Hospital Medicine

Will the new US Department of Veterans Affairs (VA) pharmacy software be safe and effective? That was the topic when David Case, the VA Deputy Inspector General, spoke in the US House of Representatives Veterans Affairs Committee technology modernization subcommittee hearing on February 15.

Questions like that have dogged the project since 2018, when the VA began rolling out the Oracle Cerner electronic health record (EHR) system as the successor to ViSTA.

The Oracle system has been beset by one glitch after another since its arrival. And in that time, Case said, the VA Office of Inspector General (OIG) has been engaging with VA employees at sites in Washington, Oregon, Ohio, Illinois, and other locations where the modernization program has been piloted.

The most recent OIG investigation of pharmacy-related patient safety issues began with a review of an allegation of a prescription backlog at Columbus, Ohio, where the system went live on April 30, 2022. The OIG found that facility leaders took “timely and sustainable steps” to manage that issue. However, other unresolved patient safety issues came to light, such as medication inaccuracies, inaccurate medication data, and insufficient staffing. The OIG also found staff were creating “numerous work arounds” to provide patient care, and that the volume of staff educational materials for pharmacy-related functions was “overwhelming.”

Those problems were just the latest in a long queue. In May 2021, after the first VA deployment of the new EHR at the Mann-Grandstaff VA Medical Center in Spokane, Washington, a pharmacy patient safety team under the VA National Center for Patient Safety (NCPS) also had identified patient safety issues and “multiple” concerns regarding the system’s usability. For example, updates to a patient’s active medication list were not routinely reflected at the patient’s next appointment. Despite knowing about such challenges, Case noted in his report, VA leaders deployed the new EHR at 4 more VA medical centers.

Cerner/ViSTA Communication

One major cause of the current problems is the way the systems “talk” to each other. EHR information is communicated between VHA facilities through channels that include the Joint Longitudinal Viewer (JLV) and the Health Data Repository, which stores patient-specific clinical information from both the legacy and the new EHR systems. The JLV application allows clinicians to access a read only version of a patient’s EHR from both systems.

Every medication used in VHA has a VA Unique Identifier (VUID). When a patient is prescribed a medication at a new EHR site, that medication’s VUID is sent to the Health Data Repository. If that patient seeks care from a legacy health care practitioner (HCP), and that HCP enters a medication order, a software interface accesses the VUID from the Health Data Repository to verify that the medication being prescribed is safe and compatible with the medications and allergies previously documented in the patient’s record.

However, on March 31, 2023, staff from a ViSTA site found an incorrect medication order when prescribing a new medication to a patient who had received care and medications at a new EHR site. This in turn led to the discovery that an error in Oracle software coding had resulted in the “widespread transmission” of incorrect VUIDs from new EHR sites to legacy EHR sites, the OIG found. VA leaders and HCPs were notified of the potential clinical impact and were given specific instructions on how to mitigate the issue. They were asked to “please share widely.”

On top of that, days later, patient safety managers across the Veterans Health Administration (VHA) were told that drug-to-drug interactions, duplicate medication orders, and allergy checks were not functioning as expected, and they too were provided with remedial actions.

Oracle applied a successful software patch on in April 2023, to ensure accurate VUIDs were attached to all mail order pharmacy–processed prescriptions from that date forward. However, the OIG learned the incorrect VUIDs sent from new EHR sites and stored in the Health Data Repository from as far back as October 2020 had not been corrected. Case told the subcommittee that on November 29, 2023, the VHA Pharmacy Council reported withdrawing a request for Oracle to send corrected medication VUID data to the Health Data Repository, on the presumption that remaining inaccurate VUIDs would expire in early April 2024, and the data would be corrected at that time.

The OIG is concerned, Case said, that patient medication data remains inaccurate almost a year after VA learned of the issue. The mail order pharmacy-related data generated from approximately 120,000 patients served by new EHR sites are still incorrect. These patients face an ongoing risk of an adverse medication-related event if they receive care and medications from a VA medical center using the legacy EHR system.

The OIG also learned of other problems associated with transmission of medication and allergy information, which could have consequences such as:

• Patient medications being discontinued or stopped by new HCPs using Cerner that appear in ViSTA as active and current prescriptions;
• Allergy-warning messages not appearing when intended or inappropriately appearing for the wrong medication;
• Duplicate medication order checks not appearing when intended or inappropriately appearing for the wrong drug;
• Patient active medication lists having incomplete or inaccurate information, such as missing prescriptions, duplicate prescriptions, or incorrect medication order statuses.

The OIG warned VHA employees about the risks, although it wasn’t possible to determine who might actually be at risk. A VHA leader told the OIG that all patients who have been prescribed any medications or have medication allergies documented at a at a Cerner site are at risk. That could mean as many as 250,000 patients: As of September 2023, approximately 190,000 patients had a medication prescribed and 126,000 had an allergy documented at a new EHR site.

Case Example

Not surprisingly, “the OIG is not confident in [EHRM-Integration Office] leaders’ oversight and control of the new systems’ Health Data Repository interface programming,” Case said. He cited the case of a patient with posttraumatic stress disorder and traumatic brain injury with adrenal insufficiency. Four days prior to admission, a ViSTA site pharmacist used the EHR to perform a medication reconciliation for the patient. The data available did not include the patient’s most recent prednisone prescription, which had been ordered by an HCP at a facility using Cerner.

A nurse practitioner performed another reconciliation when the patient was admitted to the residential program, but the patient was unsure of all their medications. Because the most recent prednisone prescription was not visible in ViSTA, the prednisone appeared to have been completed at least 3 months prior to admission and was therefore not prescribed in the admission medication orders.

Five days into the residential program, the patient began exhibiting unusual behaviors associated with the lack of prednisone. The patient realized they needed more prednisone, but the nurse explained there was no prednisone on the patient’s medication list. Eventually, the patient found the active prednisone order on their personal cell phone and was transferred to a local emergency department for care.

Work Arounds

The VHA’s efforts to forestall or mitigate system errors have in some cases had a cascade effect. For example, HCPs must essentially back up what the automated software is intended to do, with “complex, time-consuming” multistep manual safety checks when prescribing new medications for patients previously cared for at a Cerner site. The OIG is concerned that this increased vigilance is “unsustainable” by pharmacists and frontline staff and could lead to burnout and medication-related patient safety events. After the new EHR launched, the OIG found, burnout symptoms for pharmacy staff increased. Nonetheless, Case told the committee, OIG staff “have observed [employees’] unwavering commitment to prioritizing the care of patients while mitigating implementation challenges.”

EHR-related workload burdens have necessitated other adjustments. Columbus, for instance, hired 9 full-time clinical pharmacists—a 62% staffing increase—to help reduce the backlog. Pharmacy leaders created approximately 29 additional work-arounds to support pharmacy staff and prevent delays. Facility pharmacy leaders also developed approximately 25 educational materials, such as tip sheets, reference guides, and job aids. The OIG’s concern—apart from the overwhelming amount of information for staff to implement—is that such prophylactic measures may in fact give rise to inconsistent practices, which increase risks to patient safety.

Committed to Working With the VA

Mike Sicilia, executive vice president of Oracle Corporation, told lawmakers in the hearing, “After the initial deployments, it became clear that the pharmacy system needed to be enhanced to better meet VA’s needs. To that end, in August 2022, shortly after Oracle completed its acquisition of Cerner, VA contracted with us for seven enhancements that overall would adapt the pharmacy system to a more bidirectional system between VA providers placing prescription orders and VA pharmacists fulfilling and dispensing them.” Those enhancements are all live for VA providers and pharmacists to use now, he said, except for one that is undergoing additional testing.

He added, “As with any healthcare technology system, there is a need for continuous improvements but that does not mean the system is not safe and effective in its current state. Oracle is committed to working with VA … throughout the reset period to identify workflows and other items that can be simplified or streamlined to improve the overall user and pharmacy experience.”

Standardizing workflows and ensuring training and communications to pharmacists about the latest updates will discourage use of work-arounds, Sicilia said, and “help with improving morale and satisfaction with the system.” During a visit in early February by VA and the Oracle team to the Lovell Federal Health Care Center in North Chicago, “feedback from pharmacists was positive about the training and readiness for using the new pharmacy system.”

The backlog, at least, may be resolved. Sicilia said on average more than 215,000 outpatient prescriptions are being filled each month. “The current live sites do not have a backlog in filling prescriptions. Recent data from this month show that three of the five live sites have zero prescriptions waiting to be processed that are older than seven days. The two other live sites have an average of two prescriptions older than seven days,” he said.

Although Oracle Health has since resolved some of the identified issues, the OIG is concerned that the new EHR will continue to be deployed at medical facilities despite “myriad” as-yet unresolved issues related to inaccurate medication ordering, reconciliation, and dispensing. The VHA has paused Cerner deployments multiple times.

“It is unclear whether identified problems are being adequately resolved before additional deployments,” Case said. “There is also the question of whether there is sufficient transparency and communication among EHRM-IO, VHA and facility leaders, VA leaders, and Oracle Health needed for quality control and critical coordination. Trust in VA is also dependent on patients being fully and quickly advised when issues affecting them are identified and addressed. As VA moves toward its deployment next month at a complex facility jointly operated with the Department of Defense, transparency, communication, and program management will be essential to getting it right. Failures in these areas risk cascading problems.”

Will the new US Department of Veterans Affairs (VA) pharmacy software be safe and effective? That was the topic when David Case, the VA Deputy Inspector General, spoke in the US House of Representatives Veterans Affairs Committee technology modernization subcommittee hearing on February 15.

Questions like that have dogged the project since 2018, when the VA began rolling out the Oracle Cerner electronic health record (EHR) system as the successor to ViSTA.

The Oracle system has been beset by one glitch after another since its arrival. And in that time, Case said, the VA Office of Inspector General (OIG) has been engaging with VA employees at sites in Washington, Oregon, Ohio, Illinois, and other locations where the modernization program has been piloted.

