The frequency and severity of symptoms among older hospitalized patients with chronic illnesses can have a profound negative impact on their quality of life.1, 2 Nonetheless, research examining the prevalence and management of symptoms has focused predominantly on cancer patients.3 Few studies have included patients with other serious conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD),3, 4 which are very common and are major causes of morbidity and mortality in the United States.5 One longitudinal assessment of symptom severity among a group of community‐based older adults diagnosed with COPD and HF reported high rates of moderate‐to‐severe pain, dyspnea, and anxiety at baseline and follow‐up, as long as 22 months later.6 Persistent symptoms over time can have an adverse effect on an individual's physical and emotional well‐being, and highlight opportunities to improve care.3, 7 Understanding patterns of symptom change over time is a key first step in developing systems to improve quality of care for people with chronic illness.

Among hospitalized patients, pain, dyspnea, anxiety, and depression cause the greatest symptom burden, accounting for 67% of all symptoms classified as moderate to severe.8 While assessment and management of symptoms may be the reason for admission to the hospital and the focus of inpatient care, this focus may not persist after discharge, leaving patients with significant symptoms that can diminish quality of life and contribute to readmission.9 We studied a cohort of older inpatients with serious illness over time in order to determine the prevalence, severity, burden, and predictors of symptoms during the course of hospitalization and at 2 weeks after discharge.

METHODS

RESULTS

Frequency and Severity of Symptoms

On average, the postdischarge follow‐up assessment was undertaken 24 days (median = 21.0; SD: 17.9; range: 7140 days) after the baseline assessment and 20 days after discharge (median = 15; SD: 17.0; range: 4139). At baseline, a large proportion of participants reported symptoms at a moderate‐to‐severe level for pain (54%, n = 81), dyspnea (53%, n = 79), and anxiety (63%, n = 94). The majority of patients (64%, n = 96) reported having 2 or more symptoms at a moderate‐to‐severe level and one quarter (27%, n = 41) had 3 symptoms at a moderate‐to‐severe level. While the frequency of moderate‐to‐severe symptoms decreased at the 24‐hour hospital assessment (pain = 42%, dyspnea = 45%, anxiety = 55%) and again at 2‐week follow‐up (pain = 28%, dyspnea = 27%, anxiety = 25%), a substantial symptom burden persisted with 30% (n = 36) of patients having moderate‐to‐severe levels at 2‐week follow‐up. Overall there were no differences between primary diagnosis and the frequency of symptoms at baseline or 24‐hour hospital assessment (Figure 1). However at follow‐up, those diagnosed with COPD were more likely to report moderate/severe pain (54%; ² = 22.0; P < 0.001), dyspnea (45%; ² = 9.3; P = 0.05), and overall symptom burden (55%; ² = 25.9; P < 0.001) than those with cancer (pain = 22%, dyspnea = 16%, symptom burden = 16%) or HF (pain = 25%, dyspnea = 24%, symptom burden = 28%).

Figure 1

As symptom burden was our composite score for pain, dyspnea, and anxiety, we were interested in identifying variables in addition to primary diagnosis that might be associated with symptom burden at follow‐up. Bivariate analysis revealed that there was no significant association between symptom burden and age (² = 1.5; P = 0.5), gender (² = 1.3; P = 0.3), length of stay (² = 0.4; P = 0.8), and (IADL) level of independence (² = 0.3; P = 0.6). However, those with probable depression were more likely (² = 11.9; P = 0.001) to have a moderate/severe symptom burden (62%, n = 13), compared to those with no depression (24%, n = 23). After adjusting for severity of symptom burden at baseline, multivariate logistic regression revealed that primary diagnosis (P = 0.01) and probable depression (OR = 4.9; 95% CI = 1.6, 14.9; P = 0.005) were associated with symptom severity. Patients with COPD had greater odds (OR = 7.0; 95% CI = 1.9, 26.2; P = 0.002) of moderate/severe symptom burden than those with cancer, while those with HF did not (OR = 2.3; 95% CI = 0.7, 7.7; P = 0.16). There was significant interaction between primary diagnosis and depression (P = 0.2).

Primary Diagnosis, Symptom Burden, and Survival Time

A total of 75% of patients were identified by the National Death Index to have died between hospital discharge and December 2007, of which 47% had died within 12 months after discharge. KaplanMeier survival curves (Figure 2) revealed a significant difference (MantelCox: ² = 19.3; df = 1; P = 0.0001) in survival time, with patients diagnosed with COPD (median = 19.0 months; 95% CI = 6.5, 31.5) and HF (median = 20.0 months; 95% CI = 12.5, 27.5) having a longer survival than those with cancer (median = 8.0 months; 95% CI = 4.1, 11.9).

Figure 2

We also examined the relationship between symptom burden and survival time. KaplanMeier survival curves revealed no significant difference (MantelCox: ² = 0.2; P = 0.6) in the survival time of patients classified with a symptom burden of none/moderate (median = 15.0 months; 95% CI = 8.8, 21.2) or moderate/severe (median = 14.0 months; 95% CI = 2.6, 25.4).

DISCUSSION

In our sample of older inpatients diagnosed with cancer, HF, and COPD, a large proportion reported moderate‐to‐severe levels of pain, dyspnea, and anxiety at baseline and follow‐up. When combined, these levels represent a considerable symptom burden, with over three‐quarters of participants reporting 2 to 3 symptoms at a moderate/severe level at baseline. While symptom scores decreased at 24‐hours and 2‐week follow‐up, symptom burden remained high, with almost half of the participants reporting 23 symptoms at a moderate‐to‐severe level at 24‐hour assessment and a large minority reporting moderate‐to‐severe symptoms at follow‐up. A higher percentage of patients with COPD reported moderate‐to‐severe pain, dyspnea, and overall symptom burden at follow‐up than participants with cancer or HF who reported a similar symptom burden. We also found that patients with probable depression were more likely to have a significant symptom burden at follow‐up. These findings highlight the need to routinely assess and treat symptoms over time, including depression, and especially in patients with COPD. While we found that hospital care was seemingly effective in improving symptoms, they persist at distressing levels in many patients.

Few studies have assessed the severity of symptoms over time. One study that did, examined symptom severity among community‐based elders diagnosed with HF and COPD.6 At baseline, these participants had a lower prevalence of moderate‐to‐severe symptoms than the hospitalized patients enrolled in our study, a finding that would be anticipated, as they may not have been as ill. However, symptom severity persisted in the community‐based subjects and, in some cases, worsened over the 22‐month assessment period for pain (HF = 20% vs 42%; COPD = 27% vs 20%), dyspnea (HF = 19% vs 29%; COPD = 66% vs 76%), and anxiety (HF = 2% vs 12%; COPD = 32% vs 23%).6 In contrast, while our subjects with a primary diagnosis of HF and COPD had a higher prevalence of moderate‐to‐severe symptoms at baseline, they did experience an improvement in the severity of pain, dyspnea, and anxiety at the 2‐week follow‐up assessment. However, despite a decrease in the prevalence of moderate‐to‐severe symptoms from baseline to follow‐up, a high symptom burden persisted for many patients, particularly for those diagnosed with COPD and those with probable depression at baseline. The severity of a patient's symptoms can have a profound negative effect on health status and quality of life.14 Findings from these studies suggest that symptoms are currently not being adequately managed, and highlight an urgent need to develop coordinated strategies and systems that focus on improving the management of symptoms, including depression, over time.6

We also found that subjects recruited for this study had advanced disease, evidenced by the fact that nearly half died within 12 months. We did not use specific prognostic indices or severity of illness criteria for recruiting subjects and simply approached patients admitted with one of the target diagnoses. Our study suggests that targeting these patients for routine symptom assessment and management, including for palliative care, would be a reasonable approach given the high symptom burden and relatively high mortality at 1 year.

Interpretation of these findings should be mitigated by the following limitations. Because of our setting, our findings may not be generalizable to all patients with cancer, HF, and COPD. However, our subjects were admitted to general medical and cardiology services, and had common conditions, and therefore are likely similar to those presenting to other hospitals. We relied on self‐report measures to assess severity of symptoms. Patient self‐report, while potentially subject to imprecision due to poor recall and social demand biases, is considered the gold standard for symptom assessment.15 Finally, 2‐week follow‐up is relatively short, and it is possible that symptoms may have improved had we assessed them over a longer period. The longitudinal study of elders in the community that followed subjects over 22 months found that, for many patients, symptoms worsened over time and nearly half of our subjects died at 12 months, suggesting that longer follow‐up would have been unlikely to show improvement in symptoms.6

A significant minority of participants reported a substantial, persistent symptom burden, yet all symptoms assessed in our study are potentially modifiable. Recognizing and treating symptoms can be achieved through the use of targeted interventions.6 Because symptoms can occur in clusters, successful treatment of 1 symptom may also help to improve other symptoms.1 The large number of participants reporting moderate‐to‐severe levels of symptom burden at 2 weeks after discharge highlights an unmet need for improved symptom control in the outpatient setting. Unfortunately, while evidence exists for managing pain in patients with cancer, such evidence‐based practices are lacking for the management of pain and other symptoms in patients with HF and COPD. Some symptoms may require specific, disease‐oriented management. However, many symptoms may be due to common comorbidities, such as pain from degenerative joint disease, that may likely respond to proven treatments.16

Our study confirmed the significant burden of symptoms experienced by patients with serious illness and demonstrated that patients with COPD report as much symptom burden as patients with cancer and HF, if not more. While symptom severity improved over the course of the hospitalization and follow‐up, a large percentage of patients reported significant symptom burden at follow‐up. Depression was also common in these patients. Because these symptoms diminish quality of life, routine assessment and management of these symptoms is critical for improving the quality of care provided to these patients. Additional research on the best approaches to manage symptoms, including medications, interventions, and structures of care, could further improve care.

The frequency and severity of symptoms among older hospitalized patients with chronic illnesses can have a profound negative impact on their quality of life.1, 2 Nonetheless, research examining the prevalence and management of symptoms has focused predominantly on cancer patients.3 Few studies have included patients with other serious conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD),3, 4 which are very common and are major causes of morbidity and mortality in the United States.5 One longitudinal assessment of symptom severity among a group of community‐based older adults diagnosed with COPD and HF reported high rates of moderate‐to‐severe pain, dyspnea, and anxiety at baseline and follow‐up, as long as 22 months later.6 Persistent symptoms over time can have an adverse effect on an individual's physical and emotional well‐being, and highlight opportunities to improve care.3, 7 Understanding patterns of symptom change over time is a key first step in developing systems to improve quality of care for people with chronic illness.

Among hospitalized patients, pain, dyspnea, anxiety, and depression cause the greatest symptom burden, accounting for 67% of all symptoms classified as moderate to severe.8 While assessment and management of symptoms may be the reason for admission to the hospital and the focus of inpatient care, this focus may not persist after discharge, leaving patients with significant symptoms that can diminish quality of life and contribute to readmission.9 We studied a cohort of older inpatients with serious illness over time in order to determine the prevalence, severity, burden, and predictors of symptoms during the course of hospitalization and at 2 weeks after discharge.

METHODS

RESULTS

Frequency and Severity of Symptoms

On average, the postdischarge follow‐up assessment was undertaken 24 days (median = 21.0; SD: 17.9; range: 7140 days) after the baseline assessment and 20 days after discharge (median = 15; SD: 17.0; range: 4139). At baseline, a large proportion of participants reported symptoms at a moderate‐to‐severe level for pain (54%, n = 81), dyspnea (53%, n = 79), and anxiety (63%, n = 94). The majority of patients (64%, n = 96) reported having 2 or more symptoms at a moderate‐to‐severe level and one quarter (27%, n = 41) had 3 symptoms at a moderate‐to‐severe level. While the frequency of moderate‐to‐severe symptoms decreased at the 24‐hour hospital assessment (pain = 42%, dyspnea = 45%, anxiety = 55%) and again at 2‐week follow‐up (pain = 28%, dyspnea = 27%, anxiety = 25%), a substantial symptom burden persisted with 30% (n = 36) of patients having moderate‐to‐severe levels at 2‐week follow‐up. Overall there were no differences between primary diagnosis and the frequency of symptoms at baseline or 24‐hour hospital assessment (Figure 1). However at follow‐up, those diagnosed with COPD were more likely to report moderate/severe pain (54%; ² = 22.0; P < 0.001), dyspnea (45%; ² = 9.3; P = 0.05), and overall symptom burden (55%; ² = 25.9; P < 0.001) than those with cancer (pain = 22%, dyspnea = 16%, symptom burden = 16%) or HF (pain = 25%, dyspnea = 24%, symptom burden = 28%).

