal naloxone 8 mg during the trial period. Patients were excluded if their naloxone prescription was written by a non-VHI clinician, if the dose was not 4 mg or 8 mg, or if the dosage form was other than intranasal spray.

The primary endpoint was the comparison for prescribing patterns for intranasal naloxone 4 mg and intranasal naloxone 8 mg during the study period. Secondary endpoints included total naloxone prescriptions; monthly prescriptions; number of patients with repeated naloxone prescriptions; prescriber type by naloxone dose; clinic type by naloxone dose; and documented indication for naloxone use by dose.

Demographic data collected included baseline age, sex, race, comorbid mental health conditions, and active central nervous system depressant medications on patient profile (ie, opioids, gabapentinoids, benzodiazepines, antidepressants, antipsychotics). Opioid prescriptions that were active or discontinued within the last 3 months were also recorded. Comorbid mental health conditions were collected based on the most recent clinical note before initiating medication.

Prescription-related data included strength of medication prescribed (4 mg, 8 mg, or both), documented use of medication, prescriber name, prescriber discipline, prescription entered by, number of times naloxone was filled or refilled during the study period, indication, clinic location, and clinic name. If > 1 prescription was active during the study period, the number of refills, prescriber name and clinic location of the first prescription in the study period was recorded. Additionally, the indication of OUD was differentiated from substance use disorder (SUD) if the patient was only dependent on opioids, excluding tobacco or alcohol. Patients with SUDs may include opioid dependence in addition to other substance dependence (eg, cannabis, stimulants, gabapentinoids, or benzodiazepines).

Basic descriptive statistics, including mean, ranges, and percentages were used to characterize the study subjects. For nominal data, X² tests were used. A 2-sided 5% significance level was used for all statistical tests.

RESULTS

A total of 1952 active naloxone prescriptions from 1739 patients met the inclusion criteria; none were eliminated based on the exclusion criteria and some were included multiple times because data were collected for each active prescription during the study period. One hundred one patients were randomized and included in the final analysis (Figure). Most patients identified as White (81%), male (90%), and had a mean (SD) age of 60.9 (14.2) years. Common mental health comorbidities included 59 patients with depression, 50 with tobacco use disorder, and 31 with anxiety. Eighty-four patients had opioid and 60 had antidepressants/antianxiety, and 40 had gabapentinoids prescriptions. Forty-three patients had ≥ 3 mental health comorbidities. Thirty-four patients had 2 active central nervous system depressant prescriptions, 30 had 3 active prescriptions, and 9 had ≥ 4 active prescriptions. Most patients (n = 83) had an active or recently discontinued opioid prescription (Table 1).

The 101 patients received 54 prescriptions for naloxone 8 mg and 47 for 4 mg (Table 2). Five patients received prescriptions for both the 4 mg and 8 mg intranasal naloxone formulations. Sixty-six patients had naloxone filled once (66%) during the study period. Intranasal naloxone 4 mg was prescribed to 30 patients by nurse practitioners, 17 patients by physicians, and not prescribed by pharmacists. Intranasal naloxone 8 mg was prescribed to 40 patients by pharmacists, 13 patients by physicians, and 6 patients by nurses. Patients who received prescriptions for both intranasal naloxone 4 mg and 8 mg were most routinely ordered by physicians (n = 3; 60%) in primary care (n = 2; 40%) for chronic opioid use (n = 2; 40%).

Patients access naloxone from many different VHI clinics. Primary care clinics prescribed the 4 mg formulation to 31 patients, 8 mg to 3 patients, and both to 2 patients. The STORM initiative was used for 37 of 106 prescriptions (35%): 4 mg intranasal naloxone was prescribed to 1 patient, 8 mg to 36 patients, and no patients received both formulations. Chronic opioid use was the most common indication (46%) with 30 patients prescribed intranasal naloxone 4 mg, 14 patients prescribed 8 mg, and 2 patients prescribed both. OUD was the indication for 24% of patients: 2 patients prescribed intranasal naloxone 4 mg, 21 patients prescribed 8 mg, and 1 patient prescribed both.

The 106 intranasal naloxone prescriptions were equally distributed across each month from April 1, 2022, to April 1, 2023. Of the 101 patients, 34 had multiple naloxone prescriptions filled during the study period. Pharmacists wrote 40 of 106 naloxone prescriptions (38%), all for the 8 mg formulation. Nurse practitioners prescribed naloxone 4 mg 30 times and 8 mg 6 times for 36 of 106 prescriptions (34%). Physicians prescribed 30 of 106 prescriptions (28%), including intranasal naloxone 4 mg 17 times and 8 mg 13 times.