The most recent OIG investigation of pharmacy-related patient safety issues began with a review of an allegation of a prescription backlog at Columbus, Ohio, where the system went live on April 30, 2022. The OIG found that facility leaders took “timely and sustainable steps” to manage that issue. However, other unresolved patient safety issues came to light, such as medication inaccuracies, inaccurate medication data, and insufficient staffing. The OIG also found staff were creating “numerous work arounds” to provide patient care, and that the volume of staff educational materials for pharmacy-related functions was “overwhelming.”

Those problems were just the latest in a long queue. In May 2021, after the first VA deployment of the new EHR at the Mann-Grandstaff VA Medical Center in Spokane, Washington, a pharmacy patient safety team under the VA National Center for Patient Safety (NCPS) also had identified patient safety issues and “multiple” concerns regarding the system’s usability. For example, updates to a patient’s active medication list were not routinely reflected at the patient’s next appointment. Despite knowing about such challenges, Case noted in his report, VA leaders deployed the new EHR at 4 more VA medical centers.

Cerner/ViSTA Communication

One major cause of the current problems is the way the systems “talk” to each other. EHR information is communicated between VHA facilities through channels that include the Joint Longitudinal Viewer (JLV) and the Health Data Repository, which stores patient-specific clinical information from both the legacy and the new EHR systems. The JLV application allows clinicians to access a read only version of a patient’s EHR from both systems.

Every medication used in VHA has a VA Unique Identifier (VUID). When a patient is prescribed a medication at a new EHR site, that medication’s VUID is sent to the Health Data Repository. If that patient seeks care from a legacy health care practitioner (HCP), and that HCP enters a medication order, a software interface accesses the VUID from the Health Data Repository to verify that the medication being prescribed is safe and compatible with the medications and allergies previously documented in the patient’s record.

However, on March 31, 2023, staff from a ViSTA site found an incorrect medication order when prescribing a new medication to a patient who had received care and medications at a new EHR site. This in turn led to the discovery that an error in Oracle software coding had resulted in the “widespread transmission” of incorrect VUIDs from new EHR sites to legacy EHR sites, the OIG found. VA leaders and HCPs were notified of the potential clinical impact and were given specific instructions on how to mitigate the issue. They were asked to “please share widely.”

On top of that, days later, patient safety managers across the Veterans Health Administration (VHA) were told that drug-to-drug interactions, duplicate medication orders, and allergy checks were not functioning as expected, and they too were provided with remedial actions.

Oracle applied a successful software patch on in April 2023, to ensure accurate VUIDs were attached to all mail order pharmacy–processed prescriptions from that date forward. However, the OIG learned the incorrect VUIDs sent from new EHR sites and stored in the Health Data Repository from as far back as October 2020 had not been corrected. Case told the subcommittee that on November 29, 2023, the VHA Pharmacy Council reported withdrawing a request for Oracle to send corrected medication VUID data to the Health Data Repository, on the presumption that remaining inaccurate VUIDs would expire in early April 2024, and the data would be corrected at that time.

The OIG is concerned, Case said, that patient medication data remains inaccurate almost a year after VA learned of the issue. The mail order pharmacy-related data generated from approximately 120,000 patients served by new EHR sites are still incorrect. These patients face an ongoing risk of an adverse medication-related event if they receive care and medications from a VA medical center using the legacy EHR system.

The OIG also learned of other problems associated with transmission of medication and allergy information, which could have consequences such as:

• Patient medications being discontinued or stopped by new HCPs using Cerner that appear in ViSTA as active and current prescriptions;
• Allergy-warning messages not appearing when intended or inappropriately appearing for the wrong medication;
• Duplicate medication order checks not appearing when intended or inappropriately appearing for the wrong drug;
• Patient active medication lists having incomplete or inaccurate information, such as missing prescriptions, duplicate prescriptions, or incorrect medication order statuses.

The OIG warned VHA employees about the risks, although it wasn’t possible to determine who might actually be at risk. A VHA leader told the OIG that all patients who have been prescribed any medications or have medication allergies documented at a at a Cerner site are at risk. That could mean as many as 250,000 patients: As of September 2023, approximately 190,000 patients had a medication prescribed and 126,000 had an allergy documented at a new EHR site.

Case Example

Not surprisingly, “the OIG is not confident in [EHRM-Integration Office] leaders’ oversight and control of the new systems’ Health Data Repository interface programming,” Case said. He cited the case of a patient with posttraumatic stress disorder and traumatic brain injury with adrenal insufficiency. Four days prior to admission, a ViSTA site pharmacist used the EHR to perform a medication reconciliation for the patient. The data available did not include the patient’s most recent prednisone prescription, which had been ordered by an HCP at a facility using Cerner.

A nurse practitioner performed another reconciliation when the patient was admitted to the residential program, but the patient was unsure of all their medications. Because the most recent prednisone prescription was not visible in ViSTA, the prednisone appeared to have been completed at least 3 months prior to admission and was therefore not prescribed in the admission medication orders.

Five days into the residential program, the patient began exhibiting unusual behaviors associated with the lack of prednisone. The patient realized they needed more prednisone, but the nurse explained there was no prednisone on the patient’s medication list. Eventually, the patient found the active prednisone order on their personal cell phone and was transferred to a local emergency department for care.

Work Arounds

The VHA’s efforts to forestall or mitigate system errors have in some cases had a cascade effect. For example, HCPs must essentially back up what the automated software is intended to do, with “complex, time-consuming” multistep manual safety checks when prescribing new medications for patients previously cared for at a Cerner site. The OIG is concerned that this increased vigilance is “unsustainable” by pharmacists and frontline staff and could lead to burnout and medication-related patient safety events. After the new EHR launched, the OIG found, burnout symptoms for pharmacy staff increased. Nonetheless, Case told the committee, OIG staff “have observed [employees’] unwavering commitment to prioritizing the care of patients while mitigating implementation challenges.”

EHR-related workload burdens have necessitated other adjustments. Columbus, for instance, hired 9 full-time clinical pharmacists—a 62% staffing increase—to help reduce the backlog. Pharmacy leaders created approximately 29 additional work-arounds to support pharmacy staff and prevent delays. Facility pharmacy leaders also developed approximately 25 educational materials, such as tip sheets, reference guides, and job aids. The OIG’s concern—apart from the overwhelming amount of information for staff to implement—is that such prophylactic measures may in fact give rise to inconsistent practices, which increase risks to patient safety.

Committed to Working With the VA

Mike Sicilia, executive vice president of Oracle Corporation, told lawmakers in the hearing, “After the initial deployments, it became clear that the pharmacy system needed to be enhanced to better meet VA’s needs. To that end, in August 2022, shortly after Oracle completed its acquisition of Cerner, VA contracted with us for seven enhancements that overall would adapt the pharmacy system to a more bidirectional system between VA providers placing prescription orders and VA pharmacists fulfilling and dispensing them.” Those enhancements are all live for VA providers and pharmacists to use now, he said, except for one that is undergoing additional testing.

He added, “As with any healthcare technology system, there is a need for continuous improvements but that does not mean the system is not safe and effective in its current state. Oracle is committed to working with VA … throughout the reset period to identify workflows and other items that can be simplified or streamlined to improve the overall user and pharmacy experience.”

Standardizing workflows and ensuring training and communications to pharmacists about the latest updates will discourage use of work-arounds, Sicilia said, and “help with improving morale and satisfaction with the system.” During a visit in early February by VA and the Oracle team to the Lovell Federal Health Care Center in North Chicago, “feedback from pharmacists was positive about the training and readiness for using the new pharmacy system.”

The backlog, at least, may be resolved. Sicilia said on average more than 215,000 outpatient prescriptions are being filled each month. “The current live sites do not have a backlog in filling prescriptions. Recent data from this month show that three of the five live sites have zero prescriptions waiting to be processed that are older than seven days. The two other live sites have an average of two prescriptions older than seven days,” he said.

Although Oracle Health has since resolved some of the identified issues, the OIG is concerned that the new EHR will continue to be deployed at medical facilities despite “myriad” as-yet unresolved issues related to inaccurate medication ordering, reconciliation, and dispensing. The VHA has paused Cerner deployments multiple times.

“It is unclear whether identified problems are being adequately resolved before additional deployments,” Case said. “There is also the question of whether there is sufficient transparency and communication among EHRM-IO, VHA and facility leaders, VA leaders, and Oracle Health needed for quality control and critical coordination. Trust in VA is also dependent on patients being fully and quickly advised when issues affecting them are identified and addressed. As VA moves toward its deployment next month at a complex facility jointly operated with the Department of Defense, transparency, communication, and program management will be essential to getting it right. Failures in these areas risk cascading problems.”

Will the new US Department of Veterans Affairs (VA) pharmacy software be safe and effective? That was the topic when David Case, the VA Deputy Inspector General, spoke in the US House of Representatives Veterans Affairs Committee technology modernization subcommittee hearing on February 15.

Questions like that have dogged the project since 2018, when the VA began rolling out the Oracle Cerner electronic health record (EHR) system as the successor to ViSTA.

The Oracle system has been beset by one glitch after another since its arrival. And in that time, Case said, the VA Office of Inspector General (OIG) has been engaging with VA employees at sites in Washington, Oregon, Ohio, Illinois, and other locations where the modernization program has been piloted.

The most recent OIG investigation of pharmacy-related patient safety issues began with a review of an allegation of a prescription backlog at Columbus, Ohio, where the system went live on April 30, 2022. The OIG found that facility leaders took “timely and sustainable steps” to manage that issue. However, other unresolved patient safety issues came to light, such as medication inaccuracies, inaccurate medication data, and insufficient staffing. The OIG also found staff were creating “numerous work arounds” to provide patient care, and that the volume of staff educational materials for pharmacy-related functions was “overwhelming.”