Figure 1

As symptom burden was our composite score for pain, dyspnea, and anxiety, we were interested in identifying variables in addition to primary diagnosis that might be associated with symptom burden at follow‐up. Bivariate analysis revealed that there was no significant association between symptom burden and age (² = 1.5; P = 0.5), gender (² = 1.3; P = 0.3), length of stay (² = 0.4; P = 0.8), and (IADL) level of independence (² = 0.3; P = 0.6). However, those with probable depression were more likely (² = 11.9; P = 0.001) to have a moderate/severe symptom burden (62%, n = 13), compared to those with no depression (24%, n = 23). After adjusting for severity of symptom burden at baseline, multivariate logistic regression revealed that primary diagnosis (P = 0.01) and probable depression (OR = 4.9; 95% CI = 1.6, 14.9; P = 0.005) were associated with symptom severity. Patients with COPD had greater odds (OR = 7.0; 95% CI = 1.9, 26.2; P = 0.002) of moderate/severe symptom burden than those with cancer, while those with HF did not (OR = 2.3; 95% CI = 0.7, 7.7; P = 0.16). There was significant interaction between primary diagnosis and depression (P = 0.2).

Primary Diagnosis, Symptom Burden, and Survival Time

A total of 75% of patients were identified by the National Death Index to have died between hospital discharge and December 2007, of which 47% had died within 12 months after discharge. KaplanMeier survival curves (Figure 2) revealed a significant difference (MantelCox: ² = 19.3; df = 1; P = 0.0001) in survival time, with patients diagnosed with COPD (median = 19.0 months; 95% CI = 6.5, 31.5) and HF (median = 20.0 months; 95% CI = 12.5, 27.5) having a longer survival than those with cancer (median = 8.0 months; 95% CI = 4.1, 11.9).

Figure 2

We also examined the relationship between symptom burden and survival time. KaplanMeier survival curves revealed no significant difference (MantelCox: ² = 0.2; P = 0.6) in the survival time of patients classified with a symptom burden of none/moderate (median = 15.0 months; 95% CI = 8.8, 21.2) or moderate/severe (median = 14.0 months; 95% CI = 2.6, 25.4).

DISCUSSION

In our sample of older inpatients diagnosed with cancer, HF, and COPD, a large proportion reported moderate‐to‐severe levels of pain, dyspnea, and anxiety at baseline and follow‐up. When combined, these levels represent a considerable symptom burden, with over three‐quarters of participants reporting 2 to 3 symptoms at a moderate/severe level at baseline. While symptom scores decreased at 24‐hours and 2‐week follow‐up, symptom burden remained high, with almost half of the participants reporting 23 symptoms at a moderate‐to‐severe level at 24‐hour assessment and a large minority reporting moderate‐to‐severe symptoms at follow‐up. A higher percentage of patients with COPD reported moderate‐to‐severe pain, dyspnea, and overall symptom burden at follow‐up than participants with cancer or HF who reported a similar symptom burden. We also found that patients with probable depression were more likely to have a significant symptom burden at follow‐up. These findings highlight the need to routinely assess and treat symptoms over time, including depression, and especially in patients with COPD. While we found that hospital care was seemingly effective in improving symptoms, they persist at distressing levels in many patients.

Few studies have assessed the severity of symptoms over time. One study that did, examined symptom severity among community‐based elders diagnosed with HF and COPD.6 At baseline, these participants had a lower prevalence of moderate‐to‐severe symptoms than the hospitalized patients enrolled in our study, a finding that would be anticipated, as they may not have been as ill. However, symptom severity persisted in the community‐based subjects and, in some cases, worsened over the 22‐month assessment period for pain (HF = 20% vs 42%; COPD = 27% vs 20%), dyspnea (HF = 19% vs 29%; COPD = 66% vs 76%), and anxiety (HF = 2% vs 12%; COPD = 32% vs 23%).6 In contrast, while our subjects with a primary diagnosis of HF and COPD had a higher prevalence of moderate‐to‐severe symptoms at baseline, they did experience an improvement in the severity of pain, dyspnea, and anxiety at the 2‐week follow‐up assessment. However, despite a decrease in the prevalence of moderate‐to‐severe symptoms from baseline to follow‐up, a high symptom burden persisted for many patients, particularly for those diagnosed with COPD and those with probable depression at baseline. The severity of a patient's symptoms can have a profound negative effect on health status and quality of life.14 Findings from these studies suggest that symptoms are currently not being adequately managed, and highlight an urgent need to develop coordinated strategies and systems that focus on improving the management of symptoms, including depression, over time.6

We also found that subjects recruited for this study had advanced disease, evidenced by the fact that nearly half died within 12 months. We did not use specific prognostic indices or severity of illness criteria for recruiting subjects and simply approached patients admitted with one of the target diagnoses. Our study suggests that targeting these patients for routine symptom assessment and management, including for palliative care, would be a reasonable approach given the high symptom burden and relatively high mortality at 1 year.

Interpretation of these findings should be mitigated by the following limitations. Because of our setting, our findings may not be generalizable to all patients with cancer, HF, and COPD. However, our subjects were admitted to general medical and cardiology services, and had common conditions, and therefore are likely similar to those presenting to other hospitals. We relied on self‐report measures to assess severity of symptoms. Patient self‐report, while potentially subject to imprecision due to poor recall and social demand biases, is considered the gold standard for symptom assessment.15 Finally, 2‐week follow‐up is relatively short, and it is possible that symptoms may have improved had we assessed them over a longer period. The longitudinal study of elders in the community that followed subjects over 22 months found that, for many patients, symptoms worsened over time and nearly half of our subjects died at 12 months, suggesting that longer follow‐up would have been unlikely to show improvement in symptoms.6

A significant minority of participants reported a substantial, persistent symptom burden, yet all symptoms assessed in our study are potentially modifiable. Recognizing and treating symptoms can be achieved through the use of targeted interventions.6 Because symptoms can occur in clusters, successful treatment of 1 symptom may also help to improve other symptoms.1 The large number of participants reporting moderate‐to‐severe levels of symptom burden at 2 weeks after discharge highlights an unmet need for improved symptom control in the outpatient setting. Unfortunately, while evidence exists for managing pain in patients with cancer, such evidence‐based practices are lacking for the management of pain and other symptoms in patients with HF and COPD. Some symptoms may require specific, disease‐oriented management. However, many symptoms may be due to common comorbidities, such as pain from degenerative joint disease, that may likely respond to proven treatments.16

Our study confirmed the significant burden of symptoms experienced by patients with serious illness and demonstrated that patients with COPD report as much symptom burden as patients with cancer and HF, if not more. While symptom severity improved over the course of the hospitalization and follow‐up, a large percentage of patients reported significant symptom burden at follow‐up. Depression was also common in these patients. Because these symptoms diminish quality of life, routine assessment and management of these symptoms is critical for improving the quality of care provided to these patients. Additional research on the best approaches to manage symptoms, including medications, interventions, and structures of care, could further improve care.

The frequency and severity of symptoms among older hospitalized patients with chronic illnesses can have a profound negative impact on their quality of life.1, 2 Nonetheless, research examining the prevalence and management of symptoms has focused predominantly on cancer patients.3 Few studies have included patients with other serious conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD),3, 4 which are very common and are major causes of morbidity and mortality in the United States.5 One longitudinal assessment of symptom severity among a group of community‐based older adults diagnosed with COPD and HF reported high rates of moderate‐to‐severe pain, dyspnea, and anxiety at baseline and follow‐up, as long as 22 months later.6 Persistent symptoms over time can have an adverse effect on an individual's physical and emotional well‐being, and highlight opportunities to improve care.3, 7 Understanding patterns of symptom change over time is a key first step in developing systems to improve quality of care for people with chronic illness.

Among hospitalized patients, pain, dyspnea, anxiety, and depression cause the greatest symptom burden, accounting for 67% of all symptoms classified as moderate to severe.8 While assessment and management of symptoms may be the reason for admission to the hospital and the focus of inpatient care, this focus may not persist after discharge, leaving patients with significant symptoms that can diminish quality of life and contribute to readmission.9 We studied a cohort of older inpatients with serious illness over time in order to determine the prevalence, severity, burden, and predictors of symptoms during the course of hospitalization and at 2 weeks after discharge.

METHODS

RESULTS

Frequency and Severity of Symptoms

On average, the postdischarge follow‐up assessment was undertaken 24 days (median = 21.0; SD: 17.9; range: 7140 days) after the baseline assessment and 20 days after discharge (median = 15; SD: 17.0; range: 4139). At baseline, a large proportion of participants reported symptoms at a moderate‐to‐severe level for pain (54%, n = 81), dyspnea (53%, n = 79), and anxiety (63%, n = 94). The majority of patients (64%, n = 96) reported having 2 or more symptoms at a moderate‐to‐severe level and one quarter (27%, n = 41) had 3 symptoms at a moderate‐to‐severe level. While the frequency of moderate‐to‐severe symptoms decreased at the 24‐hour hospital assessment (pain = 42%, dyspnea = 45%, anxiety = 55%) and again at 2‐week follow‐up (pain = 28%, dyspnea = 27%, anxiety = 25%), a substantial symptom burden persisted with 30% (n = 36) of patients having moderate‐to‐severe levels at 2‐week follow‐up. Overall there were no differences between primary diagnosis and the frequency of symptoms at baseline or 24‐hour hospital assessment (Figure 1). However at follow‐up, those diagnosed with COPD were more likely to report moderate/severe pain (54%; ² = 22.0; P < 0.001), dyspnea (45%; ² = 9.3; P = 0.05), and overall symptom burden (55%; ² = 25.9; P < 0.001) than those with cancer (pain = 22%, dyspnea = 16%, symptom burden = 16%) or HF (pain = 25%, dyspnea = 24%, symptom burden = 28%).

Figure 1

As symptom burden was our composite score for pain, dyspnea, and anxiety, we were interested in identifying variables in addition to primary diagnosis that might be associated with symptom burden at follow‐up. Bivariate analysis revealed that there was no significant association between symptom burden and age (² = 1.5; P = 0.5), gender (² = 1.3; P = 0.3), length of stay (² = 0.4; P = 0.8), and (IADL) level of independence (² = 0.3; P = 0.6). However, those with probable depression were more likely (² = 11.9; P = 0.001) to have a moderate/severe symptom burden (62%, n = 13), compared to those with no depression (24%, n = 23). After adjusting for severity of symptom burden at baseline, multivariate logistic regression revealed that primary diagnosis (P = 0.01) and probable depression (OR = 4.9; 95% CI = 1.6, 14.9; P = 0.005) were associated with symptom severity. Patients with COPD had greater odds (OR = 7.0; 95% CI = 1.9, 26.2; P = 0.002) of moderate/severe symptom burden than those with cancer, while those with HF did not (OR = 2.3; 95% CI = 0.7, 7.7; P = 0.16). There was significant interaction between primary diagnosis and depression (P = 0.2).

Primary Diagnosis, Symptom Burden, and Survival Time

A total of 75% of patients were identified by the National Death Index to have died between hospital discharge and December 2007, of which 47% had died within 12 months after discharge. KaplanMeier survival curves (Figure 2) revealed a significant difference (MantelCox: ² = 19.3; df = 1; P = 0.0001) in survival time, with patients diagnosed with COPD (median = 19.0 months; 95% CI = 6.5, 31.5) and HF (median = 20.0 months; 95% CI = 12.5, 27.5) having a longer survival than those with cancer (median = 8.0 months; 95% CI = 4.1, 11.9).