Statistics were analyzed using a X² test; however, it was determined that the expected frequencies made the tests inappropriate. Differences in prescribing patterns between naloxone doses, prescriber disciplines, source of the prescription, or indications were not statistically significant.

DISCUSSION

Many pharmacists possess a scope of practice under state law and/or institution policy to prescribe naloxone. In this study, pharmacists prescribed the most naloxone prescriptions compared to physicians and nurse practitioners. Initiatives such as OEND and STORM have given pharmacists at VHI an avenue to combat the growing opioid epidemic while expanding their scope of practice. A systematic review of 67 studies found that pharmacist-led OEND programs showed a statistically significant increase in naloxone orders. A statistical significance was likely met given the large sample sizes ranging from 10 to 217,000 individuals, whereas this study only assessed a small portion of patients.¹⁴ This study contributes to the overwhelming amount of data that highlights pharmacists’ impact on overall naloxone distribution.

The STORM initiative and primary care clinics were responsible for large portions of naloxone prescriptions in this study. STORM was used by pharmacists and contributed to more than half of the higher dose naloxone prescriptions. Following a discussion with members of the pain management team, pharmacists involved in STORM prescribing were revealed to exclusively prescribe intranasal naloxone 8 mg as opposed to 4 mg. At the risk of precipitating withdrawal from higher doses of naloxone, it was agreed that this risk was heavily outweighed by the benefit of successful opioid reversal. In this context, it is expected for this avenue of prescribing to influence naloxone prescribing patterns at VHI.

Prescribing in primary care clinics was shown to be equally as substantial. Primary care-based multidisciplinary transition clinics have been reported to be associated with increased access to OUD treatment.¹⁵ Primary care clinics at VHI, or patient aligned care teams (PACT), largely consist of multidisciplinary health care teams. PACT clinicians are heavily involved in transitions of care because one system provides patients with comprehensive acute and chronic care. Continuing to encourage naloxone distribution through primary care and using STORM affords various patient populations access to high-level care.

Notable differences were observed between indications for naloxone use and the corresponding dose. Patients with OUD or SUD were more likely to receive intranasal naloxone 8 mg as opposed to patients receiving intranasal naloxone for chronic opioid use, who were more likely to receive the 4 mg dose. This may be due to a rationale to provide a higher dose of naloxone to combat overdoses in the case of ingesting substances mixed with fentanyl or xylazine.^12,13 Without standard of care guidelines, concerns remain for varying outcomes in opioid overdose prevention within vulnerable populations.

Limitations

Chart data were dependent on documentation, which may have omitted pertinent baseline characteristics and risk factors. Additional data collection could have further assessed a patient’s specific risk factors (eg, opioid dose in morphine equivalents) to draw conclusions to the dose of naloxone prescribed. The sample size was small, and the patient population was largely White and male, which minimized the generalizability of the results.

CONCLUSIONS

This study evaluated the differences in intranasal naloxone prescribing patterns within a veteran population at VHI over 12 months. Findings revealed that most prescriptions were written for intranasal naloxone 8 mg, by a pharmacist, in a primary care setting, and for chronic opioid use. The results revealed evidence of differing naloxone prescribing practices, which emphasize the need for clinical guidelines and better defined recommendations in relation to naloxone dosing.

The most evident gap in patient care could be addressed by urging the VA Pharmacy Benefits Management group to update naloxone recommendations for use to include more concrete dosing recommendations. Furthermore, it would be beneficial to re-educate clinicians on naloxone prescribing to increase awareness of different doses and the importance of equipping patients with the correct amount of naloxone in an emergency. Additional research assessing change in prescribing patterns is warranted as the use of higher dose naloxone becomes more routine.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; chase.d.hendrickson@vanderbilt.edu

Disclosures: None reported.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; chase.d.hendrickson@vanderbilt.edu

Disclosures: None reported.

From Vanderbilt University School of Medicine, and Vanderbilt University Medical Center, Nashville, TN.

ABSTRACT

Objective: Severe hypoglycemia can alter consciousness and inhibit oral intake, requiring nonoral rescue glucagon administration to raise blood glucose to safe levels. Thus, current guidelines recommend glucagon kit prescriptions for all patients at risk for hypoglycemia, especially patients with type 1 diabetes mellitus (T1DM). At the diabetes outpatient clinic at a tertiary medical center, glucagon prescription rates for T1DM patients remained suboptimal.