Those problems were just the latest in a long queue. In May 2021, after the first VA deployment of the new EHR at the Mann-Grandstaff VA Medical Center in Spokane, Washington, a pharmacy patient safety team under the VA National Center for Patient Safety (NCPS) also had identified patient safety issues and “multiple” concerns regarding the system’s usability. For example, updates to a patient’s active medication list were not routinely reflected at the patient’s next appointment. Despite knowing about such challenges, Case noted in his report, VA leaders deployed the new EHR at 4 more VA medical centers.

Cerner/ViSTA Communication

One major cause of the current problems is the way the systems “talk” to each other. EHR information is communicated between VHA facilities through channels that include the Joint Longitudinal Viewer (JLV) and the Health Data Repository, which stores patient-specific clinical information from both the legacy and the new EHR systems. The JLV application allows clinicians to access a read only version of a patient’s EHR from both systems.

Every medication used in VHA has a VA Unique Identifier (VUID). When a patient is prescribed a medication at a new EHR site, that medication’s VUID is sent to the Health Data Repository. If that patient seeks care from a legacy health care practitioner (HCP), and that HCP enters a medication order, a software interface accesses the VUID from the Health Data Repository to verify that the medication being prescribed is safe and compatible with the medications and allergies previously documented in the patient’s record.

However, on March 31, 2023, staff from a ViSTA site found an incorrect medication order when prescribing a new medication to a patient who had received care and medications at a new EHR site. This in turn led to the discovery that an error in Oracle software coding had resulted in the “widespread transmission” of incorrect VUIDs from new EHR sites to legacy EHR sites, the OIG found. VA leaders and HCPs were notified of the potential clinical impact and were given specific instructions on how to mitigate the issue. They were asked to “please share widely.”

On top of that, days later, patient safety managers across the Veterans Health Administration (VHA) were told that drug-to-drug interactions, duplicate medication orders, and allergy checks were not functioning as expected, and they too were provided with remedial actions.

Oracle applied a successful software patch on in April 2023, to ensure accurate VUIDs were attached to all mail order pharmacy–processed prescriptions from that date forward. However, the OIG learned the incorrect VUIDs sent from new EHR sites and stored in the Health Data Repository from as far back as October 2020 had not been corrected. Case told the subcommittee that on November 29, 2023, the VHA Pharmacy Council reported withdrawing a request for Oracle to send corrected medication VUID data to the Health Data Repository, on the presumption that remaining inaccurate VUIDs would expire in early April 2024, and the data would be corrected at that time.

The OIG is concerned, Case said, that patient medication data remains inaccurate almost a year after VA learned of the issue. The mail order pharmacy-related data generated from approximately 120,000 patients served by new EHR sites are still incorrect. These patients face an ongoing risk of an adverse medication-related event if they receive care and medications from a VA medical center using the legacy EHR system.

The OIG also learned of other problems associated with transmission of medication and allergy information, which could have consequences such as:

• Patient medications being discontinued or stopped by new HCPs using Cerner that appear in ViSTA as active and current prescriptions;
• Allergy-warning messages not appearing when intended or inappropriately appearing for the wrong medication;
• Duplicate medication order checks not appearing when intended or inappropriately appearing for the wrong drug;
• Patient active medication lists having incomplete or inaccurate information, such as missing prescriptions, duplicate prescriptions, or incorrect medication order statuses.

The OIG warned VHA employees about the risks, although it wasn’t possible to determine who might actually be at risk. A VHA leader told the OIG that all patients who have been prescribed any medications or have medication allergies documented at a at a Cerner site are at risk. That could mean as many as 250,000 patients: As of September 2023, approximately 190,000 patients had a medication prescribed and 126,000 had an allergy documented at a new EHR site.

Case Example

Not surprisingly, “the OIG is not confident in [EHRM-Integration Office] leaders’ oversight and control of the new systems’ Health Data Repository interface programming,” Case said. He cited the case of a patient with posttraumatic stress disorder and traumatic brain injury with adrenal insufficiency. Four days prior to admission, a ViSTA site pharmacist used the EHR to perform a medication reconciliation for the patient. The data available did not include the patient’s most recent prednisone prescription, which had been ordered by an HCP at a facility using Cerner.

A nurse practitioner performed another reconciliation when the patient was admitted to the residential program, but the patient was unsure of all their medications. Because the most recent prednisone prescription was not visible in ViSTA, the prednisone appeared to have been completed at least 3 months prior to admission and was therefore not prescribed in the admission medication orders.

Five days into the residential program, the patient began exhibiting unusual behaviors associated with the lack of prednisone. The patient realized they needed more prednisone, but the nurse explained there was no prednisone on the patient’s medication list. Eventually, the patient found the active prednisone order on their personal cell phone and was transferred to a local emergency department for care.

Work Arounds

The VHA’s efforts to forestall or mitigate system errors have in some cases had a cascade effect. For example, HCPs must essentially back up what the automated software is intended to do, with “complex, time-consuming” multistep manual safety checks when prescribing new medications for patients previously cared for at a Cerner site. The OIG is concerned that this increased vigilance is “unsustainable” by pharmacists and frontline staff and could lead to burnout and medication-related patient safety events. After the new EHR launched, the OIG found, burnout symptoms for pharmacy staff increased. Nonetheless, Case told the committee, OIG staff “have observed [employees’] unwavering commitment to prioritizing the care of patients while mitigating implementation challenges.”

EHR-related workload burdens have necessitated other adjustments. Columbus, for instance, hired 9 full-time clinical pharmacists—a 62% staffing increase—to help reduce the backlog. Pharmacy leaders created approximately 29 additional work-arounds to support pharmacy staff and prevent delays. Facility pharmacy leaders also developed approximately 25 educational materials, such as tip sheets, reference guides, and job aids. The OIG’s concern—apart from the overwhelming amount of information for staff to implement—is that such prophylactic measures may in fact give rise to inconsistent practices, which increase risks to patient safety.

Committed to Working With the VA

Mike Sicilia, executive vice president of Oracle Corporation, told lawmakers in the hearing, “After the initial deployments, it became clear that the pharmacy system needed to be enhanced to better meet VA’s needs. To that end, in August 2022, shortly after Oracle completed its acquisition of Cerner, VA contracted with us for seven enhancements that overall would adapt the pharmacy system to a more bidirectional system between VA providers placing prescription orders and VA pharmacists fulfilling and dispensing them.” Those enhancements are all live for VA providers and pharmacists to use now, he said, except for one that is undergoing additional testing.

He added, “As with any healthcare technology system, there is a need for continuous improvements but that does not mean the system is not safe and effective in its current state. Oracle is committed to working with VA … throughout the reset period to identify workflows and other items that can be simplified or streamlined to improve the overall user and pharmacy experience.”

Standardizing workflows and ensuring training and communications to pharmacists about the latest updates will discourage use of work-arounds, Sicilia said, and “help with improving morale and satisfaction with the system.” During a visit in early February by VA and the Oracle team to the Lovell Federal Health Care Center in North Chicago, “feedback from pharmacists was positive about the training and readiness for using the new pharmacy system.”

The backlog, at least, may be resolved. Sicilia said on average more than 215,000 outpatient prescriptions are being filled each month. “The current live sites do not have a backlog in filling prescriptions. Recent data from this month show that three of the five live sites have zero prescriptions waiting to be processed that are older than seven days. The two other live sites have an average of two prescriptions older than seven days,” he said.

Although Oracle Health has since resolved some of the identified issues, the OIG is concerned that the new EHR will continue to be deployed at medical facilities despite “myriad” as-yet unresolved issues related to inaccurate medication ordering, reconciliation, and dispensing. The VHA has paused Cerner deployments multiple times.

“It is unclear whether identified problems are being adequately resolved before additional deployments,” Case said. “There is also the question of whether there is sufficient transparency and communication among EHRM-IO, VHA and facility leaders, VA leaders, and Oracle Health needed for quality control and critical coordination. Trust in VA is also dependent on patients being fully and quickly advised when issues affecting them are identified and addressed. As VA moves toward its deployment next month at a complex facility jointly operated with the Department of Defense, transparency, communication, and program management will be essential to getting it right. Failures in these areas risk cascading problems.”

Competency in paracentesis is an important procedural skill for medical practitioners caring for patients with decompensated liver cirrhosis. Paracentesis is performed to drain ascitic fluid for both diagnosis and/or therapeutic purposes.¹ While this procedure can be performed without the use of ultrasound, it is preferable to use ultrasound to identify an area of fluid that is away from dangerous anatomy including bowel loops, the liver, and spleen. After prepping the area, lidocaine is administered locally. A catheter is then inserted until fluid begins flowing freely. The catheter is connected to a suction canister or collection kit, and the patient is monitored until the flow ceases. Samples can be sent for analysis to determine the etiology of ascites, identify concerns for infection, and more.

Paracentesis is a very common procedure. Barsuk and colleagues noted that between 2010 and 2012, 97,577 procedures were performed across 120 academic medical centers and 290 affiliated hospitals.² Patients undergo paracentesis in a variety of settings including the emergency department, inpatient hospitalizations, and clinics. Some patients may require only 1 paracentesis procedure while others may require it regularly.

Due to the rising need for paracentesis in the Central Texas Veterans Affairs Hospital (CTVAH) in Temple, a paracentesis clinic was started in February 2018. The goal of the paracentesis clinic was multifocal—to reduce hospital admissions, improve access to regularly scheduled procedures, decrease wait times, and increase patient satisfaction.³ Through the CTVAH affiliation with the Texas A&M internal medicine residency program, the paracentesis clinic started involving and training residents on this procedure. Up to 3 residents are on weekly rotation and can perform up to 6 paracentesis procedures in a week. The purpose of this article was to evaluate resident competency in paracentesis after completion of the paracentesis clinic.

Methods

The paracentesis clinic schedules up to 3 patients on Tuesdays and Thursdays between 8 am and noon. All the necessary equipment is readily available and includes the paracentesis kit, lidocaine, sterile gloves, ultrasound, and albumin if needed. Because this procedure is performed at the hospital, direct access to the emergency department is available. Residents are scheduled weekly. Up to 2 residents are scheduled for the paracentesis clinic during their dedicated clinic week. They are expected to practice obtaining consent, performing the procedure, and documenting the encounter under staff supervision. Additionally, 1 or 2 residents participate in the paracentesis clinic as part of an ultrasound elective twice per year. In this elective, they practice ultrasound skills using a simulation and translate that information in the paracentesis clinic while identifying anatomy and performing the paracentesis procedure under staff supervision.

A survey was sent via email to all categorical internal medicine residents across all 3 program years at the time of data collection. Competency for paracentesis sign-off was defined as completing and logging 5 procedures supervised by a competent physician who confirmed that all portions of the procedure were performed correctly. Residents were also asked to answer questions on a scale from 1 to 10, with 1 representing no confidence and 10 representing strong confidence to practice independently (Table).

We also evaluated the number of procedures performed by internal medicine residents 3 years before the clinic was started in 2015 up to the completion of 2022. The numbers were obtained by examining procedural log data for each year for all internal medicine residents.

Results

Thirty-three residents completed the survey: 10 first-year internal medicine residents (PGY1), 12 second-year residents (PGY2), and 11 third-year residents (PGY3). The mean participation was 4.8 paracentesis sessions per person for the duration of the study. The range of paracentesis procedures performed varied based on PGY year: PGY1s performed 1 to > 10 procedures, PGY2s performed 2 to > 10 procedures, and PGY3s performed 5 to > 10 procedures. Thirty-six percent of residents completed > 10 procedures in the paracentesis clinic; 82% of PGY3s had completed > 10 procedures by December of their third year. Twenty-six residents (79%) were credentialed to perform paracentesis procedures independently after performing > 5 procedures, and 7 residents were not yet cleared for procedural independence.

In the survey, residents rated their comfort with performing paracentesis procedures independently at a mean of 7.9. The mean comfort reported by PGY1s was 7.2, PGY2s was 7.3, and PGY3s was 9.3. Residents also rated their opinion on whether or not the paracentesis clinic adequately prepared them for paracentesis procedural independence; the mean was 8.9 across all residents.

The total number of procedures performed by residents at CTVAH also increased. Starting in 2015, 3 years before the clinic was started, 38 procedures were performed by internal medicine residents, followed by 72 procedures in 2016; 76 in 2017; 58 in 2018; 94 in 2019; 88 in 2020; 136 in 2021; and 188 in 2022.

Paracentesis is a simple but invasive procedure to relieve ascites, often relieving patients’ symptoms, preventing hospital admission, and increasing patient satisfaction.⁴ The CTVAH does not have the capacity to perform outpatient paracentesis effectively in its emergency or radiology departments. Furthermore, the use of the emergency or radiology departments for routine paracentesis may not be feasible due to the acuity of care being provided, as these procedures can be time consuming and can draw away critical resources and time from patients that need emergent care. The paracentesis clinic was then formed to provide veterans access to the procedural care they need, while also preparing residents to ably and confidently perform the procedure independently.

Based on our study, most residents were cleared to independently perform paracentesis procedures across all 3 years, with 79% of residents having completed the required 5 supervised procedures to independently practice. A study assessing unsupervised practice standards showed that paracentesis skill declines as soon as 3 months after training. However, retraining was shown to potentially interrupt this skill decline.⁵ Studies have shown that procedure-driven electives or services significantly improved paracentesis certification rates and total logged procedures, with minimal funding or scheduling changes required.⁶ Our clinic showed a significant increase in the number of procedures logged starting with the minimum of 38 procedures in 2015 and ending with 188 procedures logged at the end of 2022.

By allowing residents to routinely return to the paracentesis clinic across all 3 years, residents were more likely to feel comfortable independently performing the procedure, with residents reporting a mean comfort score of 7.9. The spaced repetition and ability to work with the clinic during elective time allows regular opportunities to undergo supervised training in a controlled environment and created scheduled retraining opportunities. Future studies should evaluate residents prior to each paracentesis clinic to ascertain if skill decline is occurring at a slower rate.

The inpatient effect of the clinic is also multifocal. Pham and colleagues showed that integrating paracentesis into timely training can reduce paracentesis delay and delays in care.⁷ By increasing the volume of procedures each resident performs and creating a sense of confidence amongst residents, the clinic increases the number of residents able and willing to perform inpatient procedures, thus reducing the number of unnecessary consultations and hospital resources. One of the reasons the paracentesis clinic was started was to allow patients to have scheduled times to remove fluid from their abdomen, thus cutting down on emergency department procedures and unnecessary admissions. Additionally, the benefits of early paracentesis procedural performance by residents and internal medicine physicians have been demonstrated in the literature. A study by Gaetano and colleagues noted that patients undergoing early paracentesis had reduced mortality of 5.5% vs 7.5% in those undergoing late paracentesis.⁸ This study also showed the in-hospital mortality rate was decreased with paracentesis (6.3%) vs without paracentesis (8.9%).⁸ By offering residents a chance to participate in the clinic, we have shown that regular opportunities to perform paracentesis may increase the number of physicians capable of independently practicing, improve procedural competency, and improve patient access to this procedure.

Limitations

Our study was not free of bias and has potential weaknesses. The survey was sent to all current residents who have participated in the paracentesis clinic, but not every resident filled out the survey (55% of all residents across 3 years completed the survey, 68.7% who had done clinic that year completed the survey). There is a possibility that those not signed off avoided doing the survey, but we are unable to confirm this. The survey also depended on resident recall of the number of paracenteses completed or looking at their procedure log. It is possible that some procedures were not documented, changing the true number. Additionally, rating comfortability with procedures is subjective, which may also create a source of potential weakness. Future projects should include a baseline survey for residents, followed by a repeat survey a year later to show changes from baseline competency.

Conclusions

A dedicated paracentesis clinic with internal medicine resident involvement may increase resident paracentesis procedural independence, the number of procedures available and performed, and procedural comfort level.

Competency in paracentesis is an important procedural skill for medical practitioners caring for patients with decompensated liver cirrhosis. Paracentesis is performed to drain ascitic fluid for both diagnosis and/or therapeutic purposes.¹ While this procedure can be performed without the use of ultrasound, it is preferable to use ultrasound to identify an area of fluid that is away from dangerous anatomy including bowel loops, the liver, and spleen. After prepping the area, lidocaine is administered locally. A catheter is then inserted until fluid begins flowing freely. The catheter is connected to a suction canister or collection kit, and the patient is monitored until the flow ceases. Samples can be sent for analysis to determine the etiology of ascites, identify concerns for infection, and more.

Paracentesis is a very common procedure. Barsuk and colleagues noted that between 2010 and 2012, 97,577 procedures were performed across 120 academic medical centers and 290 affiliated hospitals.² Patients undergo paracentesis in a variety of settings including the emergency department, inpatient hospitalizations, and clinics. Some patients may require only 1 paracentesis procedure while others may require it regularly.

Due to the rising need for paracentesis in the Central Texas Veterans Affairs Hospital (CTVAH) in Temple, a paracentesis clinic was started in February 2018. The goal of the paracentesis clinic was multifocal—to reduce hospital admissions, improve access to regularly scheduled procedures, decrease wait times, and increase patient satisfaction.³ Through the CTVAH affiliation with the Texas A&M internal medicine residency program, the paracentesis clinic started involving and training residents on this procedure. Up to 3 residents are on weekly rotation and can perform up to 6 paracentesis procedures in a week. The purpose of this article was to evaluate resident competency in paracentesis after completion of the paracentesis clinic.

Methods

The paracentesis clinic schedules up to 3 patients on Tuesdays and Thursdays between 8 am and noon. All the necessary equipment is readily available and includes the paracentesis kit, lidocaine, sterile gloves, ultrasound, and albumin if needed. Because this procedure is performed at the hospital, direct access to the emergency department is available. Residents are scheduled weekly. Up to 2 residents are scheduled for the paracentesis clinic during their dedicated clinic week. They are expected to practice obtaining consent, performing the procedure, and documenting the encounter under staff supervision. Additionally, 1 or 2 residents participate in the paracentesis clinic as part of an ultrasound elective twice per year. In this elective, they practice ultrasound skills using a simulation and translate that information in the paracentesis clinic while identifying anatomy and performing the paracentesis procedure under staff supervision.

A survey was sent via email to all categorical internal medicine residents across all 3 program years at the time of data collection. Competency for paracentesis sign-off was defined as completing and logging 5 procedures supervised by a competent physician who confirmed that all portions of the procedure were performed correctly. Residents were also asked to answer questions on a scale from 1 to 10, with 1 representing no confidence and 10 representing strong confidence to practice independently (Table).

We also evaluated the number of procedures performed by internal medicine residents 3 years before the clinic was started in 2015 up to the completion of 2022. The numbers were obtained by examining procedural log data for each year for all internal medicine residents.

Results

Thirty-three residents completed the survey: 10 first-year internal medicine residents (PGY1), 12 second-year residents (PGY2), and 11 third-year residents (PGY3). The mean participation was 4.8 paracentesis sessions per person for the duration of the study. The range of paracentesis procedures performed varied based on PGY year: PGY1s performed 1 to > 10 procedures, PGY2s performed 2 to > 10 procedures, and PGY3s performed 5 to > 10 procedures. Thirty-six percent of residents completed > 10 procedures in the paracentesis clinic; 82% of PGY3s had completed > 10 procedures by December of their third year. Twenty-six residents (79%) were credentialed to perform paracentesis procedures independently after performing > 5 procedures, and 7 residents were not yet cleared for procedural independence.

In the survey, residents rated their comfort with performing paracentesis procedures independently at a mean of 7.9. The mean comfort reported by PGY1s was 7.2, PGY2s was 7.3, and PGY3s was 9.3. Residents also rated their opinion on whether or not the paracentesis clinic adequately prepared them for paracentesis procedural independence; the mean was 8.9 across all residents.

The total number of procedures performed by residents at CTVAH also increased. Starting in 2015, 3 years before the clinic was started, 38 procedures were performed by internal medicine residents, followed by 72 procedures in 2016; 76 in 2017; 58 in 2018; 94 in 2019; 88 in 2020; 136 in 2021; and 188 in 2022.

Paracentesis is a simple but invasive procedure to relieve ascites, often relieving patients’ symptoms, preventing hospital admission, and increasing patient satisfaction.⁴ The CTVAH does not have the capacity to perform outpatient paracentesis effectively in its emergency or radiology departments. Furthermore, the use of the emergency or radiology departments for routine paracentesis may not be feasible due to the acuity of care being provided, as these procedures can be time consuming and can draw away critical resources and time from patients that need emergent care. The paracentesis clinic was then formed to provide veterans access to the procedural care they need, while also preparing residents to ably and confidently perform the procedure independently.

Based on our study, most residents were cleared to independently perform paracentesis procedures across all 3 years, with 79% of residents having completed the required 5 supervised procedures to independently practice. A study assessing unsupervised practice standards showed that paracentesis skill declines as soon as 3 months after training. However, retraining was shown to potentially interrupt this skill decline.⁵ Studies have shown that procedure-driven electives or services significantly improved paracentesis certification rates and total logged procedures, with minimal funding or scheduling changes required.⁶ Our clinic showed a significant increase in the number of procedures logged starting with the minimum of 38 procedures in 2015 and ending with 188 procedures logged at the end of 2022.

By allowing residents to routinely return to the paracentesis clinic across all 3 years, residents were more likely to feel comfortable independently performing the procedure, with residents reporting a mean comfort score of 7.9. The spaced repetition and ability to work with the clinic during elective time allows regular opportunities to undergo supervised training in a controlled environment and created scheduled retraining opportunities. Future studies should evaluate residents prior to each paracentesis clinic to ascertain if skill decline is occurring at a slower rate.

The inpatient effect of the clinic is also multifocal. Pham and colleagues showed that integrating paracentesis into timely training can reduce paracentesis delay and delays in care.⁷ By increasing the volume of procedures each resident performs and creating a sense of confidence amongst residents, the clinic increases the number of residents able and willing to perform inpatient procedures, thus reducing the number of unnecessary consultations and hospital resources. One of the reasons the paracentesis clinic was started was to allow patients to have scheduled times to remove fluid from their abdomen, thus cutting down on emergency department procedures and unnecessary admissions. Additionally, the benefits of early paracentesis procedural performance by residents and internal medicine physicians have been demonstrated in the literature. A study by Gaetano and colleagues noted that patients undergoing early paracentesis had reduced mortality of 5.5% vs 7.5% in those undergoing late paracentesis.⁸ This study also showed the in-hospital mortality rate was decreased with paracentesis (6.3%) vs without paracentesis (8.9%).⁸ By offering residents a chance to participate in the clinic, we have shown that regular opportunities to perform paracentesis may increase the number of physicians capable of independently practicing, improve procedural competency, and improve patient access to this procedure.

Limitations

Our study was not free of bias and has potential weaknesses. The survey was sent to all current residents who have participated in the paracentesis clinic, but not every resident filled out the survey (55% of all residents across 3 years completed the survey, 68.7% who had done clinic that year completed the survey). There is a possibility that those not signed off avoided doing the survey, but we are unable to confirm this. The survey also depended on resident recall of the number of paracenteses completed or looking at their procedure log. It is possible that some procedures were not documented, changing the true number. Additionally, rating comfortability with procedures is subjective, which may also create a source of potential weakness. Future projects should include a baseline survey for residents, followed by a repeat survey a year later to show changes from baseline competency.

Conclusions

A dedicated paracentesis clinic with internal medicine resident involvement may increase resident paracentesis procedural independence, the number of procedures available and performed, and procedural comfort level.

Competency in paracentesis is an important procedural skill for medical practitioners caring for patients with decompensated liver cirrhosis. Paracentesis is performed to drain ascitic fluid for both diagnosis and/or therapeutic purposes.¹ While this procedure can be performed without the use of ultrasound, it is preferable to use ultrasound to identify an area of fluid that is away from dangerous anatomy including bowel loops, the liver, and spleen. After prepping the area, lidocaine is administered locally. A catheter is then inserted until fluid begins flowing freely. The catheter is connected to a suction canister or collection kit, and the patient is monitored until the flow ceases. Samples can be sent for analysis to determine the etiology of ascites, identify concerns for infection, and more.

Paracentesis is a very common procedure. Barsuk and colleagues noted that between 2010 and 2012, 97,577 procedures were performed across 120 academic medical centers and 290 affiliated hospitals.² Patients undergo paracentesis in a variety of settings including the emergency department, inpatient hospitalizations, and clinics. Some patients may require only 1 paracentesis procedure while others may require it regularly.

Due to the rising need for paracentesis in the Central Texas Veterans Affairs Hospital (CTVAH) in Temple, a paracentesis clinic was started in February 2018. The goal of the paracentesis clinic was multifocal—to reduce hospital admissions, improve access to regularly scheduled procedures, decrease wait times, and increase patient satisfaction.³ Through the CTVAH affiliation with the Texas A&M internal medicine residency program, the paracentesis clinic started involving and training residents on this procedure. Up to 3 residents are on weekly rotation and can perform up to 6 paracentesis procedures in a week. The purpose of this article was to evaluate resident competency in paracentesis after completion of the paracentesis clinic.

Methods

The paracentesis clinic schedules up to 3 patients on Tuesdays and Thursdays between 8 am and noon. All the necessary equipment is readily available and includes the paracentesis kit, lidocaine, sterile gloves, ultrasound, and albumin if needed. Because this procedure is performed at the hospital, direct access to the emergency department is available. Residents are scheduled weekly. Up to 2 residents are scheduled for the paracentesis clinic during their dedicated clinic week. They are expected to practice obtaining consent, performing the procedure, and documenting the encounter under staff supervision. Additionally, 1 or 2 residents participate in the paracentesis clinic as part of an ultrasound elective twice per year. In this elective, they practice ultrasound skills using a simulation and translate that information in the paracentesis clinic while identifying anatomy and performing the paracentesis procedure under staff supervision.

A survey was sent via email to all categorical internal medicine residents across all 3 program years at the time of data collection. Competency for paracentesis sign-off was defined as completing and logging 5 procedures supervised by a competent physician who confirmed that all portions of the procedure were performed correctly. Residents were also asked to answer questions on a scale from 1 to 10, with 1 representing no confidence and 10 representing strong confidence to practice independently (Table).

We also evaluated the number of procedures performed by internal medicine residents 3 years before the clinic was started in 2015 up to the completion of 2022. The numbers were obtained by examining procedural log data for each year for all internal medicine residents.

Results

Thirty-three residents completed the survey: 10 first-year internal medicine residents (PGY1), 12 second-year residents (PGY2), and 11 third-year residents (PGY3). The mean participation was 4.8 paracentesis sessions per person for the duration of the study. The range of paracentesis procedures performed varied based on PGY year: PGY1s performed 1 to > 10 procedures, PGY2s performed 2 to > 10 procedures, and PGY3s performed 5 to > 10 procedures. Thirty-six percent of residents completed > 10 procedures in the paracentesis clinic; 82% of PGY3s had completed > 10 procedures by December of their third year. Twenty-six residents (79%) were credentialed to perform paracentesis procedures independently after performing > 5 procedures, and 7 residents were not yet cleared for procedural independence.

In the survey, residents rated their comfort with performing paracentesis procedures independently at a mean of 7.9. The mean comfort reported by PGY1s was 7.2, PGY2s was 7.3, and PGY3s was 9.3. Residents also rated their opinion on whether or not the paracentesis clinic adequately prepared them for paracentesis procedural independence; the mean was 8.9 across all residents.

The total number of procedures performed by residents at CTVAH also increased. Starting in 2015, 3 years before the clinic was started, 38 procedures were performed by internal medicine residents, followed by 72 procedures in 2016; 76 in 2017; 58 in 2018; 94 in 2019; 88 in 2020; 136 in 2021; and 188 in 2022.

Paracentesis is a simple but invasive procedure to relieve ascites, often relieving patients’ symptoms, preventing hospital admission, and increasing patient satisfaction.⁴ The CTVAH does not have the capacity to perform outpatient paracentesis effectively in its emergency or radiology departments. Furthermore, the use of the emergency or radiology departments for routine paracentesis may not be feasible due to the acuity of care being provided, as these procedures can be time consuming and can draw away critical resources and time from patients that need emergent care. The paracentesis clinic was then formed to provide veterans access to the procedural care they need, while also preparing residents to ably and confidently perform the procedure independently.

Based on our study, most residents were cleared to independently perform paracentesis procedures across all 3 years, with 79% of residents having completed the required 5 supervised procedures to independently practice. A study assessing unsupervised practice standards showed that paracentesis skill declines as soon as 3 months after training. However, retraining was shown to potentially interrupt this skill decline.⁵ Studies have shown that procedure-driven electives or services significantly improved paracentesis certification rates and total logged procedures, with minimal funding or scheduling changes required.⁶ Our clinic showed a significant increase in the number of procedures logged starting with the minimum of 38 procedures in 2015 and ending with 188 procedures logged at the end of 2022.

By allowing residents to routinely return to the paracentesis clinic across all 3 years, residents were more likely to feel comfortable independently performing the procedure, with residents reporting a mean comfort score of 7.9. The spaced repetition and ability to work with the clinic during elective time allows regular opportunities to undergo supervised training in a controlled environment and created scheduled retraining opportunities. Future studies should evaluate residents prior to each paracentesis clinic to ascertain if skill decline is occurring at a slower rate.

The inpatient effect of the clinic is also multifocal. Pham and colleagues showed that integrating paracentesis into timely training can reduce paracentesis delay and delays in care.⁷ By increasing the volume of procedures each resident performs and creating a sense of confidence amongst residents, the clinic increases the number of residents able and willing to perform inpatient procedures, thus reducing the number of unnecessary consultations and hospital resources. One of the reasons the paracentesis clinic was started was to allow patients to have scheduled times to remove fluid from their abdomen, thus cutting down on emergency department procedures and unnecessary admissions. Additionally, the benefits of early paracentesis procedural performance by residents and internal medicine physicians have been demonstrated in the literature. A study by Gaetano and colleagues noted that patients undergoing early paracentesis had reduced mortality of 5.5% vs 7.5% in those undergoing late paracentesis.⁸ This study also showed the in-hospital mortality rate was decreased with paracentesis (6.3%) vs without paracentesis (8.9%).⁸ By offering residents a chance to participate in the clinic, we have shown that regular opportunities to perform paracentesis may increase the number of physicians capable of independently practicing, improve procedural competency, and improve patient access to this procedure.

Limitations

Our study was not free of bias and has potential weaknesses. The survey was sent to all current residents who have participated in the paracentesis clinic, but not every resident filled out the survey (55% of all residents across 3 years completed the survey, 68.7% who had done clinic that year completed the survey). There is a possibility that those not signed off avoided doing the survey, but we are unable to confirm this. The survey also depended on resident recall of the number of paracenteses completed or looking at their procedure log. It is possible that some procedures were not documented, changing the true number. Additionally, rating comfortability with procedures is subjective, which may also create a source of potential weakness. Future projects should include a baseline survey for residents, followed by a repeat survey a year later to show changes from baseline competency.

Conclusions

A dedicated paracentesis clinic with internal medicine resident involvement may increase resident paracentesis procedural independence, the number of procedures available and performed, and procedural comfort level.

A duodenocaval fistula (DCF) is seen when a connection exists between the duodenum and the inferior vena cava. It is a rare entity that is commonly missed and presents a diagnostic challenge due to its nonspecific presenting symptoms.^1,2 Patients commonly present with gastrointestinal (GI) bleeding or sepsis. Here we present a case of a 37-year-old man who presented to the hospital for a workup related to melena but went into cardiac arrest prior to an esophagogastroduodenoscopy. Unfortunately, on autopsy, the patient was found to have a DCF. We highlight the diagnostic challenge associated with DCF and how in this case the presentation was confused by a diagnosis of possible transfusion-related acute lung injury (TRALI). To the best of our knowledge, this is also the first description of a case of DCF associated with food embolism to the lungs causing respiratory failure.

Case Presentation

A 37-year-old man with a history significant for bulimia presented to the hospital with a 3-day history of melena and reports of dizziness. The patient did not report being on any prescribed medications but noted that he took 4 aspirin daily to “calm his nerves.” The rest of the patient’s history was unremarkable aside from a reported history of induced emesis 3 to 4 times per week for an extended period up until 2 weeks before admission.

On admission, his vital signs demonstrated tachycardia and orthostatic hypotension. Pertinent findings on physical examination were skin pallor, a normal lung examination, mild epigastric tenderness, and guaiac-positive stools. He was alert and oriented to person, place, and time with no focal deficits. His admission laboratory tests were notable for a hemoglobin (Hb) level of 4.6 g/dL (reference range, 14-17.9), a white blood cell count of 13.5 K/cm (reference range, 4.5-11), an international normalized ratio of 1.21, a blood urea nitrogen of 61 mg/dL (reference range, 10-20), and a creatinine of 2.3 mg/dL (reference range, 0.8-1.4). The patient was placed on 2 L of oxygen via nasal cannula for comfort rather than true hypoxia. A chest X-ray on admission was negative with no signs of infiltrate, edema, or widened mediastinum. An abdominal X-ray was significant for a dilated stomach consistent with bulimia with no abdominal free air or signs of obstruction. The case was discussed with the gastroenterology service who felt that the patient needed to be more hemodynamically stable before pursuing endoscopic evaluation.

He was admitted to the intensive care unit and give a transfusion of 4 units of fresh frozen plasma and 2 packed red blood cells (PRBCs) without any issues. During the infusion of a third PRBC, he developed chills, tachycardia, and hypertension with accompanying respiratory distress characterized by wheezing, decreased breath sounds bilaterally, and a decrease in oxygen saturation to 70% on 2 L supplemental oxygen. He responded to treatment with meperidine, methylprednisolone sodium succinate, albuterol nebulizer, and acetaminophen. A new chest X-ray was read as “development of pulmonary edema vs bilateral pneumonitis.” A transfusion reaction was reported to the blood bank and a diagnosis of TRALI was considered. That evening, he completed a dose of platelets and another PRBC without difficulty after he was premedicated with meperidine, methylprednisolone sodium succinate, and acetaminophen. During the night, the patient spiked a temperature of 40.3 °C that was successfully treated with a cooling blanket and acetaminophen.

The following morning the patient was found to be tachypneic and tachycardic with his face mask off. His symptoms were corrected by replacing his face mask. He claimed he felt anxious about getting more transfusions and that he had breathing problems like this at home in the recent past. The patient requested an aspirin to calm his nerves. Over the course of the day, his Hb level dropped from 6.6 g/dL to 5.9 g/dL, and 2 washed leukopoor PRBCs were ordered.

The first unit was infused uneventfully, but after 125 cc of the second unit, the patient developed respiratory distress, rigors, and hypotension to 70/58 mm Hg despite premedication. He again was treated successfully with increased face mask support. A few rales were noted, but his fluid balance was even. A second transfusion reaction was filed with the blood bank and based on the 2 transfusion-associated events with no other clear explanation for his symptoms, the clinical team favored the TRALI diagnosis. However, the blood bank was suspicious this might not be TRALI as the previous night the patient had 2 episodes of respiratory distress with drops in oxygen saturation unassociated with transfusions. The patient was clinically stable for the remainder of the night.

Early the following morning the patient was scheduled for an esophagogastroduodenoscopy to evaluate for a source of his bleeding. At the beginning of the procedure, a unit of washed leukoreduced PRBCs was hung for a Hb level of 6.9 g/dL. No bleeding source was noted in the stomach, but as the endoscope was passed into the duodenum, and after an infusion of only 25 cc of RBCs, the patient became cyanotic and went into cardiac arrest. Despite advanced resuscitation efforts over 90 minutes, the patient could not be successfully resuscitated and died while in the endoscopy suite. A transfusion reaction workup was initiated but was unremarkable. The transfusion medicine staff was suspicious that something other than TRALI was the cause of the patient’s respiratory distress as he had respiratory distress remote to the transfusions and the unit was prepared correctly before administration. The patient’s family agreed to an autopsy.

Pathology

A full autopsy was performed 22 hours after the patient died. The lungs were congested and of increased weight: The right lung was 800 g, and the left was 750 g. The right lower lobe had a wedge-shaped infarction measuring 6 cm × 5 cm fed by a thrombosed vessel. Multiple small hemorrhagic wedge-shaped areas were noted in the left lung. An ulcer measuring 6 cm × 5 cm was noted just distal to the pylorus. At the base of this ulcer was a 1.5 cm × 0.5 cm tract that communicated with the inferior vena cava (Figure 1).

A postmortem blood culture was positive for Clostridium perfringens (C perfringens) and Candida albicans (C Albicans). Interestingly, one of the collected blood culture vials exploded en route to the laboratory, presumably due to the presence of many gas-forming C perfringens bacteria.

On microscopic examination of the autopsy samples, gram-positive rods were observed in the tissue of multiple organs, including the heart, lungs, liver, and kidneys (Figure 2).

Serology

Fourteen days after the patient’s death, both PRBC units infused during transfusion reactions were positive for granulocyte antibodies by immunofluorescence and agglutination techniques. Human leukocyte antigen antibody testing was also sent but was not found in either the donor or patient.

Discussion

Our case illustrates the unique and challenging diagnosis of DCF given the rarity of presentation and how quickly patients may clinically decompensate. After an extensive search of the medical literature, we were only able to identify about 40 previous cases of DCF, of which 37 were described in one review.¹ DCF, although rare, should be considered at risk for forming in the following settings: migrating inferior vena cava filter, right nephrectomy and radiotherapy, duodenal peptic ulcer, abdominal trauma, and oncologic settings involving metastatic malignancy requiring radiation and/or surgical grafting of the inferior vena cava.^1-4 When the diagnosis is considered, computed tomography (CT) is the best initial imaging modality as it allows for noninvasive evaluation of both the inferior vena cava and nonadjacent structures. A commonality of our case and those described in the literature is the diagnostic mystery and nonspecific symptoms patients present with, thus making CT an appropriate diagnostic modality. Endoscopy is useful for the further workup of GI bleeding and the diagnosis of peptic ulcer disease.⁵ In our case, given the patient’s autopsy findings and history of extensive nonsteroidal anti-inflammatory drug use, the duodenal peptic ulcer was likely the precipitating factor for his DCF.

The most challenging aspect in diagnosing DCF is that many times patients present with nonspecific symptoms, and given its rarity it is not something that is usually at the forefront of most differentials.² This diagnostic difficulty may elucidate why there is such a relatively high mortality rate—nearly 40%—associated with DCF and why many times accurate diagnosis is not made until autopsy.^1,3 The most common presenting manifestations are sepsis and/or GI bleeding; in less than half the cases described in the literature patients had both sepsis and GI bleeding. In our case, the patient had signs of melena but was not felt to be septic as his presenting signs were felt to be in the setting of blood loss and dehydration (given his history of bulimia), not an acute infectious source.

In retrospect, one of the more confounding aspects of this case is the clinical picture concerning for TRALI. The patient required supplemental oxygen throughout his hospitalization and decompensated while or after receiving a transfusion, thus having TRALI on the differential was not felt inappropriate at that time. However, this case also illustrates the power of an anchoring bias, and perhaps the clinical team anchored on the diagnosis of TRALI too quickly before considering other possible etiologies for the patient’s respiratory distress. TRALI can be one of the most challenging diagnoses to make in the field of transfusion medicine as there are no definitive diagnostic criteria.⁶ It is felt to be a clinical diagnosis of exclusion as there is no pathognomonic sign or diagnostic test to confirm it as the cause of the patient’s respiratory distress, though anti–human leukocyte antigen antibodies commonly are present.^6,7 Considering how quickly the patient decompensated on day 2 of hospitalization and the presence of C perfringens bacteremia, which carries a mortality rate of 27% to 44%, it is likely that further diagnostic workup would not have changed the clinical outcome.⁸

Conclusions

Our investigation reports a case of a DCF in the setting of significant duodenal peptic ulcer disease. We highlight the diagnostic challenge that this commonly lethal etiology presents. We believe ours is the first case in which it was confused for TRALI and associated with food embolism to the lungs causing hypoxic respiratory failure. We want to highlight that DCF, though rare, should be considered for patients who present with GI bleeding and hypoxic respiratory failure.

A duodenocaval fistula (DCF) is seen when a connection exists between the duodenum and the inferior vena cava. It is a rare entity that is commonly missed and presents a diagnostic challenge due to its nonspecific presenting symptoms.^1,2 Patients commonly present with gastrointestinal (GI) bleeding or sepsis. Here we present a case of a 37-year-old man who presented to the hospital for a workup related to melena but went into cardiac arrest prior to an esophagogastroduodenoscopy. Unfortunately, on autopsy, the patient was found to have a DCF. We highlight the diagnostic challenge associated with DCF and how in this case the presentation was confused by a diagnosis of possible transfusion-related acute lung injury (TRALI). To the best of our knowledge, this is also the first description of a case of DCF associated with food embolism to the lungs causing respiratory failure.

Case Presentation

A 37-year-old man with a history significant for bulimia presented to the hospital with a 3-day history of melena and reports of dizziness. The patient did not report being on any prescribed medications but noted that he took 4 aspirin daily to “calm his nerves.” The rest of the patient’s history was unremarkable aside from a reported history of induced emesis 3 to 4 times per week for an extended period up until 2 weeks before admission.

On admission, his vital signs demonstrated tachycardia and orthostatic hypotension. Pertinent findings on physical examination were skin pallor, a normal lung examination, mild epigastric tenderness, and guaiac-positive stools. He was alert and oriented to person, place, and time with no focal deficits. His admission laboratory tests were notable for a hemoglobin (Hb) level of 4.6 g/dL (reference range, 14-17.9), a white blood cell count of 13.5 K/cm (reference range, 4.5-11), an international normalized ratio of 1.21, a blood urea nitrogen of 61 mg/dL (reference range, 10-20), and a creatinine of 2.3 mg/dL (reference range, 0.8-1.4). The patient was placed on 2 L of oxygen via nasal cannula for comfort rather than true hypoxia. A chest X-ray on admission was negative with no signs of infiltrate, edema, or widened mediastinum. An abdominal X-ray was significant for a dilated stomach consistent with bulimia with no abdominal free air or signs of obstruction. The case was discussed with the gastroenterology service who felt that the patient needed to be more hemodynamically stable before pursuing endoscopic evaluation.

He was admitted to the intensive care unit and give a transfusion of 4 units of fresh frozen plasma and 2 packed red blood cells (PRBCs) without any issues. During the infusion of a third PRBC, he developed chills, tachycardia, and hypertension with accompanying respiratory distress characterized by wheezing, decreased breath sounds bilaterally, and a decrease in oxygen saturation to 70% on 2 L supplemental oxygen. He responded to treatment with meperidine, methylprednisolone sodium succinate, albuterol nebulizer, and acetaminophen. A new chest X-ray was read as “development of pulmonary edema vs bilateral pneumonitis.” A transfusion reaction was reported to the blood bank and a diagnosis of TRALI was considered. That evening, he completed a dose of platelets and another PRBC without difficulty after he was premedicated with meperidine, methylprednisolone sodium succinate, and acetaminophen. During the night, the patient spiked a temperature of 40.3 °C that was successfully treated with a cooling blanket and acetaminophen.

The following morning the patient was found to be tachypneic and tachycardic with his face mask off. His symptoms were corrected by replacing his face mask. He claimed he felt anxious about getting more transfusions and that he had breathing problems like this at home in the recent past. The patient requested an aspirin to calm his nerves. Over the course of the day, his Hb level dropped from 6.6 g/dL to 5.9 g/dL, and 2 washed leukopoor PRBCs were ordered.

The first unit was infused uneventfully, but after 125 cc of the second unit, the patient developed respiratory distress, rigors, and hypotension to 70/58 mm Hg despite premedication. He again was treated successfully with increased face mask support. A few rales were noted, but his fluid balance was even. A second transfusion reaction was filed with the blood bank and based on the 2 transfusion-associated events with no other clear explanation for his symptoms, the clinical team favored the TRALI diagnosis. However, the blood bank was suspicious this might not be TRALI as the previous night the patient had 2 episodes of respiratory distress with drops in oxygen saturation unassociated with transfusions. The patient was clinically stable for the remainder of the night.

Early the following morning the patient was scheduled for an esophagogastroduodenoscopy to evaluate for a source of his bleeding. At the beginning of the procedure, a unit of washed leukoreduced PRBCs was hung for a Hb level of 6.9 g/dL. No bleeding source was noted in the stomach, but as the endoscope was passed into the duodenum, and after an infusion of only 25 cc of RBCs, the patient became cyanotic and went into cardiac arrest. Despite advanced resuscitation efforts over 90 minutes, the patient could not be successfully resuscitated and died while in the endoscopy suite. A transfusion reaction workup was initiated but was unremarkable. The transfusion medicine staff was suspicious that something other than TRALI was the cause of the patient’s respiratory distress as he had respiratory distress remote to the transfusions and the unit was prepared correctly before administration. The patient’s family agreed to an autopsy.

Pathology

A full autopsy was performed 22 hours after the patient died. The lungs were congested and of increased weight: The right lung was 800 g, and the left was 750 g. The right lower lobe had a wedge-shaped infarction measuring 6 cm × 5 cm fed by a thrombosed vessel. Multiple small hemorrhagic wedge-shaped areas were noted in the left lung. An ulcer measuring 6 cm × 5 cm was noted just distal to the pylorus. At the base of this ulcer was a 1.5 cm × 0.5 cm tract that communicated with the inferior vena cava (Figure 1).

A postmortem blood culture was positive for Clostridium perfringens (C perfringens) and Candida albicans (C Albicans). Interestingly, one of the collected blood culture vials exploded en route to the laboratory, presumably due to the presence of many gas-forming C perfringens bacteria.

On microscopic examination of the autopsy samples, gram-positive rods were observed in the tissue of multiple organs, including the heart, lungs, liver, and kidneys (Figure 2).

Serology

Fourteen days after the patient’s death, both PRBC units infused during transfusion reactions were positive for granulocyte antibodies by immunofluorescence and agglutination techniques. Human leukocyte antigen antibody testing was also sent but was not found in either the donor or patient.

Discussion

Our case illustrates the unique and challenging diagnosis of DCF given the rarity of presentation and how quickly patients may clinically decompensate. After an extensive search of the medical literature, we were only able to identify about 40 previous cases of DCF, of which 37 were described in one review.¹ DCF, although rare, should be considered at risk for forming in the following settings: migrating inferior vena cava filter, right nephrectomy and radiotherapy, duodenal peptic ulcer, abdominal trauma, and oncologic settings involving metastatic malignancy requiring radiation and/or surgical grafting of the inferior vena cava.^1-4 When the diagnosis is considered, computed tomography (CT) is the best initial imaging modality as it allows for noninvasive evaluation of both the inferior vena cava and nonadjacent structures. A commonality of our case and those described in the literature is the diagnostic mystery and nonspecific symptoms patients present with, thus making CT an appropriate diagnostic modality. Endoscopy is useful for the further workup of GI bleeding and the diagnosis of peptic ulcer disease.⁵ In our case, given the patient’s autopsy findings and history of extensive nonsteroidal anti-inflammatory drug use, the duodenal peptic ulcer was likely the precipitating factor for his DCF.

The most challenging aspect in diagnosing DCF is that many times patients present with nonspecific symptoms, and given its rarity it is not something that is usually at the forefront of most differentials.² This diagnostic difficulty may elucidate why there is such a relatively high mortality rate—nearly 40%—associated with DCF and why many times accurate diagnosis is not made until autopsy.^1,3 The most common presenting manifestations are sepsis and/or GI bleeding; in less than half the cases described in the literature patients had both sepsis and GI bleeding. In our case, the patient had signs of melena but was not felt to be septic as his presenting signs were felt to be in the setting of blood loss and dehydration (given his history of bulimia), not an acute infectious source.

In retrospect, one of the more confounding aspects of this case is the clinical picture concerning for TRALI. The patient required supplemental oxygen throughout his hospitalization and decompensated while or after receiving a transfusion, thus having TRALI on the differential was not felt inappropriate at that time. However, this case also illustrates the power of an anchoring bias, and perhaps the clinical team anchored on the diagnosis of TRALI too quickly before considering other possible etiologies for the patient’s respiratory distress. TRALI can be one of the most challenging diagnoses to make in the field of transfusion medicine as there are no definitive diagnostic criteria.⁶ It is felt to be a clinical diagnosis of exclusion as there is no pathognomonic sign or diagnostic test to confirm it as the cause of the patient’s respiratory distress, though anti–human leukocyte antigen antibodies commonly are present.^6,7 Considering how quickly the patient decompensated on day 2 of hospitalization and the presence of C perfringens bacteremia, which carries a mortality rate of 27% to 44%, it is likely that further diagnostic workup would not have changed the clinical outcome.⁸

Conclusions

Our investigation reports a case of a DCF in the setting of significant duodenal peptic ulcer disease. We highlight the diagnostic challenge that this commonly lethal etiology presents. We believe ours is the first case in which it was confused for TRALI and associated with food embolism to the lungs causing hypoxic respiratory failure. We want to highlight that DCF, though rare, should be considered for patients who present with GI bleeding and hypoxic respiratory failure.

A duodenocaval fistula (DCF) is seen when a connection exists between the duodenum and the inferior vena cava. It is a rare entity that is commonly missed and presents a diagnostic challenge due to its nonspecific presenting symptoms.^1,2 Patients commonly present with gastrointestinal (GI) bleeding or sepsis. Here we present a case of a 37-year-old man who presented to the hospital for a workup related to melena but went into cardiac arrest prior to an esophagogastroduodenoscopy. Unfortunately, on autopsy, the patient was found to have a DCF. We highlight the diagnostic challenge associated with DCF and how in this case the presentation was confused by a diagnosis of possible transfusion-related acute lung injury (TRALI). To the best of our knowledge, this is also the first description of a case of DCF associated with food embolism to the lungs causing respiratory failure.

Case Presentation

A 37-year-old man with a history significant for bulimia presented to the hospital with a 3-day history of melena and reports of dizziness. The patient did not report being on any prescribed medications but noted that he took 4 aspirin daily to “calm his nerves.” The rest of the patient’s history was unremarkable aside from a reported history of induced emesis 3 to 4 times per week for an extended period up until 2 weeks before admission.

On admission, his vital signs demonstrated tachycardia and orthostatic hypotension. Pertinent findings on physical examination were skin pallor, a normal lung examination, mild epigastric tenderness, and guaiac-positive stools. He was alert and oriented to person, place, and time with no focal deficits. His admission laboratory tests were notable for a hemoglobin (Hb) level of 4.6 g/dL (reference range, 14-17.9), a white blood cell count of 13.5 K/cm (reference range, 4.5-11), an international normalized ratio of 1.21, a blood urea nitrogen of 61 mg/dL (reference range, 10-20), and a creatinine of 2.3 mg/dL (reference range, 0.8-1.4). The patient was placed on 2 L of oxygen via nasal cannula for comfort rather than true hypoxia. A chest X-ray on admission was negative with no signs of infiltrate, edema, or widened mediastinum. An abdominal X-ray was significant for a dilated stomach consistent with bulimia with no abdominal free air or signs of obstruction. The case was discussed with the gastroenterology service who felt that the patient needed to be more hemodynamically stable before pursuing endoscopic evaluation.

He was admitted to the intensive care unit and give a transfusion of 4 units of fresh frozen plasma and 2 packed red blood cells (PRBCs) without any issues. During the infusion of a third PRBC, he developed chills, tachycardia, and hypertension with accompanying respiratory distress characterized by wheezing, decreased breath sounds bilaterally, and a decrease in oxygen saturation to 70% on 2 L supplemental oxygen. He responded to treatment with meperidine, methylprednisolone sodium succinate, albuterol nebulizer, and acetaminophen. A new chest X-ray was read as “development of pulmonary edema vs bilateral pneumonitis.” A transfusion reaction was reported to the blood bank and a diagnosis of TRALI was considered. That evening, he completed a dose of platelets and another PRBC without difficulty after he was premedicated with meperidine, methylprednisolone sodium succinate, and acetaminophen. During the night, the patient spiked a temperature of 40.3 °C that was successfully treated with a cooling blanket and acetaminophen.

The following morning the patient was found to be tachypneic and tachycardic with his face mask off. His symptoms were corrected by replacing his face mask. He claimed he felt anxious about getting more transfusions and that he had breathing problems like this at home in the recent past. The patient requested an aspirin to calm his nerves. Over the course of the day, his Hb level dropped from 6.6 g/dL to 5.9 g/dL, and 2 washed leukopoor PRBCs were ordered.

The first unit was infused uneventfully, but after 125 cc of the second unit, the patient developed respiratory distress, rigors, and hypotension to 70/58 mm Hg despite premedication. He again was treated successfully with increased face mask support. A few rales were noted, but his fluid balance was even. A second transfusion reaction was filed with the blood bank and based on the 2 transfusion-associated events with no other clear explanation for his symptoms, the clinical team favored the TRALI diagnosis. However, the blood bank was suspicious this might not be TRALI as the previous night the patient had 2 episodes of respiratory distress with drops in oxygen saturation unassociated with transfusions. The patient was clinically stable for the remainder of the night.

Early the following morning the patient was scheduled for an esophagogastroduodenoscopy to evaluate for a source of his bleeding. At the beginning of the procedure, a unit of washed leukoreduced PRBCs was hung for a Hb level of 6.9 g/dL. No bleeding source was noted in the stomach, but as the endoscope was passed into the duodenum, and after an infusion of only 25 cc of RBCs, the patient became cyanotic and went into cardiac arrest. Despite advanced resuscitation efforts over 90 minutes, the patient could not be successfully resuscitated and died while in the endoscopy suite. A transfusion reaction workup was initiated but was unremarkable. The transfusion medicine staff was suspicious that something other than TRALI was the cause of the patient’s respiratory distress as he had respiratory distress remote to the transfusions and the unit was prepared correctly before administration. The patient’s family agreed to an autopsy.

Pathology

A full autopsy was performed 22 hours after the patient died. The lungs were congested and of increased weight: The right lung was 800 g, and the left was 750 g. The right lower lobe had a wedge-shaped infarction measuring 6 cm × 5 cm fed by a thrombosed vessel. Multiple small hemorrhagic wedge-shaped areas were noted in the left lung. An ulcer measuring 6 cm × 5 cm was noted just distal to the pylorus. At the base of this ulcer was a 1.5 cm × 0.5 cm tract that communicated with the inferior vena cava (Figure 1).

A postmortem blood culture was positive for Clostridium perfringens (C perfringens) and Candida albicans (C Albicans). Interestingly, one of the collected blood culture vials exploded en route to the laboratory, presumably due to the presence of many gas-forming C perfringens bacteria.

On microscopic examination of the autopsy samples, gram-positive rods were observed in the tissue of multiple organs, including the heart, lungs, liver, and kidneys (Figure 2).

Serology

Fourteen days after the patient’s death, both PRBC units infused during transfusion reactions were positive for granulocyte antibodies by immunofluorescence and agglutination techniques. Human leukocyte antigen antibody testing was also sent but was not found in either the donor or patient.

Discussion

Our case illustrates the unique and challenging diagnosis of DCF given the rarity of presentation and how quickly patients may clinically decompensate. After an extensive search of the medical literature, we were only able to identify about 40 previous cases of DCF, of which 37 were described in one review.¹ DCF, although rare, should be considered at risk for forming in the following settings: migrating inferior vena cava filter, right nephrectomy and radiotherapy, duodenal peptic ulcer, abdominal trauma, and oncologic settings involving metastatic malignancy requiring radiation and/or surgical grafting of the inferior vena cava.^1-4 When the diagnosis is considered, computed tomography (CT) is the best initial imaging modality as it allows for noninvasive evaluation of both the inferior vena cava and nonadjacent structures. A commonality of our case and those described in the literature is the diagnostic mystery and nonspecific symptoms patients present with, thus making CT an appropriate diagnostic modality. Endoscopy is useful for the further workup of GI bleeding and the diagnosis of peptic ulcer disease.⁵ In our case, given the patient’s autopsy findings and history of extensive nonsteroidal anti-inflammatory drug use, the duodenal peptic ulcer was likely the precipitating factor for his DCF.

The most challenging aspect in diagnosing DCF is that many times patients present with nonspecific symptoms, and given its rarity it is not something that is usually at the forefront of most differentials.² This diagnostic difficulty may elucidate why there is such a relatively high mortality rate—nearly 40%—associated with DCF and why many times accurate diagnosis is not made until autopsy.^1,3 The most common presenting manifestations are sepsis and/or GI bleeding; in less than half the cases described in the literature patients had both sepsis and GI bleeding. In our case, the patient had signs of melena but was not felt to be septic as his presenting signs were felt to be in the setting of blood loss and dehydration (given his history of bulimia), not an acute infectious source.

In retrospect, one of the more confounding aspects of this case is the clinical picture concerning for TRALI. The patient required supplemental oxygen throughout his hospitalization and decompensated while or after receiving a transfusion, thus having TRALI on the differential was not felt inappropriate at that time. However, this case also illustrates the power of an anchoring bias, and perhaps the clinical team anchored on the diagnosis of TRALI too quickly before considering other possible etiologies for the patient’s respiratory distress. TRALI can be one of the most challenging diagnoses to make in the field of transfusion medicine as there are no definitive diagnostic criteria.⁶ It is felt to be a clinical diagnosis of exclusion as there is no pathognomonic sign or diagnostic test to confirm it as the cause of the patient’s respiratory distress, though anti–human leukocyte antigen antibodies commonly are present.^6,7 Considering how quickly the patient decompensated on day 2 of hospitalization and the presence of C perfringens bacteremia, which carries a mortality rate of 27% to 44%, it is likely that further diagnostic workup would not have changed the clinical outcome.⁸

Conclusions

Our investigation reports a case of a DCF in the setting of significant duodenal peptic ulcer disease. We highlight the diagnostic challenge that this commonly lethal etiology presents. We believe ours is the first case in which it was confused for TRALI and associated with food embolism to the lungs causing hypoxic respiratory failure. We want to highlight that DCF, though rare, should be considered for patients who present with GI bleeding and hypoxic respiratory failure.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org