Figure 2

We also examined the relationship between symptom burden and survival time. KaplanMeier survival curves revealed no significant difference (MantelCox: ² = 0.2; P = 0.6) in the survival time of patients classified with a symptom burden of none/moderate (median = 15.0 months; 95% CI = 8.8, 21.2) or moderate/severe (median = 14.0 months; 95% CI = 2.6, 25.4).

DISCUSSION

In our sample of older inpatients diagnosed with cancer, HF, and COPD, a large proportion reported moderate‐to‐severe levels of pain, dyspnea, and anxiety at baseline and follow‐up. When combined, these levels represent a considerable symptom burden, with over three‐quarters of participants reporting 2 to 3 symptoms at a moderate/severe level at baseline. While symptom scores decreased at 24‐hours and 2‐week follow‐up, symptom burden remained high, with almost half of the participants reporting 23 symptoms at a moderate‐to‐severe level at 24‐hour assessment and a large minority reporting moderate‐to‐severe symptoms at follow‐up. A higher percentage of patients with COPD reported moderate‐to‐severe pain, dyspnea, and overall symptom burden at follow‐up than participants with cancer or HF who reported a similar symptom burden. We also found that patients with probable depression were more likely to have a significant symptom burden at follow‐up. These findings highlight the need to routinely assess and treat symptoms over time, including depression, and especially in patients with COPD. While we found that hospital care was seemingly effective in improving symptoms, they persist at distressing levels in many patients.

Few studies have assessed the severity of symptoms over time. One study that did, examined symptom severity among community‐based elders diagnosed with HF and COPD.6 At baseline, these participants had a lower prevalence of moderate‐to‐severe symptoms than the hospitalized patients enrolled in our study, a finding that would be anticipated, as they may not have been as ill. However, symptom severity persisted in the community‐based subjects and, in some cases, worsened over the 22‐month assessment period for pain (HF = 20% vs 42%; COPD = 27% vs 20%), dyspnea (HF = 19% vs 29%; COPD = 66% vs 76%), and anxiety (HF = 2% vs 12%; COPD = 32% vs 23%).6 In contrast, while our subjects with a primary diagnosis of HF and COPD had a higher prevalence of moderate‐to‐severe symptoms at baseline, they did experience an improvement in the severity of pain, dyspnea, and anxiety at the 2‐week follow‐up assessment. However, despite a decrease in the prevalence of moderate‐to‐severe symptoms from baseline to follow‐up, a high symptom burden persisted for many patients, particularly for those diagnosed with COPD and those with probable depression at baseline. The severity of a patient's symptoms can have a profound negative effect on health status and quality of life.14 Findings from these studies suggest that symptoms are currently not being adequately managed, and highlight an urgent need to develop coordinated strategies and systems that focus on improving the management of symptoms, including depression, over time.6

We also found that subjects recruited for this study had advanced disease, evidenced by the fact that nearly half died within 12 months. We did not use specific prognostic indices or severity of illness criteria for recruiting subjects and simply approached patients admitted with one of the target diagnoses. Our study suggests that targeting these patients for routine symptom assessment and management, including for palliative care, would be a reasonable approach given the high symptom burden and relatively high mortality at 1 year.

Interpretation of these findings should be mitigated by the following limitations. Because of our setting, our findings may not be generalizable to all patients with cancer, HF, and COPD. However, our subjects were admitted to general medical and cardiology services, and had common conditions, and therefore are likely similar to those presenting to other hospitals. We relied on self‐report measures to assess severity of symptoms. Patient self‐report, while potentially subject to imprecision due to poor recall and social demand biases, is considered the gold standard for symptom assessment.15 Finally, 2‐week follow‐up is relatively short, and it is possible that symptoms may have improved had we assessed them over a longer period. The longitudinal study of elders in the community that followed subjects over 22 months found that, for many patients, symptoms worsened over time and nearly half of our subjects died at 12 months, suggesting that longer follow‐up would have been unlikely to show improvement in symptoms.6

A significant minority of participants reported a substantial, persistent symptom burden, yet all symptoms assessed in our study are potentially modifiable. Recognizing and treating symptoms can be achieved through the use of targeted interventions.6 Because symptoms can occur in clusters, successful treatment of 1 symptom may also help to improve other symptoms.1 The large number of participants reporting moderate‐to‐severe levels of symptom burden at 2 weeks after discharge highlights an unmet need for improved symptom control in the outpatient setting. Unfortunately, while evidence exists for managing pain in patients with cancer, such evidence‐based practices are lacking for the management of pain and other symptoms in patients with HF and COPD. Some symptoms may require specific, disease‐oriented management. However, many symptoms may be due to common comorbidities, such as pain from degenerative joint disease, that may likely respond to proven treatments.16

Our study confirmed the significant burden of symptoms experienced by patients with serious illness and demonstrated that patients with COPD report as much symptom burden as patients with cancer and HF, if not more. While symptom severity improved over the course of the hospitalization and follow‐up, a large percentage of patients reported significant symptom burden at follow‐up. Depression was also common in these patients. Because these symptoms diminish quality of life, routine assessment and management of these symptoms is critical for improving the quality of care provided to these patients. Additional research on the best approaches to manage symptoms, including medications, interventions, and structures of care, could further improve care.

In recent decades, hospital lengths of stay have decreased and there has been a shift toward outpatient management for many pediatric conditions. In 2003, one‐third of all children admitted to US hospitals experienced 1‐day inpatient stays, an increase from 19% in 1993.1 Some hospitals have developed dedicated observation units for the care of children, with select diagnoses, who are expected to respond to less than 24 hours of treatment.26 Expansion of observation services has been suggested as an approach to lessen emergency department (ED) crowding7 and alleviate high‐capacity conditions within hospital inpatient units.8

In contrast to care delivered in a dedicated observation unit, observation status is an administrative label applied to patients who do not meet inpatient criteria as defined by third parties such as InterQual. While the decision to admit a patient is ultimately at the discretion of the ordering physician, many hospitals use predetermined criteria to assign observation status to patients admitted to observation and inpatient units.9 Treatment provided under observation status is designated by hospitals and payers as outpatient care, even when delivered in an inpatient bed.10 As outpatient‐designated care, observation cases do not enter publicly available administrative datasets of hospital discharges that have traditionally been used to understand hospital resource utilization, including the National Hospital Discharge Survey and the Kid's Inpatient Database.11, 12

We hypothesize that there has been an increase in observation status care delivered to children in recent years, and that the majority of children under observation were discharged home without converting to inpatient status. To determine trends in pediatric observation status care, we conducted the first longitudinal, multicenter evaluation of observation status code utilization following ED treatment in a sample of US freestanding children's hospitals. In addition, we focused on the most recent year of data among top ranking diagnoses to assess the current state of observation status stay outcomes (including conversion to inpatient status and return visits).

METHODS

DATA ANALYSIS

For each of the 6 years of study, we calculated the following proportions to determine trends over time: 1) the number of Observation Status admissions from the ED as a proportion of the total number of ED visits resulting in Observation or Inpatient admission, and 2) the number of 1‐Day Stays admitted from the ED as a proportion of the total number of ED visits resulting in Observation or Inpatient admissions. Trends were analyzed using linear regression. Trends were also calculated for the total volume of admissions from the ED and the case‐mix index (CMI). CMI was assessed to evaluate for changes in the severity of illness for children admitted from the ED over the study period. Each hospital's CMI was calculated as an average of their Observation and Inpatient Status discharges' charge weights during the study period. Charge weights were calculated at the All Patient Refined Diagnosis Related Groups (APR‐DRG)/severity of illness level (3M Health Information Systems, St Paul, MN) and were normalized national average charges derived by Thomson‐Reuters from their Pediatric Projected National Database. Weights were then assigned to each discharge based on the discharge's APR‐DRG and severity level assignment.

To assess the current outcomes for observation, we analyzed stays with associated observation billing codes from the most recent year of available data (2009). Stays with Observation patient type were considered to have been discharged from observation, while those with an Inpatient Status patient type were considered to have converted to an inpatient admission during the observation period.

Using the 2009 data, we calculated descriptive statistics for patient characteristics (eg, age, gender, payer) comparing Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions using chi‐square statistics. Age was categorized using the American Academy of Pediatrics groupings: <30 days, 30 days1 year, 12 years, 34 years, 512 years, 1317 years, >18 years. Designated payer was categorized into government, private, and other, including self‐pay and uninsured groups.

We used the Severity Classification Systems (SCS) developed for pediatric emergency care to estimate severity of illness for the visit.17 In this 5‐level system, each ICD‐9 diagnosis code is associated with a score related to the intensity of ED resources needed to care for a child with that diagnosis. In our analyses, each case was assigned the maximal SCS category based on the highest severity ICD‐9 code associated with the stay. Within the SCS, a score of 1 indicates minor illness (eg, diaper dermatitis) and 5 indicates major illness (eg, septic shock). The proportions of visits within categorical SCS scores were compared for Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions using chi‐square statistics.

We determined the top 10 ranking diagnoses for which children were admitted from the ED in 2009 using the Diagnosis Grouping System (DGS).18 The DGS was designed specifically to categorize pediatric ED visits into clinically meaningful groups. The ICD‐9 code for the principal discharge diagnosis was used to assign records to 1 of the 77 DGS subgroups. Within each of the top ranking DGS subgroups, we determined the proportion of Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions.

To provide clinically relevant outcomes of Observation Stays for common conditions, we selected stays with observation charges from within the top 10 ranking observation stay DGS subgroups in 2009. Outcomes for observation included: 1) immediate outcome of the observation stay (ie, discharge or conversion to inpatient status), 2) return visits to the ED in the 3 days following observation, and 3) readmissions to the hospital in the 3 and 30 days following observation. Bivariate comparisons of return visits and readmissions for Observation versus 1‐Day Stays within DGS subgroups were analyzed using chi‐square tests. Multivariate analyses of return visits and readmissions were conducted using Generalized Estimating Equations adjusting for severity of illness by SCS score and clustering by hospital. To account for local practice patterns, we also adjusted for a grouped treatment variable that included the site level proportion of children admitted to Observation Status, 1‐Day‐Stays, and longer Inpatient admissions. All statistical analyses were performed using SAS (version 9.2, SAS Institute, Inc, Cary, NC); P values <0.05 were considered statistically significant.

RESULTS

Trends in Short‐Stays

An increase in proportion of Observation Stays was mirrored by a decrease in proportion of 1‐Day Stays over the study period (Figure 1). In 2009, there were 1.4 times more Observation Stays than 1‐Day Stays (25,653 vs 18,425) compared with 14,242 and 20,747, respectively, in 2004. This shift toward more Observation Stays occurred as hospitals faced a 16% increase in the total number of admissions from the ED (91,318 to 108,217) and change in CMI from 1.48 to 1.51. Over the study period, roughly 40% of all admissions from the ED were Short‐Stays (Observation and 1‐Day Stays). Median LOS for Observation Status stays was 1 day (interquartile range [IQR]: 11).

Figure 1

DISCUSSION

In this first, multicenter longitudinal study of pediatric observation following an ED visit, we found that Observation Status code utilization has increased steadily over the past 6 years and, in 2007, the proportion of children admitted to observation status surpassed the proportion of children experiencing a 1‐day inpatient admission. Taken together, Short‐Stays made up more than 40% of the hospital‐based care delivered to children admitted from an ED. Stable trends in CMI over time suggest that observation status may be replacing inpatient status designated care for pediatric Short‐Stays in these hospitals. Our findings suggest the lines between outpatient observation and short‐stay inpatient care are becoming increasingly blurred. These trends have occurred in the setting of changing policies for hospital reimbursement, requirements for patients to meet criteria to qualify for inpatient admissions, and efforts to avoid stays deemed unnecessary or inappropriate by their brief duration.19 Therefore there is a growing need to understand the impact of children under observation on the structure, delivery, and financing of acute hospital care for children.