Methods: A quality improvement team analyzed patient flow through the endocrinology clinic and identified the lack of a systematic approach to assessing patients for home glucagon prescriptions as a major barrier. The team implemented 2 successive interventions. First, intake staff indicated whether patients lacked an active glucagon prescription on patients’ face sheets. Second, clinical pharmacists reviewed patient prescriptions prior to scheduled visits and pended glucagon orders for patients without active prescriptions. Of note, when a pharmacy pends an order, the pharmacist enters an order into the electronic health record (EHR) but does not sign it. The order is saved for a provider to later access and sign. A statistical process control p-chart tracked monthly prescription rates.

Results: After 7 months, glucagon prescription rates increased from a baseline of 59% to 72% as the new steady state.

Conclusion: This project demonstrates that a series of interventions can improve glucagon prescription rates for patients at risk for hypoglycemia. The project’s success stemmed from combining an EHR-generated report and interdisciplinary staff members’ involvement. Other endocrinology clinics may incorporate this approach to implement similar processes and improve glucagon prescription rates.

Keywords: diabetes, hypoglycemia, glucagon, quality improvement, prescription rates, medical student.

Hypoglycemia limits the management of blood glucose in patients with type 1 diabetes mellitus (T1DM). Severe hypoglycemia, characterized by altered mental status (AMS) or physical status requiring assistance for recovery, can lead to seizure, coma, or death.¹ Hypoglycemia in diabetes often occurs iatrogenically, primarily from insulin therapy: 30% to 40% of patients with T1DM and 10% to 30% of patients with insulin-treated type 2 diabetes mellitus experience severe hypoglycemia in a given year.² One study estimated that nearly 100,000 emergency department visits for hypoglycemia occur in the United States per year, with almost one-third resulting in hospitalization.³

Most patients self-treat mild hypoglycemia with oral intake of carbohydrates. However, since hypoglycemia-induced nausea and AMS can make oral intake more difficult or prevent it entirely, patients require a treatment that family, friends, or coworkers can administer. Rescue glucagon, prescribed as intramuscular injections or intranasal sprays, raises blood glucose to safe levels in 10 to 15 minutes.⁴ Therefore, the American Diabetes Association (ADA) recommends glucagon for all patients at risk for hypoglycemia, especially patients with T1DM.⁵ Despite the ADA’s recommendation, current evidence suggests suboptimal glucagon prescription rates, particularly in patients with T1DM. One study reported that, although 85% of US adults with T1DM had formerly been prescribed glucagon, only 68% of these patients (57.8% overall) had a current prescription.⁴ Few quality improvement efforts have tackled increasing prescription rates. Prior successful studies have attempted to do so via pharmacist-led educational interventions for providers⁶ and via electronic health record (EHR) notifications for patient risk.⁷ The project described here aimed to expand upon prior studies with a quality improvement project to increase glucagon prescription rates among patients at risk for severe hypoglycemia.

Methods

Setting

This study was conducted at a tertiary medical center’s outpatient diabetes clinic; the clinic treats more than 9500 patients with DM annually, more than 2700 of whom have T1DM. In the clinic’s multidisciplinary care model, patients typically follow up every 3 to 6 months, alternating between appointments with fellowship-trained endocrinologists and advanced practice providers (APPs). In addition to having certified diabetes educators, the clinic employs 2 dedicated clinical pharmacists whose duties include assisting providers in prescription management, helping patients identify the most affordable way to obtain their medications, and educating patients regarding their medications.

Patient flow through the clinic involves close coordination with multiple health professionals. Medical assistants (MAs) and licensed practical nurses (LPNs) perform patient intake, document vital signs, and ask screening questions, including dates of patients’ last hemoglobin A1c tests and diabetic eye examination. After intake, the provider (endocrinologist or APP) sees the patient. Once the appointment concludes, patients proceed to the in-house phlebotomy laboratory as indicated and check out with administrative staff to schedule future appointments.

Project Design

From August 2021 through June 2022, teams of medical students at the tertiary center completed this project as part of a 4-week integrated science course on diabetes. Longitudinal supervision by an endocrinology faculty member ensured project continuity. The project employed the Standards for QUality Improvement Reporting Excellence (SQUIRE 2.0) method for reporting.⁸

Stakeholder analysis took place in August 2021. Surveyed clinic providers identified patients with T1DM as the most appropriate population and the outpatient setting as the most appropriate site for intervention. A fishbone diagram illustrated stakeholders to interview, impacts of the clinical flow, information technology to leverage, and potential holes contributing to glucagon prescription conversations falling through.

Interviews with T1DM patients, clinical pharmacists, APPs, MAs/LPNs, and endocrinologists identified barriers to glucagon prescription. The interviews and a process map analysis revealed several themes. While patients and providers understood the importance of glucagon prescription, barriers included glucagon cost, prescription fill burden, and, most pervasively, providers forgetting to ask patients whether they have a glucagon prescription and failing to consider glucagon prescriptions.For this study, each team of medical students worked on the project for 1 month. The revolving teams of medical students met approximately once per week for the duration of the project to review data and implementation phases. At the end of each month, the current team recorded the steps they had taken and information they had analyzed in a shared document, prepared short videos summarizing the work completed, and proposed next steps for the incoming team to support knowledge generation and continuity. Students from outgoing teams were available to contact if incoming teams had any questions.

Interventions

In the first implementation phase, which was carried out over 4 months (December 2021 to March 2022), the patient care manager trained MAs/LPNs to write a glucagon reminder on patients’ face sheets. At check-in, MAs/LPNs screened for a current glucagon prescription. If the patient lacked an up-to-date prescription, the MAs/LPNs hand-wrote a reminder on the patient’s face sheet, which was given to the provider immediately prior to seeing the patient. The clinical staff received an email explaining the intervention beforehand; the daily intake staff email included project reminders.

In the second implementation phase, which started in April 2022, had been carried out for 3 months at the time of this report, and is ongoing, clinical pharmacists have been pending glucagon prescriptions ahead of patients’ appointments. Each week, the pharmacists generate an EHR report that includes all patients with T1DM who have attended at least 1 appointment at the clinic within the past year (regardless of whether each patient possessed an active and up-to-date glucagon prescription) and the date of each patient’s next appointment. For patients who have an appointment in the upcoming week and lack an active glucagon prescription, the pharmacists run a benefits investigation to determine the insurance-preferred glucagon formulation and then pend the appropriate order in the EHR. During the patient’s next appointment, the EHR prompts the provider to review and sign the pharmacist’s pended order (Figure 1).

Measures

This project used a process measure in its analysis: the percentage of patients with T1DM with an active glucagon prescription at the time of their visit to the clinic. The patient population included all patients with a visit diagnosis of T1DM seen by an APP at the clinic during the time scope of the project. The project’s scope was limited to patients seen by APPs to help standardize appointment comparisons, with the intent to expand to the endocrinologist staff if the interventions proved successful with APPs. Patients seen by APPs were also under the care of endocrinologists and seen by them during this time period. The project excluded no patients.

Each individual patient appointment represented a data point: a time at which an APP could prescribe glucagon for a patient with T1DM. Thus, a single patient who had multiple appointments during the study period would generate multiple data points in this study.

Specific Aims and Analysis

For all T1DM patients at the clinic seen by an APP during the study period, the project aimed to increase the percentage with an active and up-to-date glucagon prescription from 58.8% to 70% over a 6-month period, a relatively modest goal appropriate for the time constraints and that would be similar to the changes seen in previous work in the same clinic.⁹

This project analyzed de-identified data using a statistical process control chart (specifically, a p-chart) and standard rules for assessing special-cause signals and thus statistical significance.

Results

Baseline data were collected from October 2020 to September 2021. During this time, APPs saw 1959 T1DM patients, of whom 1152 (58.8%) had an active glucagon prescription at the time of visit and 41.2% lacked a glucagon prescription (Figure 2). During the 4 months of implementation phase 1, analysis of the statistical process control chart identified no special cause signal. Therefore, the project moved to a second intervention with implementation phase 2 in April 2022 (3 months of postintervention data are reported). During the entire intervention, 731 of 1080 (67.7%) patients had a glucagon prescription. The average for the last 2 months, with phase 2 fully implemented, was 72.3%, surpassing the 70% threshold identified as the study target (Figure 3).

Interviews with clinical pharmacists during implementation phase 2 revealed that generating the EHR report and reviewing patients with glucagon prescription indications resulted in variable daily workload increases ranging from approximately 15 to 45 minutes, depending on the number of patients requiring intervention that day. During the first month of implementation phase 2, the EHR report required repeated modification to fulfill the intervention needs. Staffing changes over the intervention period potentially impacted the pattern of glucagon prescribing. This project excluded the 2 months immediately prior to implementation phase 1, from October 2021 to November 2021, because the staff had begun having discussions about this initiative, which may have influenced glucagon prescription rates.

Discussion

This project evaluated 2 interventions over the course of 7 months to determine their efficacy in increasing the frequency of glucagon prescribing for individuals with T1DM in an endocrinology clinic. These interventions were associated with increased prescribing from a baseline of 58.8% to 72.3% over the last 2 months of the project. In the first intervention, performed over 4 months, MAs/LPNs wrote reminders on the appropriate patients’ face sheets, which were given to providers prior to appointments. This project adapted the approach from a successful previous quality improvement study on increasing microalbuminuria screening rates.⁹ However, glucagon prescription rates did not increase significantly, likely because, unlike with microalbuminuria screenings, MAs/LPNs could not pend glucagon prescriptions.