Our results also have implications for pediatric health services research that relies on hospital administrative databases that do not contain observation stays. Currently, observation stays are systematically excluded from many inpatient administrative datasets.11, 12 Analyses of datasets that do not account for observation stays likely result in underestimation of hospitalization rates and hospital resource utilization for children. This may be particularly important for high‐volume conditions, such as asthma and acute infections, for which children commonly require brief periods of hospital‐based care beyond an ED encounter. Data from pediatric observation status admissions should be consistently included in hospital administrative datasets to allow for more comprehensive analyses of hospital resource utilization among children.

Prior research has shown that the diagnoses commonly treated in pediatric observation units overlap with the diagnoses for which children experience 1‐Day Stays.1, 20 We found a similar pattern of conditions for which children were under Observation Status and 1‐Day Stays with comparable severity of illness between the groups in terms of SCS scores. Our findings imply a need to determine how and why hospitals differentiate Observation Status from 1‐Day‐Stay groups in order to improve the assignment of observation status. Assuming continued pressures from payers to provide more care in outpatient or observation settings, there is potential for expansion of dedicated observation services for children in the US. Without designated observation units or processes to group patients with lower severity conditions, there may be limited opportunities to realize more efficient hospital care simply through the application of the label of observation status.

For more than 30 years, observation services have been provided to children who require a period of monitoring to determine their response to therapy and the need for acute inpatient admission from the ED.21While we were not able to determine the location of care for observation status patients in this study, we know that few children's hospitals have dedicated observation units and, even when an observation unit is present, not all observation status patients are cared for in dedicated observation units.9 This, in essence, means that most children under observation status are cared for in virtual observation by inpatient teams using inpatient beds. If observation patients are treated in inpatient beds and consume the same resources as inpatients, then cost‐savings based on reimbursement contracts with payers may not reflect an actual reduction in services. Pediatric institutions will need to closely monitor the financial implications of observation status given the historical differences in payment for observation and inpatient care.

With more than 70% of children being discharged home following observation, our results are comparable to the published literature2, 5, 6, 22, 23 and guidelines for observation unit operations.24 Similar to prior studies,4, 15, 2530 our results also indicate that return visits and readmissions following observation are uncommon events. Our findings can serve as initial benchmarks for condition‐specific outcomes for pediatric observation care. Studies are needed both to identify the clinical characteristics predictive of successful discharge home from observation and to explore the hospital‐to‐hospital variability in outcomes for observation. Such studies are necessary to identify the most successful healthcare delivery models for pediatric observation stays.

LIMITATIONS

The primary limitation to our results is that data from a subset of freestanding children's hospitals may not reflect observation stays at other children's hospitals or the community hospitals that care for children across the US. Only 18 of 42 current PHIS member hospitals have provided both outpatient visit and inpatient stay data for each year of the study period and were considered eligible. In an effort to ensure the quality of observation stay data, we included the 16 hospitals that assigned observation charges to at least 90% of their observation status stays in the PHIS database. The exclusion of the 2 hospitals where <90% of observation status patients were assigned observation charges likely resulted in an underestimation of the utilization of observation status.

Second, there is potential for misclassification of patient type given institutional variations in the assignment of patient status. The PHIS database does not contain information about the factors that were considered in the assignment of observation status. At the time of admission from the ED, observation or inpatient status is assigned. While this decision is clearly reserved for the admitting physician, the process is not standardized across hospitals.9 Some institutions have Utilization Managers on site to help guide decision‐making, while others allow the assignment to be made by physicians without specific guidance. As a result, some patients may be assigned to observation status at admission and reassigned to inpatient status following Utilization Review, which may bias our results toward overestimation of the number of observation stays that converted to inpatient status.

The third limitation to our results relates to return visits. An accurate assessment of return visits is subject to the patient returning to the same hospital. If children do not return to the same hospital, our results would underestimate return visits and readmissions. In addition, we did not assess the reason for return visit as there was no way to verify if the return visit was truly related to the index visit without detailed chart review. Assuming children return to the same hospital for different reasons, our results would overestimate return visits associated with observation stays. We suspect that many 3‐day return visits result from the progression of acute illness or failure to respond to initial treatment, and 30‐day readmissions reflect recurrent hospital care needs related to chronic illnesses.

Lastly, severity classification is difficult when analyzing administrative datasets without physiologic patient data, and the SCS may not provide enough detail to reveal clinically important differences between patient groups.

CONCLUSIONS

Short‐stay hospitalizations following ED visits are common among children, and the majority of pediatric short‐stays are under observation status. Analyses of inpatient administrative databases that exclude observation stays likely result in an underestimation of hospital resource utilization for children. Efforts are needed to ensure that patients under observation status are accounted for in hospital administrative datasets used for pediatric health services research, and healthcare resource allocation, as it relates to hospital‐based care. While the clinical outcomes for observation patients appear favorable in terms of conversion to inpatient admissions and return visits, the financial implications of observation status care within children's hospitals are currently unknown.

In recent decades, hospital lengths of stay have decreased and there has been a shift toward outpatient management for many pediatric conditions. In 2003, one‐third of all children admitted to US hospitals experienced 1‐day inpatient stays, an increase from 19% in 1993.1 Some hospitals have developed dedicated observation units for the care of children, with select diagnoses, who are expected to respond to less than 24 hours of treatment.26 Expansion of observation services has been suggested as an approach to lessen emergency department (ED) crowding7 and alleviate high‐capacity conditions within hospital inpatient units.8

In contrast to care delivered in a dedicated observation unit, observation status is an administrative label applied to patients who do not meet inpatient criteria as defined by third parties such as InterQual. While the decision to admit a patient is ultimately at the discretion of the ordering physician, many hospitals use predetermined criteria to assign observation status to patients admitted to observation and inpatient units.9 Treatment provided under observation status is designated by hospitals and payers as outpatient care, even when delivered in an inpatient bed.10 As outpatient‐designated care, observation cases do not enter publicly available administrative datasets of hospital discharges that have traditionally been used to understand hospital resource utilization, including the National Hospital Discharge Survey and the Kid's Inpatient Database.11, 12

We hypothesize that there has been an increase in observation status care delivered to children in recent years, and that the majority of children under observation were discharged home without converting to inpatient status. To determine trends in pediatric observation status care, we conducted the first longitudinal, multicenter evaluation of observation status code utilization following ED treatment in a sample of US freestanding children's hospitals. In addition, we focused on the most recent year of data among top ranking diagnoses to assess the current state of observation status stay outcomes (including conversion to inpatient status and return visits).

METHODS

DATA ANALYSIS

For each of the 6 years of study, we calculated the following proportions to determine trends over time: 1) the number of Observation Status admissions from the ED as a proportion of the total number of ED visits resulting in Observation or Inpatient admission, and 2) the number of 1‐Day Stays admitted from the ED as a proportion of the total number of ED visits resulting in Observation or Inpatient admissions. Trends were analyzed using linear regression. Trends were also calculated for the total volume of admissions from the ED and the case‐mix index (CMI). CMI was assessed to evaluate for changes in the severity of illness for children admitted from the ED over the study period. Each hospital's CMI was calculated as an average of their Observation and Inpatient Status discharges' charge weights during the study period. Charge weights were calculated at the All Patient Refined Diagnosis Related Groups (APR‐DRG)/severity of illness level (3M Health Information Systems, St Paul, MN) and were normalized national average charges derived by Thomson‐Reuters from their Pediatric Projected National Database. Weights were then assigned to each discharge based on the discharge's APR‐DRG and severity level assignment.

To assess the current outcomes for observation, we analyzed stays with associated observation billing codes from the most recent year of available data (2009). Stays with Observation patient type were considered to have been discharged from observation, while those with an Inpatient Status patient type were considered to have converted to an inpatient admission during the observation period.

Using the 2009 data, we calculated descriptive statistics for patient characteristics (eg, age, gender, payer) comparing Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions using chi‐square statistics. Age was categorized using the American Academy of Pediatrics groupings: <30 days, 30 days1 year, 12 years, 34 years, 512 years, 1317 years, >18 years. Designated payer was categorized into government, private, and other, including self‐pay and uninsured groups.

We used the Severity Classification Systems (SCS) developed for pediatric emergency care to estimate severity of illness for the visit.17 In this 5‐level system, each ICD‐9 diagnosis code is associated with a score related to the intensity of ED resources needed to care for a child with that diagnosis. In our analyses, each case was assigned the maximal SCS category based on the highest severity ICD‐9 code associated with the stay. Within the SCS, a score of 1 indicates minor illness (eg, diaper dermatitis) and 5 indicates major illness (eg, septic shock). The proportions of visits within categorical SCS scores were compared for Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions using chi‐square statistics.

We determined the top 10 ranking diagnoses for which children were admitted from the ED in 2009 using the Diagnosis Grouping System (DGS).18 The DGS was designed specifically to categorize pediatric ED visits into clinically meaningful groups. The ICD‐9 code for the principal discharge diagnosis was used to assign records to 1 of the 77 DGS subgroups. Within each of the top ranking DGS subgroups, we determined the proportion of Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions.

To provide clinically relevant outcomes of Observation Stays for common conditions, we selected stays with observation charges from within the top 10 ranking observation stay DGS subgroups in 2009. Outcomes for observation included: 1) immediate outcome of the observation stay (ie, discharge or conversion to inpatient status), 2) return visits to the ED in the 3 days following observation, and 3) readmissions to the hospital in the 3 and 30 days following observation. Bivariate comparisons of return visits and readmissions for Observation versus 1‐Day Stays within DGS subgroups were analyzed using chi‐square tests. Multivariate analyses of return visits and readmissions were conducted using Generalized Estimating Equations adjusting for severity of illness by SCS score and clustering by hospital. To account for local practice patterns, we also adjusted for a grouped treatment variable that included the site level proportion of children admitted to Observation Status, 1‐Day‐Stays, and longer Inpatient admissions. All statistical analyses were performed using SAS (version 9.2, SAS Institute, Inc, Cary, NC); P values <0.05 were considered statistically significant.

RESULTS

Trends in Short‐Stays

An increase in proportion of Observation Stays was mirrored by a decrease in proportion of 1‐Day Stays over the study period (Figure 1). In 2009, there were 1.4 times more Observation Stays than 1‐Day Stays (25,653 vs 18,425) compared with 14,242 and 20,747, respectively, in 2004. This shift toward more Observation Stays occurred as hospitals faced a 16% increase in the total number of admissions from the ED (91,318 to 108,217) and change in CMI from 1.48 to 1.51. Over the study period, roughly 40% of all admissions from the ED were Short‐Stays (Observation and 1‐Day Stays). Median LOS for Observation Status stays was 1 day (interquartile range [IQR]: 11).

Figure 1

DISCUSSION

In this first, multicenter longitudinal study of pediatric observation following an ED visit, we found that Observation Status code utilization has increased steadily over the past 6 years and, in 2007, the proportion of children admitted to observation status surpassed the proportion of children experiencing a 1‐day inpatient admission. Taken together, Short‐Stays made up more than 40% of the hospital‐based care delivered to children admitted from an ED. Stable trends in CMI over time suggest that observation status may be replacing inpatient status designated care for pediatric Short‐Stays in these hospitals. Our findings suggest the lines between outpatient observation and short‐stay inpatient care are becoming increasingly blurred. These trends have occurred in the setting of changing policies for hospital reimbursement, requirements for patients to meet criteria to qualify for inpatient admissions, and efforts to avoid stays deemed unnecessary or inappropriate by their brief duration.19 Therefore there is a growing need to understand the impact of children under observation on the structure, delivery, and financing of acute hospital care for children.

Our results also have implications for pediatric health services research that relies on hospital administrative databases that do not contain observation stays. Currently, observation stays are systematically excluded from many inpatient administrative datasets.11, 12 Analyses of datasets that do not account for observation stays likely result in underestimation of hospitalization rates and hospital resource utilization for children. This may be particularly important for high‐volume conditions, such as asthma and acute infections, for which children commonly require brief periods of hospital‐based care beyond an ED encounter. Data from pediatric observation status admissions should be consistently included in hospital administrative datasets to allow for more comprehensive analyses of hospital resource utilization among children.

Prior research has shown that the diagnoses commonly treated in pediatric observation units overlap with the diagnoses for which children experience 1‐Day Stays.1, 20 We found a similar pattern of conditions for which children were under Observation Status and 1‐Day Stays with comparable severity of illness between the groups in terms of SCS scores. Our findings imply a need to determine how and why hospitals differentiate Observation Status from 1‐Day‐Stay groups in order to improve the assignment of observation status. Assuming continued pressures from payers to provide more care in outpatient or observation settings, there is potential for expansion of dedicated observation services for children in the US. Without designated observation units or processes to group patients with lower severity conditions, there may be limited opportunities to realize more efficient hospital care simply through the application of the label of observation status.

For more than 30 years, observation services have been provided to children who require a period of monitoring to determine their response to therapy and the need for acute inpatient admission from the ED.21While we were not able to determine the location of care for observation status patients in this study, we know that few children's hospitals have dedicated observation units and, even when an observation unit is present, not all observation status patients are cared for in dedicated observation units.9 This, in essence, means that most children under observation status are cared for in virtual observation by inpatient teams using inpatient beds. If observation patients are treated in inpatient beds and consume the same resources as inpatients, then cost‐savings based on reimbursement contracts with payers may not reflect an actual reduction in services. Pediatric institutions will need to closely monitor the financial implications of observation status given the historical differences in payment for observation and inpatient care.

With more than 70% of children being discharged home following observation, our results are comparable to the published literature2, 5, 6, 22, 23 and guidelines for observation unit operations.24 Similar to prior studies,4, 15, 2530 our results also indicate that return visits and readmissions following observation are uncommon events. Our findings can serve as initial benchmarks for condition‐specific outcomes for pediatric observation care. Studies are needed both to identify the clinical characteristics predictive of successful discharge home from observation and to explore the hospital‐to‐hospital variability in outcomes for observation. Such studies are necessary to identify the most successful healthcare delivery models for pediatric observation stays.

LIMITATIONS

The primary limitation to our results is that data from a subset of freestanding children's hospitals may not reflect observation stays at other children's hospitals or the community hospitals that care for children across the US. Only 18 of 42 current PHIS member hospitals have provided both outpatient visit and inpatient stay data for each year of the study period and were considered eligible. In an effort to ensure the quality of observation stay data, we included the 16 hospitals that assigned observation charges to at least 90% of their observation status stays in the PHIS database. The exclusion of the 2 hospitals where <90% of observation status patients were assigned observation charges likely resulted in an underestimation of the utilization of observation status.

Second, there is potential for misclassification of patient type given institutional variations in the assignment of patient status. The PHIS database does not contain information about the factors that were considered in the assignment of observation status. At the time of admission from the ED, observation or inpatient status is assigned. While this decision is clearly reserved for the admitting physician, the process is not standardized across hospitals.9 Some institutions have Utilization Managers on site to help guide decision‐making, while others allow the assignment to be made by physicians without specific guidance. As a result, some patients may be assigned to observation status at admission and reassigned to inpatient status following Utilization Review, which may bias our results toward overestimation of the number of observation stays that converted to inpatient status.

The third limitation to our results relates to return visits. An accurate assessment of return visits is subject to the patient returning to the same hospital. If children do not return to the same hospital, our results would underestimate return visits and readmissions. In addition, we did not assess the reason for return visit as there was no way to verify if the return visit was truly related to the index visit without detailed chart review. Assuming children return to the same hospital for different reasons, our results would overestimate return visits associated with observation stays. We suspect that many 3‐day return visits result from the progression of acute illness or failure to respond to initial treatment, and 30‐day readmissions reflect recurrent hospital care needs related to chronic illnesses.

Lastly, severity classification is difficult when analyzing administrative datasets without physiologic patient data, and the SCS may not provide enough detail to reveal clinically important differences between patient groups.

CONCLUSIONS

Short‐stay hospitalizations following ED visits are common among children, and the majority of pediatric short‐stays are under observation status. Analyses of inpatient administrative databases that exclude observation stays likely result in an underestimation of hospital resource utilization for children. Efforts are needed to ensure that patients under observation status are accounted for in hospital administrative datasets used for pediatric health services research, and healthcare resource allocation, as it relates to hospital‐based care. While the clinical outcomes for observation patients appear favorable in terms of conversion to inpatient admissions and return visits, the financial implications of observation status care within children's hospitals are currently unknown.

In recent decades, hospital lengths of stay have decreased and there has been a shift toward outpatient management for many pediatric conditions. In 2003, one‐third of all children admitted to US hospitals experienced 1‐day inpatient stays, an increase from 19% in 1993.1 Some hospitals have developed dedicated observation units for the care of children, with select diagnoses, who are expected to respond to less than 24 hours of treatment.26 Expansion of observation services has been suggested as an approach to lessen emergency department (ED) crowding7 and alleviate high‐capacity conditions within hospital inpatient units.8

In contrast to care delivered in a dedicated observation unit, observation status is an administrative label applied to patients who do not meet inpatient criteria as defined by third parties such as InterQual. While the decision to admit a patient is ultimately at the discretion of the ordering physician, many hospitals use predetermined criteria to assign observation status to patients admitted to observation and inpatient units.9 Treatment provided under observation status is designated by hospitals and payers as outpatient care, even when delivered in an inpatient bed.10 As outpatient‐designated care, observation cases do not enter publicly available administrative datasets of hospital discharges that have traditionally been used to understand hospital resource utilization, including the National Hospital Discharge Survey and the Kid's Inpatient Database.11, 12

We hypothesize that there has been an increase in observation status care delivered to children in recent years, and that the majority of children under observation were discharged home without converting to inpatient status. To determine trends in pediatric observation status care, we conducted the first longitudinal, multicenter evaluation of observation status code utilization following ED treatment in a sample of US freestanding children's hospitals. In addition, we focused on the most recent year of data among top ranking diagnoses to assess the current state of observation status stay outcomes (including conversion to inpatient status and return visits).

METHODS

DATA ANALYSIS

For each of the 6 years of study, we calculated the following proportions to determine trends over time: 1) the number of Observation Status admissions from the ED as a proportion of the total number of ED visits resulting in Observation or Inpatient admission, and 2) the number of 1‐Day Stays admitted from the ED as a proportion of the total number of ED visits resulting in Observation or Inpatient admissions. Trends were analyzed using linear regression. Trends were also calculated for the total volume of admissions from the ED and the case‐mix index (CMI). CMI was assessed to evaluate for changes in the severity of illness for children admitted from the ED over the study period. Each hospital's CMI was calculated as an average of their Observation and Inpatient Status discharges' charge weights during the study period. Charge weights were calculated at the All Patient Refined Diagnosis Related Groups (APR‐DRG)/severity of illness level (3M Health Information Systems, St Paul, MN) and were normalized national average charges derived by Thomson‐Reuters from their Pediatric Projected National Database. Weights were then assigned to each discharge based on the discharge's APR‐DRG and severity level assignment.

To assess the current outcomes for observation, we analyzed stays with associated observation billing codes from the most recent year of available data (2009). Stays with Observation patient type were considered to have been discharged from observation, while those with an Inpatient Status patient type were considered to have converted to an inpatient admission during the observation period.

Using the 2009 data, we calculated descriptive statistics for patient characteristics (eg, age, gender, payer) comparing Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions using chi‐square statistics. Age was categorized using the American Academy of Pediatrics groupings: <30 days, 30 days1 year, 12 years, 34 years, 512 years, 1317 years, >18 years. Designated payer was categorized into government, private, and other, including self‐pay and uninsured groups.

We used the Severity Classification Systems (SCS) developed for pediatric emergency care to estimate severity of illness for the visit.17 In this 5‐level system, each ICD‐9 diagnosis code is associated with a score related to the intensity of ED resources needed to care for a child with that diagnosis. In our analyses, each case was assigned the maximal SCS category based on the highest severity ICD‐9 code associated with the stay. Within the SCS, a score of 1 indicates minor illness (eg, diaper dermatitis) and 5 indicates major illness (eg, septic shock). The proportions of visits within categorical SCS scores were compared for Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions using chi‐square statistics.

We determined the top 10 ranking diagnoses for which children were admitted from the ED in 2009 using the Diagnosis Grouping System (DGS).18 The DGS was designed specifically to categorize pediatric ED visits into clinically meaningful groups. The ICD‐9 code for the principal discharge diagnosis was used to assign records to 1 of the 77 DGS subgroups. Within each of the top ranking DGS subgroups, we determined the proportion of Observation Stays, 1‐Day Stays, and longer‐duration Inpatient admissions.

To provide clinically relevant outcomes of Observation Stays for common conditions, we selected stays with observation charges from within the top 10 ranking observation stay DGS subgroups in 2009. Outcomes for observation included: 1) immediate outcome of the observation stay (ie, discharge or conversion to inpatient status), 2) return visits to the ED in the 3 days following observation, and 3) readmissions to the hospital in the 3 and 30 days following observation. Bivariate comparisons of return visits and readmissions for Observation versus 1‐Day Stays within DGS subgroups were analyzed using chi‐square tests. Multivariate analyses of return visits and readmissions were conducted using Generalized Estimating Equations adjusting for severity of illness by SCS score and clustering by hospital. To account for local practice patterns, we also adjusted for a grouped treatment variable that included the site level proportion of children admitted to Observation Status, 1‐Day‐Stays, and longer Inpatient admissions. All statistical analyses were performed using SAS (version 9.2, SAS Institute, Inc, Cary, NC); P values <0.05 were considered statistically significant.

RESULTS

Trends in Short‐Stays

An increase in proportion of Observation Stays was mirrored by a decrease in proportion of 1‐Day Stays over the study period (Figure 1). In 2009, there were 1.4 times more Observation Stays than 1‐Day Stays (25,653 vs 18,425) compared with 14,242 and 20,747, respectively, in 2004. This shift toward more Observation Stays occurred as hospitals faced a 16% increase in the total number of admissions from the ED (91,318 to 108,217) and change in CMI from 1.48 to 1.51. Over the study period, roughly 40% of all admissions from the ED were Short‐Stays (Observation and 1‐Day Stays). Median LOS for Observation Status stays was 1 day (interquartile range [IQR]: 11).

Figure 1

DISCUSSION

In this first, multicenter longitudinal study of pediatric observation following an ED visit, we found that Observation Status code utilization has increased steadily over the past 6 years and, in 2007, the proportion of children admitted to observation status surpassed the proportion of children experiencing a 1‐day inpatient admission. Taken together, Short‐Stays made up more than 40% of the hospital‐based care delivered to children admitted from an ED. Stable trends in CMI over time suggest that observation status may be replacing inpatient status designated care for pediatric Short‐Stays in these hospitals. Our findings suggest the lines between outpatient observation and short‐stay inpatient care are becoming increasingly blurred. These trends have occurred in the setting of changing policies for hospital reimbursement, requirements for patients to meet criteria to qualify for inpatient admissions, and efforts to avoid stays deemed unnecessary or inappropriate by their brief duration.19 Therefore there is a growing need to understand the impact of children under observation on the structure, delivery, and financing of acute hospital care for children.

Our results also have implications for pediatric health services research that relies on hospital administrative databases that do not contain observation stays. Currently, observation stays are systematically excluded from many inpatient administrative datasets.11, 12 Analyses of datasets that do not account for observation stays likely result in underestimation of hospitalization rates and hospital resource utilization for children. This may be particularly important for high‐volume conditions, such as asthma and acute infections, for which children commonly require brief periods of hospital‐based care beyond an ED encounter. Data from pediatric observation status admissions should be consistently included in hospital administrative datasets to allow for more comprehensive analyses of hospital resource utilization among children.

Prior research has shown that the diagnoses commonly treated in pediatric observation units overlap with the diagnoses for which children experience 1‐Day Stays.1, 20 We found a similar pattern of conditions for which children were under Observation Status and 1‐Day Stays with comparable severity of illness between the groups in terms of SCS scores. Our findings imply a need to determine how and why hospitals differentiate Observation Status from 1‐Day‐Stay groups in order to improve the assignment of observation status. Assuming continued pressures from payers to provide more care in outpatient or observation settings, there is potential for expansion of dedicated observation services for children in the US. Without designated observation units or processes to group patients with lower severity conditions, there may be limited opportunities to realize more efficient hospital care simply through the application of the label of observation status.

For more than 30 years, observation services have been provided to children who require a period of monitoring to determine their response to therapy and the need for acute inpatient admission from the ED.21While we were not able to determine the location of care for observation status patients in this study, we know that few children's hospitals have dedicated observation units and, even when an observation unit is present, not all observation status patients are cared for in dedicated observation units.9 This, in essence, means that most children under observation status are cared for in virtual observation by inpatient teams using inpatient beds. If observation patients are treated in inpatient beds and consume the same resources as inpatients, then cost‐savings based on reimbursement contracts with payers may not reflect an actual reduction in services. Pediatric institutions will need to closely monitor the financial implications of observation status given the historical differences in payment for observation and inpatient care.

With more than 70% of children being discharged home following observation, our results are comparable to the published literature2, 5, 6, 22, 23 and guidelines for observation unit operations.24 Similar to prior studies,4, 15, 2530 our results also indicate that return visits and readmissions following observation are uncommon events. Our findings can serve as initial benchmarks for condition‐specific outcomes for pediatric observation care. Studies are needed both to identify the clinical characteristics predictive of successful discharge home from observation and to explore the hospital‐to‐hospital variability in outcomes for observation. Such studies are necessary to identify the most successful healthcare delivery models for pediatric observation stays.

LIMITATIONS

The primary limitation to our results is that data from a subset of freestanding children's hospitals may not reflect observation stays at other children's hospitals or the community hospitals that care for children across the US. Only 18 of 42 current PHIS member hospitals have provided both outpatient visit and inpatient stay data for each year of the study period and were considered eligible. In an effort to ensure the quality of observation stay data, we included the 16 hospitals that assigned observation charges to at least 90% of their observation status stays in the PHIS database. The exclusion of the 2 hospitals where <90% of observation status patients were assigned observation charges likely resulted in an underestimation of the utilization of observation status.

Second, there is potential for misclassification of patient type given institutional variations in the assignment of patient status. The PHIS database does not contain information about the factors that were considered in the assignment of observation status. At the time of admission from the ED, observation or inpatient status is assigned. While this decision is clearly reserved for the admitting physician, the process is not standardized across hospitals.9 Some institutions have Utilization Managers on site to help guide decision‐making, while others allow the assignment to be made by physicians without specific guidance. As a result, some patients may be assigned to observation status at admission and reassigned to inpatient status following Utilization Review, which may bias our results toward overestimation of the number of observation stays that converted to inpatient status.

The third limitation to our results relates to return visits. An accurate assessment of return visits is subject to the patient returning to the same hospital. If children do not return to the same hospital, our results would underestimate return visits and readmissions. In addition, we did not assess the reason for return visit as there was no way to verify if the return visit was truly related to the index visit without detailed chart review. Assuming children return to the same hospital for different reasons, our results would overestimate return visits associated with observation stays. We suspect that many 3‐day return visits result from the progression of acute illness or failure to respond to initial treatment, and 30‐day readmissions reflect recurrent hospital care needs related to chronic illnesses.

Lastly, severity classification is difficult when analyzing administrative datasets without physiologic patient data, and the SCS may not provide enough detail to reveal clinically important differences between patient groups.

CONCLUSIONS

Short‐stay hospitalizations following ED visits are common among children, and the majority of pediatric short‐stays are under observation status. Analyses of inpatient administrative databases that exclude observation stays likely result in an underestimation of hospital resource utilization for children. Efforts are needed to ensure that patients under observation status are accounted for in hospital administrative datasets used for pediatric health services research, and healthcare resource allocation, as it relates to hospital‐based care. While the clinical outcomes for observation patients appear favorable in terms of conversion to inpatient admissions and return visits, the financial implications of observation status care within children's hospitals are currently unknown.

Inpatient falls are the most common type of inpatient adverse event,1 persist as a significant problem nationally, and result in patient injury, increased length of stay, healthcare costs, and litigation.27 Inpatient falls remain a main focus of patient safety and a measure of quality in this era of healthcare reform and quality improvement.8 Inpatient fall rates per 1000 patient‐days range from 1.4 to 18.2.4, 9 The absolute percentage of inpatients that fall ranges from 1.3% to 7%.4, 5, 9, 10 Of inpatient falls, almost all data suggest that roughly one‐third result in some type of injury while 3%‐8% result in serious injury or death.9, 1113

Fall prevention interventions have largely been aimed at modifiable risk factors such as getting out of bed with bed alarms, toileting needs with bedside commodes, and reducing delirium through reorientation techniques. There have been several attempts at decreasing fall rates in hospitals surrounding a multidisciplinary, team‐based approach. Two Cochrane reviews and 2 meta‐analyses have partially examined this issue with mixed results.1417 However, none of these reviews focused on the acute care inpatient population. In fact, the majority of the data analyzed for inpatients was from rehabilitation wards and long‐term care wards. Additionally, there exists almost no data examining fall prevention with single interventions in the acute inpatient population, likely due to the belief that falls are multifactorial in etiology and require more comprehensive interventions.

The aim of this article is to determine the impact of team‐based, multidisciplinary quality improvement efforts to reduce inpatient falls in acute care inpatient hospitals and identify key features that determine their effectiveness.

METHODS

RESULTS

Fall Rates

Dykes et al22 and Williams et al24 found a statistically significant reduction in fall rate with falls reduced by 1.16 per 1000‐patient days and 1.5 per 1000‐patient days, respectively. Mitchell and Jones25 demonstrated a large fall reduction but had an extremely small sample size. Brandis21 found an extremely small reduction in fall rates and failed to report a P‐value. Krauss et al23 showed a trend towards reducing falls, and even showed a statistically significant reduction over the first 5 months of the study, but lost significance in the final 4 months. Similarly, Schwendimann et al9 saw more impressive fall reductions in the first year of the study that dissipated in the final 3 years of data collection.

Results from the meta‐analysis of the 6 studies comparing odds ratios are displayed quantitatively and as a forest plot in Figure 1. The figure shows results with 95% CI for each individual study and overall. There was no statistical evidence of heterogeneity between the studies or study designs. Although, due to the small number of studies included, there is poor power to detect true heterogeneity among studies. The magnitude of boxes shown is a relative sample size indicator. Using the random‐effects model, the summary odds ratio is 0.90 (95% CI, 0.83 to 0.99) (P = 0.02) (I² = 0%).27

Figure 1

DISCUSSION

The frequency and morbidity associated with inpatient falls is well established, based on reproduced epidemiologic data. Reducing these adverse events could reduce morbidity, mortality, and healthcare costs, and has become the focus of most hospitals quality and patient safety initiatives. The focus of this review was to examine multidisciplinary efforts to reduce falls in acute care inpatient hospitals. Despite the importance and scope of the problem, there is a paucity of research available on this topic, with a wide literature search yielding only 6 primary research studies.

Our major finding is that multidisciplinary fall prevention strategies have a statistically significant impact on fall rates with a combined OR of 0.90. While this review demonstrates a significant benefit to multidisciplinary fall prevention strategies in the acute inpatient population, the clinical impact of these efforts may be limited. Based on rates ranging from 1.7 to 9.5 falls per 1000‐patient days, multidisciplinary interventions would reduce falls by 1 to 10 falls per 10,000‐patient days using the combined OR calculated of 0.9. Using other available incidence data regarding inpatient falls,4, 9 a reasonable baseline frequency to consider would be 8 falls per 1000 patient‐days. Assuming that prevalence, the number needed to treat (NNT) to prevent a single inpatient fall is 1250 patient days. Furthermore, based on available data, only approximately one‐third of these falls result in injury and only a minor fraction of these results in serious injury.9, 1113 The magnitude of this apparent benefit in the context of fall incidence rates raises some concerns about cost‐effectiveness given the high staffing and systems needs that multidisciplinary prevention programs require. This also suggests that there are limitations when using inpatient falls as a measure of healthcare quality given the absence of high‐quality evidence demonstrating a viable solution to the problem. At present, the Center for Medicare and Medicaid services limit reimbursement for fall‐related injuries if they occur during an acute inpatient hospitalization.28

The complexity of the interventions used may help explain the limited impact. Krauss et al23 examined compliance to their interventions and found less than ideal results. They found only 36.4% of intervention floor patients had maintained a toileting schedule compared to 24.6% on control floors. Additionally, a greater proportion of patients on control floors had a physical or occupational therapy consult, and only 1.8% more patients on intervention floors had walking aids provided. These were all strategies emphasized on the intervention floors. Similarly, Schwendimann et al9 questioned their staff's adherence to protocol after fall prevention committee audits. This may help explain why a potential benefit lost statistical significance with time, based on a natural tendency towards more participation at the beginning of a new policy. Williams et al24 reported only a 64% compliance rate with fall care plan forms and 77% rate of missing information on fall care plans. A multidisciplinary fall prevention study that did not meet inclusion criteria (based on study population) yielded strongly positive results for which the authors commented mostly on changing of the hospital culture surrounding fall prevention as a key to their success.29 Adoptability of a multidisciplinary intervention will clearly impact adherence and the intervention's ultimate effectiveness.

Single intervention strategies, not analyzed in this review, are simpler to execute and adhere to. While these types of interventions may be superior, there is extremely limited data supporting or refuting patient fall benefits in the acute care inpatient population when using simple single interventions. However, some data generated partially on acute care geriatrics wards targeting patient education only showed benefit.30

Dykes et al22 was able to improve compliance rates by removing steps in the process of executing interventions with the FPTK built into the EMR. Importantly, the FPTK was compared against very similar fall prevention strategies, the difference being that patients randomized to the FPTK arm had the assessment and interventions automatically prompted on admission in the EMR. Adherence was measured through Morse Fall Scale completion rates (81% in control units versus 94% in intervention units).22 In many ways, the utility of this study was displaying a fall risk reduction by simply enhancing compliance using health information technology with automated alerts. Additionally, both arms of the study reported low fall rates compared to previously reported data, and there may have been larger benefit seen if the FPTK was compared against no fall prevention strategy. This diminishing of effect size may have been present in all studies reviewed, as usual hospital care commonly includes basic patient safety measures.

Another potential problem with the multidisciplinary fall prevention programs included in the meta‐analysis is the inability to target interventions. Each study employed a fall risk score in an attempt to focus resources on a select group of high‐risk patients. This method is problematic given that countless risk factors for inpatient falls have been identified in the literature. Factors that have been described range from clinical characteristics to laboratory tests.31 The most consistently reproducible patient‐related risks are altered mental status (including cognitive impairment and depression), altered mobility (particularly lower limb weakness), a history of falls, and toileting needs.13, 3236 Less consistency is seen with other traditional risk factors such as age, sedating medication, and length of stay.5, 13, 32, 3638 Attempting to risk‐stratify patients using simple and accurate assessment tools developed from these risk factors has proven to be very difficult. Many tools have been developed based on identified risk factors, but perform very poorly when trying to identify patients who will fall with reasonable specificity and positive predictive value.34, 3944 In fact, it has been demonstrated that using a nurse's judgment, a physician's opinion based on a patient's likelihood to wander or a simple 2‐question tool have all performed better than sophisticated risk calculators.33, 45, 46 Therefore, it is possible that interventions could benefit from including all patients, with de‐emphasis on unproven risk stratification tools.

In contrast to our findings, a modest risk reduction has been demonstrated in several primary articles and meta‐analyses in the subacute, rehabilitation, and long‐term care populations.15, 16, 4750 Additionally, a recent study has described a 63.9% risk reduction in a population that included medical, surgical, psychiatric, and rehabilitation wards.29 One important difference between these settings and the acute inpatient populations may be the amount of time and energy that can be dedicated to fall prevention and overall care planning. Another likely factor is the added challenge of preventing falls in patients with more active medical illnesses. In the acute care setting, a patient's chief complaint may not be completely addressed at the time of first mobilization and ambulation. This may be most relevant in patients who are admitted with syncope, seizure, vertigo, and dehydration.

Our study has several limitations; most notably, the available evidence is limited in quality and quantity. Furthermore, omission of unpublished data may also lead to effect bias, though this would likely be in the direction of ineffective interventions supporting a conclusion that multidisciplinary efforts have had only a small impact on fall rates. Ideally, future studies can limit confounding variables through randomization. However, it is difficult to adequately blind when studying a multidisciplinary fall intervention that depends on patient and provider participation. As a result, none of the papers reviewed met criteria for high quality. However, almost all available data examined in this review came from large sample sizes in which thoughtful interventions were used. Since an inpatient fall will not affect the majority of patients, it was crucial for these studies to recruit a large sample size to have adequate power to detect a difference in fall rates. However, each study used risk assessment tools, which are poor indicators of who will and will not fall in the hospital.34, 39, 42 This may suggest a need for improved risk assessment tools, or be further evidence to include all patients in fall prevention regardless of risk. Quantitative synthesis of multidisciplinary fall interventions has the added limitation of comparing complex, multifaceted treatments that are not perfectly uniform. It is our opinion that interventions are semi‐standardized using the grouping methods employed in Table 3.

Preventing inpatient falls remains a difficult issue to address while convincing data is lacking. Based on current evidence, multidisciplinary fall prevention efforts on acutely ill inpatients show a possible small benefit and should be explored from a cost‐effectiveness standpoint to ensure they garner appropriate investment. Many resources are required to run such teams including nursing staff, equipment, physical and occupational therapy staff, pharmacists, and specialized staff training. We are unaware of any such cost‐effectiveness data available. Effective interventions may be those that maximize compliance through health information technology, maintain staff dedication, increase staff availability, improve risk assessment, or include all patients regardless of calculated fall risk, and take the patient's chief complaint into account in the fall prevention strategy. Where resources are limited, it appears most reasonable to focus on major risk factors for inpatient falls that have independently been shown to be detrimental to outcomes, such as delirium.51 Additionally, using inpatient fall rates as a hospital quality measure may be premature, given the lack of proven efforts to lower fall rates. Multidisciplinary fall prevention efforts on acutely ill inpatients should be further studied using high‐quality, randomized trials. It remains to be seen whether these large programs are cost‐effective, or on balance clinically effective.

Inpatient falls are the most common type of inpatient adverse event,1 persist as a significant problem nationally, and result in patient injury, increased length of stay, healthcare costs, and litigation.27 Inpatient falls remain a main focus of patient safety and a measure of quality in this era of healthcare reform and quality improvement.8 Inpatient fall rates per 1000 patient‐days range from 1.4 to 18.2.4, 9 The absolute percentage of inpatients that fall ranges from 1.3% to 7%.4, 5, 9, 10 Of inpatient falls, almost all data suggest that roughly one‐third result in some type of injury while 3%‐8% result in serious injury or death.9, 1113

Fall prevention interventions have largely been aimed at modifiable risk factors such as getting out of bed with bed alarms, toileting needs with bedside commodes, and reducing delirium through reorientation techniques. There have been several attempts at decreasing fall rates in hospitals surrounding a multidisciplinary, team‐based approach. Two Cochrane reviews and 2 meta‐analyses have partially examined this issue with mixed results.1417 However, none of these reviews focused on the acute care inpatient population. In fact, the majority of the data analyzed for inpatients was from rehabilitation wards and long‐term care wards. Additionally, there exists almost no data examining fall prevention with single interventions in the acute inpatient population, likely due to the belief that falls are multifactorial in etiology and require more comprehensive interventions.

The aim of this article is to determine the impact of team‐based, multidisciplinary quality improvement efforts to reduce inpatient falls in acute care inpatient hospitals and identify key features that determine their effectiveness.

METHODS

RESULTS

Fall Rates

Dykes et al22 and Williams et al24 found a statistically significant reduction in fall rate with falls reduced by 1.16 per 1000‐patient days and 1.5 per 1000‐patient days, respectively. Mitchell and Jones25 demonstrated a large fall reduction but had an extremely small sample size. Brandis21 found an extremely small reduction in fall rates and failed to report a P‐value. Krauss et al23 showed a trend towards reducing falls, and even showed a statistically significant reduction over the first 5 months of the study, but lost significance in the final 4 months. Similarly, Schwendimann et al9 saw more impressive fall reductions in the first year of the study that dissipated in the final 3 years of data collection.

Results from the meta‐analysis of the 6 studies comparing odds ratios are displayed quantitatively and as a forest plot in Figure 1. The figure shows results with 95% CI for each individual study and overall. There was no statistical evidence of heterogeneity between the studies or study designs. Although, due to the small number of studies included, there is poor power to detect true heterogeneity among studies. The magnitude of boxes shown is a relative sample size indicator. Using the random‐effects model, the summary odds ratio is 0.90 (95% CI, 0.83 to 0.99) (P = 0.02) (I² = 0%).27

Figure 1

DISCUSSION

The frequency and morbidity associated with inpatient falls is well established, based on reproduced epidemiologic data. Reducing these adverse events could reduce morbidity, mortality, and healthcare costs, and has become the focus of most hospitals quality and patient safety initiatives. The focus of this review was to examine multidisciplinary efforts to reduce falls in acute care inpatient hospitals. Despite the importance and scope of the problem, there is a paucity of research available on this topic, with a wide literature search yielding only 6 primary research studies.

Our major finding is that multidisciplinary fall prevention strategies have a statistically significant impact on fall rates with a combined OR of 0.90. While this review demonstrates a significant benefit to multidisciplinary fall prevention strategies in the acute inpatient population, the clinical impact of these efforts may be limited. Based on rates ranging from 1.7 to 9.5 falls per 1000‐patient days, multidisciplinary interventions would reduce falls by 1 to 10 falls per 10,000‐patient days using the combined OR calculated of 0.9. Using other available incidence data regarding inpatient falls,4, 9 a reasonable baseline frequency to consider would be 8 falls per 1000 patient‐days. Assuming that prevalence, the number needed to treat (NNT) to prevent a single inpatient fall is 1250 patient days. Furthermore, based on available data, only approximately one‐third of these falls result in injury and only a minor fraction of these results in serious injury.9, 1113 The magnitude of this apparent benefit in the context of fall incidence rates raises some concerns about cost‐effectiveness given the high staffing and systems needs that multidisciplinary prevention programs require. This also suggests that there are limitations when using inpatient falls as a measure of healthcare quality given the absence of high‐quality evidence demonstrating a viable solution to the problem. At present, the Center for Medicare and Medicaid services limit reimbursement for fall‐related injuries if they occur during an acute inpatient hospitalization.28

The complexity of the interventions used may help explain the limited impact. Krauss et al23 examined compliance to their interventions and found less than ideal results. They found only 36.4% of intervention floor patients had maintained a toileting schedule compared to 24.6% on control floors. Additionally, a greater proportion of patients on control floors had a physical or occupational therapy consult, and only 1.8% more patients on intervention floors had walking aids provided. These were all strategies emphasized on the intervention floors. Similarly, Schwendimann et al9 questioned their staff's adherence to protocol after fall prevention committee audits. This may help explain why a potential benefit lost statistical significance with time, based on a natural tendency towards more participation at the beginning of a new policy. Williams et al24 reported only a 64% compliance rate with fall care plan forms and 77% rate of missing information on fall care plans. A multidisciplinary fall prevention study that did not meet inclusion criteria (based on study population) yielded strongly positive results for which the authors commented mostly on changing of the hospital culture surrounding fall prevention as a key to their success.29 Adoptability of a multidisciplinary intervention will clearly impact adherence and the intervention's ultimate effectiveness.

Single intervention strategies, not analyzed in this review, are simpler to execute and adhere to. While these types of interventions may be superior, there is extremely limited data supporting or refuting patient fall benefits in the acute care inpatient population when using simple single interventions. However, some data generated partially on acute care geriatrics wards targeting patient education only showed benefit.30

Dykes et al22 was able to improve compliance rates by removing steps in the process of executing interventions with the FPTK built into the EMR. Importantly, the FPTK was compared against very similar fall prevention strategies, the difference being that patients randomized to the FPTK arm had the assessment and interventions automatically prompted on admission in the EMR. Adherence was measured through Morse Fall Scale completion rates (81% in control units versus 94% in intervention units).22 In many ways, the utility of this study was displaying a fall risk reduction by simply enhancing compliance using health information technology with automated alerts. Additionally, both arms of the study reported low fall rates compared to previously reported data, and there may have been larger benefit seen if the FPTK was compared against no fall prevention strategy. This diminishing of effect size may have been present in all studies reviewed, as usual hospital care commonly includes basic patient safety measures.

Another potential problem with the multidisciplinary fall prevention programs included in the meta‐analysis is the inability to target interventions. Each study employed a fall risk score in an attempt to focus resources on a select group of high‐risk patients. This method is problematic given that countless risk factors for inpatient falls have been identified in the literature. Factors that have been described range from clinical characteristics to laboratory tests.31 The most consistently reproducible patient‐related risks are altered mental status (including cognitive impairment and depression), altered mobility (particularly lower limb weakness), a history of falls, and toileting needs.13, 3236 Less consistency is seen with other traditional risk factors such as age, sedating medication, and length of stay.5, 13, 32, 3638 Attempting to risk‐stratify patients using simple and accurate assessment tools developed from these risk factors has proven to be very difficult. Many tools have been developed based on identified risk factors, but perform very poorly when trying to identify patients who will fall with reasonable specificity and positive predictive value.34, 3944 In fact, it has been demonstrated that using a nurse's judgment, a physician's opinion based on a patient's likelihood to wander or a simple 2‐question tool have all performed better than sophisticated risk calculators.33, 45, 46 Therefore, it is possible that interventions could benefit from including all patients, with de‐emphasis on unproven risk stratification tools.

In contrast to our findings, a modest risk reduction has been demonstrated in several primary articles and meta‐analyses in the subacute, rehabilitation, and long‐term care populations.15, 16, 4750 Additionally, a recent study has described a 63.9% risk reduction in a population that included medical, surgical, psychiatric, and rehabilitation wards.29 One important difference between these settings and the acute inpatient populations may be the amount of time and energy that can be dedicated to fall prevention and overall care planning. Another likely factor is the added challenge of preventing falls in patients with more active medical illnesses. In the acute care setting, a patient's chief complaint may not be completely addressed at the time of first mobilization and ambulation. This may be most relevant in patients who are admitted with syncope, seizure, vertigo, and dehydration.

Our study has several limitations; most notably, the available evidence is limited in quality and quantity. Furthermore, omission of unpublished data may also lead to effect bias, though this would likely be in the direction of ineffective interventions supporting a conclusion that multidisciplinary efforts have had only a small impact on fall rates. Ideally, future studies can limit confounding variables through randomization. However, it is difficult to adequately blind when studying a multidisciplinary fall intervention that depends on patient and provider participation. As a result, none of the papers reviewed met criteria for high quality. However, almost all available data examined in this review came from large sample sizes in which thoughtful interventions were used. Since an inpatient fall will not affect the majority of patients, it was crucial for these studies to recruit a large sample size to have adequate power to detect a difference in fall rates. However, each study used risk assessment tools, which are poor indicators of who will and will not fall in the hospital.34, 39, 42 This may suggest a need for improved risk assessment tools, or be further evidence to include all patients in fall prevention regardless of risk. Quantitative synthesis of multidisciplinary fall interventions has the added limitation of comparing complex, multifaceted treatments that are not perfectly uniform. It is our opinion that interventions are semi‐standardized using the grouping methods employed in Table 3.

Preventing inpatient falls remains a difficult issue to address while convincing data is lacking. Based on current evidence, multidisciplinary fall prevention efforts on acutely ill inpatients show a possible small benefit and should be explored from a cost‐effectiveness standpoint to ensure they garner appropriate investment. Many resources are required to run such teams including nursing staff, equipment, physical and occupational therapy staff, pharmacists, and specialized staff training. We are unaware of any such cost‐effectiveness data available. Effective interventions may be those that maximize compliance through health information technology, maintain staff dedication, increase staff availability, improve risk assessment, or include all patients regardless of calculated fall risk, and take the patient's chief complaint into account in the fall prevention strategy. Where resources are limited, it appears most reasonable to focus on major risk factors for inpatient falls that have independently been shown to be detrimental to outcomes, such as delirium.51 Additionally, using inpatient fall rates as a hospital quality measure may be premature, given the lack of proven efforts to lower fall rates. Multidisciplinary fall prevention efforts on acutely ill inpatients should be further studied using high‐quality, randomized trials. It remains to be seen whether these large programs are cost‐effective, or on balance clinically effective.

Inpatient falls are the most common type of inpatient adverse event,1 persist as a significant problem nationally, and result in patient injury, increased length of stay, healthcare costs, and litigation.27 Inpatient falls remain a main focus of patient safety and a measure of quality in this era of healthcare reform and quality improvement.8 Inpatient fall rates per 1000 patient‐days range from 1.4 to 18.2.4, 9 The absolute percentage of inpatients that fall ranges from 1.3% to 7%.4, 5, 9, 10 Of inpatient falls, almost all data suggest that roughly one‐third result in some type of injury while 3%‐8% result in serious injury or death.9, 1113

Fall prevention interventions have largely been aimed at modifiable risk factors such as getting out of bed with bed alarms, toileting needs with bedside commodes, and reducing delirium through reorientation techniques. There have been several attempts at decreasing fall rates in hospitals surrounding a multidisciplinary, team‐based approach. Two Cochrane reviews and 2 meta‐analyses have partially examined this issue with mixed results.1417 However, none of these reviews focused on the acute care inpatient population. In fact, the majority of the data analyzed for inpatients was from rehabilitation wards and long‐term care wards. Additionally, there exists almost no data examining fall prevention with single interventions in the acute inpatient population, likely due to the belief that falls are multifactorial in etiology and require more comprehensive interventions.

The aim of this article is to determine the impact of team‐based, multidisciplinary quality improvement efforts to reduce inpatient falls in acute care inpatient hospitals and identify key features that determine their effectiveness.

METHODS

RESULTS

Fall Rates

Dykes et al22 and Williams et al24 found a statistically significant reduction in fall rate with falls reduced by 1.16 per 1000‐patient days and 1.5 per 1000‐patient days, respectively. Mitchell and Jones25 demonstrated a large fall reduction but had an extremely small sample size. Brandis21 found an extremely small reduction in fall rates and failed to report a P‐value. Krauss et al23 showed a trend towards reducing falls, and even showed a statistically significant reduction over the first 5 months of the study, but lost significance in the final 4 months. Similarly, Schwendimann et al9 saw more impressive fall reductions in the first year of the study that dissipated in the final 3 years of data collection.

Results from the meta‐analysis of the 6 studies comparing odds ratios are displayed quantitatively and as a forest plot in Figure 1. The figure shows results with 95% CI for each individual study and overall. There was no statistical evidence of heterogeneity between the studies or study designs. Although, due to the small number of studies included, there is poor power to detect true heterogeneity among studies. The magnitude of boxes shown is a relative sample size indicator. Using the random‐effects model, the summary odds ratio is 0.90 (95% CI, 0.83 to 0.99) (P = 0.02) (I² = 0%).27

Figure 1

DISCUSSION

The frequency and morbidity associated with inpatient falls is well established, based on reproduced epidemiologic data. Reducing these adverse events could reduce morbidity, mortality, and healthcare costs, and has become the focus of most hospitals quality and patient safety initiatives. The focus of this review was to examine multidisciplinary efforts to reduce falls in acute care inpatient hospitals. Despite the importance and scope of the problem, there is a paucity of research available on this topic, with a wide literature search yielding only 6 primary research studies.

Our major finding is that multidisciplinary fall prevention strategies have a statistically significant impact on fall rates with a combined OR of 0.90. While this review demonstrates a significant benefit to multidisciplinary fall prevention strategies in the acute inpatient population, the clinical impact of these efforts may be limited. Based on rates ranging from 1.7 to 9.5 falls per 1000‐patient days, multidisciplinary interventions would reduce falls by 1 to 10 falls per 10,000‐patient days using the combined OR calculated of 0.9. Using other available incidence data regarding inpatient falls,4, 9 a reasonable baseline frequency to consider would be 8 falls per 1000 patient‐days. Assuming that prevalence, the number needed to treat (NNT) to prevent a single inpatient fall is 1250 patient days. Furthermore, based on available data, only approximately one‐third of these falls result in injury and only a minor fraction of these results in serious injury.9, 1113 The magnitude of this apparent benefit in the context of fall incidence rates raises some concerns about cost‐effectiveness given the high staffing and systems needs that multidisciplinary prevention programs require. This also suggests that there are limitations when using inpatient falls as a measure of healthcare quality given the absence of high‐quality evidence demonstrating a viable solution to the problem. At present, the Center for Medicare and Medicaid services limit reimbursement for fall‐related injuries if they occur during an acute inpatient hospitalization.28

The complexity of the interventions used may help explain the limited impact. Krauss et al23 examined compliance to their interventions and found less than ideal results. They found only 36.4% of intervention floor patients had maintained a toileting schedule compared to 24.6% on control floors. Additionally, a greater proportion of patients on control floors had a physical or occupational therapy consult, and only 1.8% more patients on intervention floors had walking aids provided. These were all strategies emphasized on the intervention floors. Similarly, Schwendimann et al9 questioned their staff's adherence to protocol after fall prevention committee audits. This may help explain why a potential benefit lost statistical significance with time, based on a natural tendency towards more participation at the beginning of a new policy. Williams et al24 reported only a 64% compliance rate with fall care plan forms and 77% rate of missing information on fall care plans. A multidisciplinary fall prevention study that did not meet inclusion criteria (based on study population) yielded strongly positive results for which the authors commented mostly on changing of the hospital culture surrounding fall prevention as a key to their success.29 Adoptability of a multidisciplinary intervention will clearly impact adherence and the intervention's ultimate effectiveness.

Single intervention strategies, not analyzed in this review, are simpler to execute and adhere to. While these types of interventions may be superior, there is extremely limited data supporting or refuting patient fall benefits in the acute care inpatient population when using simple single interventions. However, some data generated partially on acute care geriatrics wards targeting patient education only showed benefit.30

Dykes et al22 was able to improve compliance rates by removing steps in the process of executing interventions with the FPTK built into the EMR. Importantly, the FPTK was compared against very similar fall prevention strategies, the difference being that patients randomized to the FPTK arm had the assessment and interventions automatically prompted on admission in the EMR. Adherence was measured through Morse Fall Scale completion rates (81% in control units versus 94% in intervention units).22 In many ways, the utility of this study was displaying a fall risk reduction by simply enhancing compliance using health information technology with automated alerts. Additionally, both arms of the study reported low fall rates compared to previously reported data, and there may have been larger benefit seen if the FPTK was compared against no fall prevention strategy. This diminishing of effect size may have been present in all studies reviewed, as usual hospital care commonly includes basic patient safety measures.

Another potential problem with the multidisciplinary fall prevention programs included in the meta‐analysis is the inability to target interventions. Each study employed a fall risk score in an attempt to focus resources on a select group of high‐risk patients. This method is problematic given that countless risk factors for inpatient falls have been identified in the literature. Factors that have been described range from clinical characteristics to laboratory tests.31 The most consistently reproducible patient‐related risks are altered mental status (including cognitive impairment and depression), altered mobility (particularly lower limb weakness), a history of falls, and toileting needs.13, 3236 Less consistency is seen with other traditional risk factors such as age, sedating medication, and length of stay.5, 13, 32, 3638 Attempting to risk‐stratify patients using simple and accurate assessment tools developed from these risk factors has proven to be very difficult. Many tools have been developed based on identified risk factors, but perform very poorly when trying to identify patients who will fall with reasonable specificity and positive predictive value.34, 3944 In fact, it has been demonstrated that using a nurse's judgment, a physician's opinion based on a patient's likelihood to wander or a simple 2‐question tool have all performed better than sophisticated risk calculators.33, 45, 46 Therefore, it is possible that interventions could benefit from including all patients, with de‐emphasis on unproven risk stratification tools.

In contrast to our findings, a modest risk reduction has been demonstrated in several primary articles and meta‐analyses in the subacute, rehabilitation, and long‐term care populations.15, 16, 4750 Additionally, a recent study has described a 63.9% risk reduction in a population that included medical, surgical, psychiatric, and rehabilitation wards.29 One important difference between these settings and the acute inpatient populations may be the amount of time and energy that can be dedicated to fall prevention and overall care planning. Another likely factor is the added challenge of preventing falls in patients with more active medical illnesses. In the acute care setting, a patient's chief complaint may not be completely addressed at the time of first mobilization and ambulation. This may be most relevant in patients who are admitted with syncope, seizure, vertigo, and dehydration.

Our study has several limitations; most notably, the available evidence is limited in quality and quantity. Furthermore, omission of unpublished data may also lead to effect bias, though this would likely be in the direction of ineffective interventions supporting a conclusion that multidisciplinary efforts have had only a small impact on fall rates. Ideally, future studies can limit confounding variables through randomization. However, it is difficult to adequately blind when studying a multidisciplinary fall intervention that depends on patient and provider participation. As a result, none of the papers reviewed met criteria for high quality. However, almost all available data examined in this review came from large sample sizes in which thoughtful interventions were used. Since an inpatient fall will not affect the majority of patients, it was crucial for these studies to recruit a large sample size to have adequate power to detect a difference in fall rates. However, each study used risk assessment tools, which are poor indicators of who will and will not fall in the hospital.34, 39, 42 This may suggest a need for improved risk assessment tools, or be further evidence to include all patients in fall prevention regardless of risk. Quantitative synthesis of multidisciplinary fall interventions has the added limitation of comparing complex, multifaceted treatments that are not perfectly uniform. It is our opinion that interventions are semi‐standardized using the grouping methods employed in Table 3.

Preventing inpatient falls remains a difficult issue to address while convincing data is lacking. Based on current evidence, multidisciplinary fall prevention efforts on acutely ill inpatients show a possible small benefit and should be explored from a cost‐effectiveness standpoint to ensure they garner appropriate investment. Many resources are required to run such teams including nursing staff, equipment, physical and occupational therapy staff, pharmacists, and specialized staff training. We are unaware of any such cost‐effectiveness data available. Effective interventions may be those that maximize compliance through health information technology, maintain staff dedication, increase staff availability, improve risk assessment, or include all patients regardless of calculated fall risk, and take the patient's chief complaint into account in the fall prevention strategy. Where resources are limited, it appears most reasonable to focus on major risk factors for inpatient falls that have independently been shown to be detrimental to outcomes, such as delirium.51 Additionally, using inpatient fall rates as a hospital quality measure may be premature, given the lack of proven efforts to lower fall rates. Multidisciplinary fall prevention efforts on acutely ill inpatients should be further studied using high‐quality, randomized trials. It remains to be seen whether these large programs are cost‐effective, or on balance clinically effective.