In the second intervention, performed over 3 months, clinical pharmacists pended glucagon prescriptions for identified eligible patients. Glucagon prescribing rates increased considerably, with rates of 72.3% and 72.4% over May and June 2021, respectively, indicating that the intervention successfully established a new higher steady state of proportion of patient visits with active glucagon prescriptions compared with the baseline rate of 58.8%. Given that the baseline data for this clinic were higher than the baseline glucagon prescription rates reported in other studies (49.3%),¹⁰ this intervention could have a major impact in clinics with a baseline more comparable to conditions in that study.

This project demonstrated how a combination of an EHR-generated report and interdisciplinary involvement provides an actionable process to increase glucagon prescription rates for patients with T1DM. Compared to prior studies that implemented passive interventions, such as a note template that relies on provider adherence,⁷ this project emphasizes the benefit of implementing an active systems-level intervention with a pre-pended order.

Regarding prior studies, 1 large, 2-arm study of clinical pharmacists proactively pending orders for appropriate patients showed a 56% glucagon prescription rate in the intervention group, compared with 0.9% in the control group with no pharmacist intervention.¹¹ Our project had a much higher baseline rate: 58.8% prior to intervention vs 0.9% in the nonintervention group for the previous study—likely due to its chosen location’s status as an endocrinology clinic rather than a general health care setting.

A different study that focused on patient education rather than glucagon prescription rates used similar EHR-generated reports to identify appropriate patients and assessed glucagon prescription needs during check-in. Following the educational interventions in that study, patients reporting self-comfort and education with glucagon administration significantly increased from 66.2% to 83.2%, and household member comfort and education with glucagon administration increased from 50.8% to 79.7%. This suggests the possibility of expanding the use of the EHR-generated report to assist not only with increasing glucagon prescription rates, but also with patient education on glucagon use rates and possibly fill rates.⁷ While novel glucagon products may change uptake rates, no new glucagon products arose or were prescribed at this clinic during the course of data collection.

Of note, our project increased the workload on clinical pharmacists. The pharmacists agreed to participate, despite the increased work, after a collaborative discussion about how to best address the need to increase glucagon prescriptions or patient safety; the pharmacy department had initially agreed to collaborate specifically to identify and attend to unmet needs such as this one. Although this project greatly benefited from the expertise and enthusiasm of the clinical pharmacists involved, this tradeoff requires further study to determine sustainability.

Limitations

This project had several limitations. Because of the structure in which this intervention occurred (a year-long course with rotating groups of medical students), there was a necessary component of time constraint, and this project had just 2 implementation phases, for a total of 7 months of postintervention data. The clinic has permanently implemented these changes into its workflow, but subsequent assessments are needed to monitor the effects and assess sustainability.

The specific clinical site chosen for this study benefited from dedicated onsite clinical pharmacists, who are not available at all comparable clinical sites. Due to feasibility, this project only assessed whether the providers prescribed the glucagon, not whether the patients filled the prescriptions and used the glucagon when necessary. Although prescribing rates increased in our study, it cannot be assumed that fill rates increased identically.

Finally, interventions relying on EHR-generated reports carry inherent limitations, such as the risk of misidentification or omission of patients who had indications for a glucagon prescription. The project attempted to mitigate this limitation through random sampling of the EHR report to ensure accuracy. Additionally, EHR-generated reports encourage sustainability and expansion to all clinic patients, with far less required overhead work compared to manually derived data.

Future investigations may focus on expanding this intervention to all patients at risk for hypoglycemia, as well as to study further interventions into prescription fill rates and glucagon use rates.

Conclusion

This project indicates that a proactive, interdisciplinary quality improvement project can increase glucagon prescription rates for patients with T1DM in the outpatient setting. The most effective intervention mobilized clinical pharmacists to identify patients with indications for a glucagon prescription using an integrated EHR-generated report and subsequently pend a glucagon order for the endocrinology provider to sign during the visit. The strengths of the approach included using a multidisciplinary team, minimizing costs to patients by leveraging the pharmacists’ expertise to ensure insurance coverage of specific formulations, and utilizing automatic EHR reporting to streamline patient identification. Ideally, improvements in glucagon prescription rates should ultimately decrease hospitalizations and improve treatment of severe hypoglycemia for at-risk patients.

Corresponding author: Chase D. Hendrickson, MD, MPH; chase.d.hendrickson@vanderbilt.edu

Disclosures: None reported.

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY