Jacques Donzé, MD, MSc

The need to improve end‐of‐life care is well recognized. Its quality is often poor, and its cost is enormous, with 30% of the Medicare expenditures used for medical treatments of the 6% of beneficiaries who die each year.[1, 2] Repeated hospitalizations are frequent toward the end of life,[3] where each admission should be viewed as an opportunity to initiate advance care planning to improve end‐of‐life care and possibly reduce future unnecessary readmissions.[4, 5] Identified problems include undertreatment of pain, lack of awareness of patient wishes or advance directives, and unwanted overtreatment.

To improve quality and reduce unnecessary hospital use near the end of life, there is an urgent need to help healthcare providers to better identify the most vulnerable and at‐risk patients to provide them with care coordination and supportive care services. We aimed to identify the risk factors for having a 30‐day potentially avoidable readmission (PAR) due to end‐of‐life care issues.

METHODS

RESULTS

From the total of 12,383 patients who were discharged from the medical services of the Brigham and Women's Hospital during the study period, 2108 (17.0%) were excluded because of: (1) death before discharge, (2) transfer to another acute care hospital, (3) discharge against medical advice, or (4) missing data (Figure 1). Among the 10,275 eligible admissions, 22.3% (n=2301) were followed by a 30‐day readmission. Of these, 826 (8.0% of all admissions) were identified as potentially avoidable. Among a random sample of 534 PARs, 80 (15.0%) were related to end‐of‐life care issues (cases). Of note, only 16 (20%) of these patients received palliative care consultation during the index hospitalization. A total of 7974 discharges were not followed by any 30‐day readmission (controls).

Figure 1

Baseline characteristics are presented in Table 1. Among the combined cohort of cases plus controls, the patient's mean age at inclusion was 61.3 years, and about half were male. In bivariate analysis, demographics such as age and sex were similar between cases and controls. Cases had more hospitalizations in the previous year, a higher number of medications at discharge, and a higher Elixhauser comorbidity index. When looking at diseases more specifically, neoplasm was significantly associated with potentially avoidable 30‐day readmission due to end‐of‐life care issues. In contrast, end‐stage renal disease was associated with a significantly lower risk of 30‐day PAR due to end‐of‐life care issues.

In multivariate analysis, 4 factors remained significantly associated with 30‐day PAR due to end‐of‐life care issues (Table 2). Neoplasm was the strongest risk factor, with an odds ratio of 5.6 (95% confidence interval: 2.8511.0), followed by opiate medication use, Elixhauser score, and number of admissions in the previous 12 months.

The model, including all 4 variables, had an excellent discrimination power, with a C statistic of 0.85. Without the Elixhauser score, the C statistic remained very high, with a value of 0.82.

DISCUSSION

In a large medical population, potentially avoidable readmissions due to end‐of‐life care issues were not uncommon: 15% of all potentially avoidable readmissions (1.2% of all discharges). We identified 4 main risk factors for having a 30‐day potentially avoidable readmission due to end‐of‐life care issues: neoplasm, opiate use, Elixhauser comorbidity index, and number of admissions in the previous year. In a model that includes these 4 variables, the discrimination was very high with a C statistic of 0.85.

This study extends prior work indicating some risk factors for the need for palliative care. Neoplasm has been logically identified as a criterion for palliative care assessment at the time of admission.[13] Patients with neoplasm are not only at overall high risk for readmission,[14, 15, 16] but they obviously represent a fragile population whose condition is often terminal. Our results suggest that still more attention may be necessary to reduce the risk of readmission due to end‐of‐life care issues in this population (for example, only 20% of cases in our study received palliative care consultation during the index hospitalization). The overall comorbidity measured by the Elixhauser index was not surprisingly a significant risk factor. It probably accounts for the burden of comorbidities, but also for other advanced diseases besides neoplasm, like heart failure, chronic obstructive pulmonary disease, and others that may also be terminal. The number of previous hospital admissions in the past year is also an important risk factor, not only for the general population,[10, 11, 14, 17, 18] but also for patients with more advanced conditions,[19, 20, 21, 22] where admissions become more frequent as the disease progresses toward end stage. Opioid use was the final statistically significant risk factor, specific for this population, likely as a proxy for disease severity and progression toward terminal illness, especially in combination with the other risk factors such as cancer. Age was not a significant factor in either bivariate or multivariate analysis. Previous studies on the risk factors for readmission among patients receiving palliative care also failed to show age as a significant factor.[23, 24] Both of these studies looked at readmissions among patients who were already receiving palliative care. Our study asks a fundamentally different (and in many ways a more practical) question: who among a large population of medical patients might benefit from receiving input from palliative care in the first place. The number of medications at discharge was no longer significant in the multivariate analysis, likely due to its collinearity with the Elixhauser comorbidity index. An increased number of medications might be associated with a higher risk of adverse drug events and readmission, but they would not be necessarily considered to be end‐of‐life readmissions. Taken together, the 4 variables provide a very promising prediction model with high discrimination. To our knowledge, there is no previous existing list of risk factors for 30‐day potentially avoidable readmission due to end‐of‐life care issues, and no existing model to help prioritize palliative care to the most high‐risk patients. It is worth noting that the Elixhauser score might be difficult to calculate before the discharge of the patient (although hospitals with electronic capture of medical problem lists might be able to approximate it). However, even without the Elixhauser score, the C statistic remained very high at 0.82.

Our study has several limitations. Although we looked at readmissions at 2 other affiliated hospitals, some patients might have been readmitted to other acute care facilities outside our network. However, we would not expect the risk factors in these patients to be so different. The identification of end‐of‐life care issues by medical record review is based on a subjective judgment, although strict criteria were used. Furthermore, differentiation between potentially avoidable readmission and unavoidable readmission cannot be perfect. We used clear and logical criteria that were previously validated and allow large database management. Also, we did not analyze a comprehensive list of potential risk factors. It is probable that functional or cognitive status, for example, could also be important risk factors. We purposely chose a set of variables that could be easily obtained from administrative data sources. The small number of cases may have led to limited statistical power to identify less strongly associated risk factors. Last, the results may not be completely generalizable to small or community hospitals, in particular those that may care for less severely ill cancer patients.

Our findings have important implications. End‐of‐life care issues are not infrequent causes of readmission. Our study's findings could help prioritize palliative care resources to those patients at higher risk to improve the quality of end‐of‐life care. The risk factors identified in this study could be used informally by physicians at the bedside to identify such patients. In addition, a hospital could use these factors to provide a second‐level screen, beyond clinician recognition, to assist palliative care teams to identify patients who may not have otherwise been referred. This screen could be automated, for example, by using a list of medical problems from an electronic medical record to approximate an Elixhauser comorbidity score, or even leaving comorbidities out and simply relying on the other 3 easily identifiable risk factors. Such efforts could have a substantial effect on improving care near the end of life and potentially reducing unnecessary hospitalizations.

The need to improve end‐of‐life care is well recognized. Its quality is often poor, and its cost is enormous, with 30% of the Medicare expenditures used for medical treatments of the 6% of beneficiaries who die each year.[1, 2] Repeated hospitalizations are frequent toward the end of life,[3] where each admission should be viewed as an opportunity to initiate advance care planning to improve end‐of‐life care and possibly reduce future unnecessary readmissions.[4, 5] Identified problems include undertreatment of pain, lack of awareness of patient wishes or advance directives, and unwanted overtreatment.

To improve quality and reduce unnecessary hospital use near the end of life, there is an urgent need to help healthcare providers to better identify the most vulnerable and at‐risk patients to provide them with care coordination and supportive care services. We aimed to identify the risk factors for having a 30‐day potentially avoidable readmission (PAR) due to end‐of‐life care issues.

METHODS

RESULTS

From the total of 12,383 patients who were discharged from the medical services of the Brigham and Women's Hospital during the study period, 2108 (17.0%) were excluded because of: (1) death before discharge, (2) transfer to another acute care hospital, (3) discharge against medical advice, or (4) missing data (Figure 1). Among the 10,275 eligible admissions, 22.3% (n=2301) were followed by a 30‐day readmission. Of these, 826 (8.0% of all admissions) were identified as potentially avoidable. Among a random sample of 534 PARs, 80 (15.0%) were related to end‐of‐life care issues (cases). Of note, only 16 (20%) of these patients received palliative care consultation during the index hospitalization. A total of 7974 discharges were not followed by any 30‐day readmission (controls).

Figure 1

Baseline characteristics are presented in Table 1. Among the combined cohort of cases plus controls, the patient's mean age at inclusion was 61.3 years, and about half were male. In bivariate analysis, demographics such as age and sex were similar between cases and controls. Cases had more hospitalizations in the previous year, a higher number of medications at discharge, and a higher Elixhauser comorbidity index. When looking at diseases more specifically, neoplasm was significantly associated with potentially avoidable 30‐day readmission due to end‐of‐life care issues. In contrast, end‐stage renal disease was associated with a significantly lower risk of 30‐day PAR due to end‐of‐life care issues.

In multivariate analysis, 4 factors remained significantly associated with 30‐day PAR due to end‐of‐life care issues (Table 2). Neoplasm was the strongest risk factor, with an odds ratio of 5.6 (95% confidence interval: 2.8511.0), followed by opiate medication use, Elixhauser score, and number of admissions in the previous 12 months.

The model, including all 4 variables, had an excellent discrimination power, with a C statistic of 0.85. Without the Elixhauser score, the C statistic remained very high, with a value of 0.82.

DISCUSSION

In a large medical population, potentially avoidable readmissions due to end‐of‐life care issues were not uncommon: 15% of all potentially avoidable readmissions (1.2% of all discharges). We identified 4 main risk factors for having a 30‐day potentially avoidable readmission due to end‐of‐life care issues: neoplasm, opiate use, Elixhauser comorbidity index, and number of admissions in the previous year. In a model that includes these 4 variables, the discrimination was very high with a C statistic of 0.85.

This study extends prior work indicating some risk factors for the need for palliative care. Neoplasm has been logically identified as a criterion for palliative care assessment at the time of admission.[13] Patients with neoplasm are not only at overall high risk for readmission,[14, 15, 16] but they obviously represent a fragile population whose condition is often terminal. Our results suggest that still more attention may be necessary to reduce the risk of readmission due to end‐of‐life care issues in this population (for example, only 20% of cases in our study received palliative care consultation during the index hospitalization). The overall comorbidity measured by the Elixhauser index was not surprisingly a significant risk factor. It probably accounts for the burden of comorbidities, but also for other advanced diseases besides neoplasm, like heart failure, chronic obstructive pulmonary disease, and others that may also be terminal. The number of previous hospital admissions in the past year is also an important risk factor, not only for the general population,[10, 11, 14, 17, 18] but also for patients with more advanced conditions,[19, 20, 21, 22] where admissions become more frequent as the disease progresses toward end stage. Opioid use was the final statistically significant risk factor, specific for this population, likely as a proxy for disease severity and progression toward terminal illness, especially in combination with the other risk factors such as cancer. Age was not a significant factor in either bivariate or multivariate analysis. Previous studies on the risk factors for readmission among patients receiving palliative care also failed to show age as a significant factor.[23, 24] Both of these studies looked at readmissions among patients who were already receiving palliative care. Our study asks a fundamentally different (and in many ways a more practical) question: who among a large population of medical patients might benefit from receiving input from palliative care in the first place. The number of medications at discharge was no longer significant in the multivariate analysis, likely due to its collinearity with the Elixhauser comorbidity index. An increased number of medications might be associated with a higher risk of adverse drug events and readmission, but they would not be necessarily considered to be end‐of‐life readmissions. Taken together, the 4 variables provide a very promising prediction model with high discrimination. To our knowledge, there is no previous existing list of risk factors for 30‐day potentially avoidable readmission due to end‐of‐life care issues, and no existing model to help prioritize palliative care to the most high‐risk patients. It is worth noting that the Elixhauser score might be difficult to calculate before the discharge of the patient (although hospitals with electronic capture of medical problem lists might be able to approximate it). However, even without the Elixhauser score, the C statistic remained very high at 0.82.

Our study has several limitations. Although we looked at readmissions at 2 other affiliated hospitals, some patients might have been readmitted to other acute care facilities outside our network. However, we would not expect the risk factors in these patients to be so different. The identification of end‐of‐life care issues by medical record review is based on a subjective judgment, although strict criteria were used. Furthermore, differentiation between potentially avoidable readmission and unavoidable readmission cannot be perfect. We used clear and logical criteria that were previously validated and allow large database management. Also, we did not analyze a comprehensive list of potential risk factors. It is probable that functional or cognitive status, for example, could also be important risk factors. We purposely chose a set of variables that could be easily obtained from administrative data sources. The small number of cases may have led to limited statistical power to identify less strongly associated risk factors. Last, the results may not be completely generalizable to small or community hospitals, in particular those that may care for less severely ill cancer patients.

Our findings have important implications. End‐of‐life care issues are not infrequent causes of readmission. Our study's findings could help prioritize palliative care resources to those patients at higher risk to improve the quality of end‐of‐life care. The risk factors identified in this study could be used informally by physicians at the bedside to identify such patients. In addition, a hospital could use these factors to provide a second‐level screen, beyond clinician recognition, to assist palliative care teams to identify patients who may not have otherwise been referred. This screen could be automated, for example, by using a list of medical problems from an electronic medical record to approximate an Elixhauser comorbidity score, or even leaving comorbidities out and simply relying on the other 3 easily identifiable risk factors. Such efforts could have a substantial effect on improving care near the end of life and potentially reducing unnecessary hospitalizations.

The need to improve end‐of‐life care is well recognized. Its quality is often poor, and its cost is enormous, with 30% of the Medicare expenditures used for medical treatments of the 6% of beneficiaries who die each year.[1, 2] Repeated hospitalizations are frequent toward the end of life,[3] where each admission should be viewed as an opportunity to initiate advance care planning to improve end‐of‐life care and possibly reduce future unnecessary readmissions.[4, 5] Identified problems include undertreatment of pain, lack of awareness of patient wishes or advance directives, and unwanted overtreatment.

To improve quality and reduce unnecessary hospital use near the end of life, there is an urgent need to help healthcare providers to better identify the most vulnerable and at‐risk patients to provide them with care coordination and supportive care services. We aimed to identify the risk factors for having a 30‐day potentially avoidable readmission (PAR) due to end‐of‐life care issues.

METHODS

RESULTS

From the total of 12,383 patients who were discharged from the medical services of the Brigham and Women's Hospital during the study period, 2108 (17.0%) were excluded because of: (1) death before discharge, (2) transfer to another acute care hospital, (3) discharge against medical advice, or (4) missing data (Figure 1). Among the 10,275 eligible admissions, 22.3% (n=2301) were followed by a 30‐day readmission. Of these, 826 (8.0% of all admissions) were identified as potentially avoidable. Among a random sample of 534 PARs, 80 (15.0%) were related to end‐of‐life care issues (cases). Of note, only 16 (20%) of these patients received palliative care consultation during the index hospitalization. A total of 7974 discharges were not followed by any 30‐day readmission (controls).

Figure 1

Baseline characteristics are presented in Table 1. Among the combined cohort of cases plus controls, the patient's mean age at inclusion was 61.3 years, and about half were male. In bivariate analysis, demographics such as age and sex were similar between cases and controls. Cases had more hospitalizations in the previous year, a higher number of medications at discharge, and a higher Elixhauser comorbidity index. When looking at diseases more specifically, neoplasm was significantly associated with potentially avoidable 30‐day readmission due to end‐of‐life care issues. In contrast, end‐stage renal disease was associated with a significantly lower risk of 30‐day PAR due to end‐of‐life care issues.

In multivariate analysis, 4 factors remained significantly associated with 30‐day PAR due to end‐of‐life care issues (Table 2). Neoplasm was the strongest risk factor, with an odds ratio of 5.6 (95% confidence interval: 2.8511.0), followed by opiate medication use, Elixhauser score, and number of admissions in the previous 12 months.

The model, including all 4 variables, had an excellent discrimination power, with a C statistic of 0.85. Without the Elixhauser score, the C statistic remained very high, with a value of 0.82.

DISCUSSION

In a large medical population, potentially avoidable readmissions due to end‐of‐life care issues were not uncommon: 15% of all potentially avoidable readmissions (1.2% of all discharges). We identified 4 main risk factors for having a 30‐day potentially avoidable readmission due to end‐of‐life care issues: neoplasm, opiate use, Elixhauser comorbidity index, and number of admissions in the previous year. In a model that includes these 4 variables, the discrimination was very high with a C statistic of 0.85.

This study extends prior work indicating some risk factors for the need for palliative care. Neoplasm has been logically identified as a criterion for palliative care assessment at the time of admission.[13] Patients with neoplasm are not only at overall high risk for readmission,[14, 15, 16] but they obviously represent a fragile population whose condition is often terminal. Our results suggest that still more attention may be necessary to reduce the risk of readmission due to end‐of‐life care issues in this population (for example, only 20% of cases in our study received palliative care consultation during the index hospitalization). The overall comorbidity measured by the Elixhauser index was not surprisingly a significant risk factor. It probably accounts for the burden of comorbidities, but also for other advanced diseases besides neoplasm, like heart failure, chronic obstructive pulmonary disease, and others that may also be terminal. The number of previous hospital admissions in the past year is also an important risk factor, not only for the general population,[10, 11, 14, 17, 18] but also for patients with more advanced conditions,[19, 20, 21, 22] where admissions become more frequent as the disease progresses toward end stage. Opioid use was the final statistically significant risk factor, specific for this population, likely as a proxy for disease severity and progression toward terminal illness, especially in combination with the other risk factors such as cancer. Age was not a significant factor in either bivariate or multivariate analysis. Previous studies on the risk factors for readmission among patients receiving palliative care also failed to show age as a significant factor.[23, 24] Both of these studies looked at readmissions among patients who were already receiving palliative care. Our study asks a fundamentally different (and in many ways a more practical) question: who among a large population of medical patients might benefit from receiving input from palliative care in the first place. The number of medications at discharge was no longer significant in the multivariate analysis, likely due to its collinearity with the Elixhauser comorbidity index. An increased number of medications might be associated with a higher risk of adverse drug events and readmission, but they would not be necessarily considered to be end‐of‐life readmissions. Taken together, the 4 variables provide a very promising prediction model with high discrimination. To our knowledge, there is no previous existing list of risk factors for 30‐day potentially avoidable readmission due to end‐of‐life care issues, and no existing model to help prioritize palliative care to the most high‐risk patients. It is worth noting that the Elixhauser score might be difficult to calculate before the discharge of the patient (although hospitals with electronic capture of medical problem lists might be able to approximate it). However, even without the Elixhauser score, the C statistic remained very high at 0.82.

Our study has several limitations. Although we looked at readmissions at 2 other affiliated hospitals, some patients might have been readmitted to other acute care facilities outside our network. However, we would not expect the risk factors in these patients to be so different. The identification of end‐of‐life care issues by medical record review is based on a subjective judgment, although strict criteria were used. Furthermore, differentiation between potentially avoidable readmission and unavoidable readmission cannot be perfect. We used clear and logical criteria that were previously validated and allow large database management. Also, we did not analyze a comprehensive list of potential risk factors. It is probable that functional or cognitive status, for example, could also be important risk factors. We purposely chose a set of variables that could be easily obtained from administrative data sources. The small number of cases may have led to limited statistical power to identify less strongly associated risk factors. Last, the results may not be completely generalizable to small or community hospitals, in particular those that may care for less severely ill cancer patients.

Our findings have important implications. End‐of‐life care issues are not infrequent causes of readmission. Our study's findings could help prioritize palliative care resources to those patients at higher risk to improve the quality of end‐of‐life care. The risk factors identified in this study could be used informally by physicians at the bedside to identify such patients. In addition, a hospital could use these factors to provide a second‐level screen, beyond clinician recognition, to assist palliative care teams to identify patients who may not have otherwise been referred. This screen could be automated, for example, by using a list of medical problems from an electronic medical record to approximate an Elixhauser comorbidity score, or even leaving comorbidities out and simply relying on the other 3 easily identifiable risk factors. Such efforts could have a substantial effect on improving care near the end of life and potentially reducing unnecessary hospitalizations.

Readmissions to the hospital are common and costly.[1] However, identifying patients prospectively who are likely to be readmitted and who may benefit from interventions to reduce readmission risk has proven challenging, with published risk scores having only moderate ability to discriminate between patients likely and unlikely to be readmitted.[2] One reason for this may be that published studies have not typically focused on patients who are cognitively impaired, psychiatrically ill, have low health or English literacy, or have poor social supports, all of whom may represent a substantial fraction of readmitted patients.[2, 3, 4, 5]

Psychiatric disease, in particular, may contribute to increased readmission risk for nonpsychiatric (medical) illness, and is associated with increased utilization of healthcare resources.[6, 7, 8, 9, 10, 11] For example, patients with mental illness who were discharged from New York hospitals were more likely to be rehospitalized and had more costly readmissions than patients without these comorbidities, including a length of stay nearly 1 day longer on average.[7] An unmet need for treatment of substance abuse was projected to cost Tennessee $772 million of excess healthcare costs in 2000, mostly incurred through repeat hospitalizations and emergency department (ED) visits.[10]

Despite this, few investigators have considered the role of psychiatric disease and/or substance abuse in medical readmission risk. The purpose of the current study was to evaluate the role of psychiatric illness and substance abuse in unselected medical patients to determine their relative contributions to 30‐day all‐cause readmissions (ACR) and potentially avoidable readmissions (PAR).

METHODS

RESULTS

There were 7984 unique patients discharged during the study period. Patients were generally white and English speaking; just over half of admissions came from the ED (Table 1). Of note, nearly all patients were insured, as are almost all patients in Massachusetts. They had high degrees of comorbid illness and large numbers of prescribed medications. Nearly 30% had at least 1 hospital admission within the prior year.

DISCUSSION

Comorbid psychiatric illness was common among patients admitted to the medicine wards. Patients with documented discharge diagnoses of depression or schizophrenia were at highest risk for a potentially avoidable 30‐day readmission, whereas those prescribed more psychiatric medications were at increased risk for ACR. These findings were independent of a comprehensive set of risk factors among medicine inpatients in this retrospective cohort study.

This study extends prior work indicating patients with psychiatric disease have increased healthcare utilization,[6, 7, 8, 9, 10, 11] by identifying at least 2 subpopulations of the psychiatrically ill (those with depression and schizophrenia) at particularly high risk for 30‐day PAR. To our knowledge, this is the first study to identify schizophrenia as a predictor of hospital readmission for medical illnesses. One prior study prospectively identified depression as increasing the 90‐day risk of readmission 3‐fold, although medication usage was not assessed,[6] and our report strengthens this association.

There are several possible explanations why these two subpopulations in particular would be more predisposed to readmissions that are potentially avoidable. It is known that patients with schizophrenia, for example, live on average 20 years less than the general population, and most of this excess mortality is due to medical illnesses.[20, 21] Reasons for this may include poor healthcare access, adverse effects of medication, and socioeconomic factors among others.[21, 22] All of these reasons may contribute to the increased PAR risk in this population, mediated, for example, by decreased ability to adhere to postdischarge care plans. Successful community‐based interventions to decrease these inequities have been described and could serve as a model for addressing the increased readmission risk in this population.[23]

Our finding that patients with a greater number of prescribed psychiatric medications are at increased risk for ACR may be expected, given other studies that have highlighted the crucial importance of medications in postdischarge adverse events, including readmissions.[24] Indeed, medication‐related errors and toxicities are the most common postdischarge adverse events experienced by patients.[25] Whether psychiatric medications are particularly prone to causing postdischarge adverse events or whether these medications represent greater psychiatric comorbidity cannot be answered by this study.

It was surprising but reassuring that substance abuse was not a predictor of short‐term readmissions as identified using our measures; in fact, a discharge diagnosis of substance abuse was associated with lower risk of ACR than comparator patients. It seems unlikely that we would have inadequate power to find such a result, as we found a statistically significant negative association in the ACR population, and 17% of our population overall was considered likely to have a substance abuse comorbidity. However, it is likely the burden of disease was underestimated given that we did not try to determine the contribution of long‐term substance abuse to medical diseases that may increase readmission risk (eg, liver cirrhosis from alcohol use). Unlike other conditions in our study, patients with substance abuse diagnoses at BWH can be seen by a dedicated multidisciplinary team while an inpatient to start treatment and plan for postdischarge follow‐up; this may have played a role in our findings.

A discharge diagnosis of anxiety was also somewhat protective against readmission, whereas a prescription of an anxiolytic (predominantly benzodiazepines) increased risk; many patients prescribed a benzodiazepine do not have a Diagnostic and Statistical Manual of Mental Disorders4th Edition (DSM‐IV) diagnosis of anxiety disorder, and thus these findings may reflect different patient populations. Discharging physicians may have used anxiety as a discharge diagnosis in patients in whom they suspected somatic complaints without organic basis; these patients may be at lower risk of readmission.

Discharge diagnoses of psychiatric illnesses were associated with ACR and PAR in our study, but outpatient diagnoses were not. This likely reflects greater severity of illness (documentation as a treated diagnosis on discharge indicates the illness was relevant during the hospitalization), but may also reflect inaccuracies of diagnosis and lack of assessment of severity in outpatient coding, which would bias toward null findings. Although many of the patients in our study were seen by primary care doctors within the Partners system, some patients had outside primary care physicians and we did not have access to these records. This may also have decreased our ability to find associations.

The findings of our study should be interpreted in the context of the study design. Our study was retrospective, which limited our ability to conclusively diagnose psychiatric disease presence or severity (as is true of most institutions, validated psychiatric screening was not routinely used at our institutions on hospital admission or discharge). However, we used a conservative scale to classify the likelihood of patients having psychiatric or substance abuse disorders, and we used other metrics to establish the presence of illness, such as the number of prescribed medications, inpatient consultation with a substance abuse service, and hospital discharge diagnoses. This approach also allowed us to quickly identify a large cohort unaffected by selection bias. Our study was single center, potentially limiting generalizability. Although we capture at least 80% of readmissions, we were not able to capture all readmissions, and we cannot rule out that patients readmitted elsewhere are different than those readmitted within the Partners system. Last, the SQLape algorithm is not perfectly sensitive or specific in identifying avoidable readmissions,[13] but it does eliminate many readmissions that are clearly unavoidable, creating an enriched cohort of patients whose readmissions are more likely to be avoidable and therefore potentially actionable.

We suggest that our study findings first be considered when risk stratifying patients before hospital discharge in terms of readmission risk. Patients with depression and schizophrenia would seem to merit postdischarge interventions to decrease their potentially avoidable readmissions. Compulsory community treatment (a feature of treatment in Canada and Australia that is ordered by clinicians) has been shown to decrease mortality due to medical illness in patients who have been hospitalized and are psychiatrically ill, and addition of these services to postdischarge care may be useful.[23] Inpatient physicians could work to ensure follow‐up not just with medical providers but with robust outpatient mental health programs to decrease potentially avoidable readmission risk, and administrators could work to ensure close linkages with these community resources. Studies evaluating the impact of these types of interventions would need to be conducted. Patients with polypharmacy, including psychiatric medications, may benefit from interventions to improve medication safety, such as enhanced medication reconciliation and pharmacist counseling.[26]

Our study suggests that patients with depression, those with schizophrenia, and those who have increased numbers of prescribed psychiatric medications should be considered at high risk for readmission for medical illnesses. Targeting interventions to these patients may be fruitful in preventing avoidable readmissions.

Readmissions to the hospital are common and costly.[1] However, identifying patients prospectively who are likely to be readmitted and who may benefit from interventions to reduce readmission risk has proven challenging, with published risk scores having only moderate ability to discriminate between patients likely and unlikely to be readmitted.[2] One reason for this may be that published studies have not typically focused on patients who are cognitively impaired, psychiatrically ill, have low health or English literacy, or have poor social supports, all of whom may represent a substantial fraction of readmitted patients.[2, 3, 4, 5]

Psychiatric disease, in particular, may contribute to increased readmission risk for nonpsychiatric (medical) illness, and is associated with increased utilization of healthcare resources.[6, 7, 8, 9, 10, 11] For example, patients with mental illness who were discharged from New York hospitals were more likely to be rehospitalized and had more costly readmissions than patients without these comorbidities, including a length of stay nearly 1 day longer on average.[7] An unmet need for treatment of substance abuse was projected to cost Tennessee $772 million of excess healthcare costs in 2000, mostly incurred through repeat hospitalizations and emergency department (ED) visits.[10]

Despite this, few investigators have considered the role of psychiatric disease and/or substance abuse in medical readmission risk. The purpose of the current study was to evaluate the role of psychiatric illness and substance abuse in unselected medical patients to determine their relative contributions to 30‐day all‐cause readmissions (ACR) and potentially avoidable readmissions (PAR).

METHODS

RESULTS

There were 7984 unique patients discharged during the study period. Patients were generally white and English speaking; just over half of admissions came from the ED (Table 1). Of note, nearly all patients were insured, as are almost all patients in Massachusetts. They had high degrees of comorbid illness and large numbers of prescribed medications. Nearly 30% had at least 1 hospital admission within the prior year.

DISCUSSION

Comorbid psychiatric illness was common among patients admitted to the medicine wards. Patients with documented discharge diagnoses of depression or schizophrenia were at highest risk for a potentially avoidable 30‐day readmission, whereas those prescribed more psychiatric medications were at increased risk for ACR. These findings were independent of a comprehensive set of risk factors among medicine inpatients in this retrospective cohort study.

This study extends prior work indicating patients with psychiatric disease have increased healthcare utilization,[6, 7, 8, 9, 10, 11] by identifying at least 2 subpopulations of the psychiatrically ill (those with depression and schizophrenia) at particularly high risk for 30‐day PAR. To our knowledge, this is the first study to identify schizophrenia as a predictor of hospital readmission for medical illnesses. One prior study prospectively identified depression as increasing the 90‐day risk of readmission 3‐fold, although medication usage was not assessed,[6] and our report strengthens this association.

There are several possible explanations why these two subpopulations in particular would be more predisposed to readmissions that are potentially avoidable. It is known that patients with schizophrenia, for example, live on average 20 years less than the general population, and most of this excess mortality is due to medical illnesses.[20, 21] Reasons for this may include poor healthcare access, adverse effects of medication, and socioeconomic factors among others.[21, 22] All of these reasons may contribute to the increased PAR risk in this population, mediated, for example, by decreased ability to adhere to postdischarge care plans. Successful community‐based interventions to decrease these inequities have been described and could serve as a model for addressing the increased readmission risk in this population.[23]

Our finding that patients with a greater number of prescribed psychiatric medications are at increased risk for ACR may be expected, given other studies that have highlighted the crucial importance of medications in postdischarge adverse events, including readmissions.[24] Indeed, medication‐related errors and toxicities are the most common postdischarge adverse events experienced by patients.[25] Whether psychiatric medications are particularly prone to causing postdischarge adverse events or whether these medications represent greater psychiatric comorbidity cannot be answered by this study.

It was surprising but reassuring that substance abuse was not a predictor of short‐term readmissions as identified using our measures; in fact, a discharge diagnosis of substance abuse was associated with lower risk of ACR than comparator patients. It seems unlikely that we would have inadequate power to find such a result, as we found a statistically significant negative association in the ACR population, and 17% of our population overall was considered likely to have a substance abuse comorbidity. However, it is likely the burden of disease was underestimated given that we did not try to determine the contribution of long‐term substance abuse to medical diseases that may increase readmission risk (eg, liver cirrhosis from alcohol use). Unlike other conditions in our study, patients with substance abuse diagnoses at BWH can be seen by a dedicated multidisciplinary team while an inpatient to start treatment and plan for postdischarge follow‐up; this may have played a role in our findings.

A discharge diagnosis of anxiety was also somewhat protective against readmission, whereas a prescription of an anxiolytic (predominantly benzodiazepines) increased risk; many patients prescribed a benzodiazepine do not have a Diagnostic and Statistical Manual of Mental Disorders4th Edition (DSM‐IV) diagnosis of anxiety disorder, and thus these findings may reflect different patient populations. Discharging physicians may have used anxiety as a discharge diagnosis in patients in whom they suspected somatic complaints without organic basis; these patients may be at lower risk of readmission.

Discharge diagnoses of psychiatric illnesses were associated with ACR and PAR in our study, but outpatient diagnoses were not. This likely reflects greater severity of illness (documentation as a treated diagnosis on discharge indicates the illness was relevant during the hospitalization), but may also reflect inaccuracies of diagnosis and lack of assessment of severity in outpatient coding, which would bias toward null findings. Although many of the patients in our study were seen by primary care doctors within the Partners system, some patients had outside primary care physicians and we did not have access to these records. This may also have decreased our ability to find associations.

The findings of our study should be interpreted in the context of the study design. Our study was retrospective, which limited our ability to conclusively diagnose psychiatric disease presence or severity (as is true of most institutions, validated psychiatric screening was not routinely used at our institutions on hospital admission or discharge). However, we used a conservative scale to classify the likelihood of patients having psychiatric or substance abuse disorders, and we used other metrics to establish the presence of illness, such as the number of prescribed medications, inpatient consultation with a substance abuse service, and hospital discharge diagnoses. This approach also allowed us to quickly identify a large cohort unaffected by selection bias. Our study was single center, potentially limiting generalizability. Although we capture at least 80% of readmissions, we were not able to capture all readmissions, and we cannot rule out that patients readmitted elsewhere are different than those readmitted within the Partners system. Last, the SQLape algorithm is not perfectly sensitive or specific in identifying avoidable readmissions,[13] but it does eliminate many readmissions that are clearly unavoidable, creating an enriched cohort of patients whose readmissions are more likely to be avoidable and therefore potentially actionable.

We suggest that our study findings first be considered when risk stratifying patients before hospital discharge in terms of readmission risk. Patients with depression and schizophrenia would seem to merit postdischarge interventions to decrease their potentially avoidable readmissions. Compulsory community treatment (a feature of treatment in Canada and Australia that is ordered by clinicians) has been shown to decrease mortality due to medical illness in patients who have been hospitalized and are psychiatrically ill, and addition of these services to postdischarge care may be useful.[23] Inpatient physicians could work to ensure follow‐up not just with medical providers but with robust outpatient mental health programs to decrease potentially avoidable readmission risk, and administrators could work to ensure close linkages with these community resources. Studies evaluating the impact of these types of interventions would need to be conducted. Patients with polypharmacy, including psychiatric medications, may benefit from interventions to improve medication safety, such as enhanced medication reconciliation and pharmacist counseling.[26]

Our study suggests that patients with depression, those with schizophrenia, and those who have increased numbers of prescribed psychiatric medications should be considered at high risk for readmission for medical illnesses. Targeting interventions to these patients may be fruitful in preventing avoidable readmissions.

Readmissions to the hospital are common and costly.[1] However, identifying patients prospectively who are likely to be readmitted and who may benefit from interventions to reduce readmission risk has proven challenging, with published risk scores having only moderate ability to discriminate between patients likely and unlikely to be readmitted.[2] One reason for this may be that published studies have not typically focused on patients who are cognitively impaired, psychiatrically ill, have low health or English literacy, or have poor social supports, all of whom may represent a substantial fraction of readmitted patients.[2, 3, 4, 5]

Psychiatric disease, in particular, may contribute to increased readmission risk for nonpsychiatric (medical) illness, and is associated with increased utilization of healthcare resources.[6, 7, 8, 9, 10, 11] For example, patients with mental illness who were discharged from New York hospitals were more likely to be rehospitalized and had more costly readmissions than patients without these comorbidities, including a length of stay nearly 1 day longer on average.[7] An unmet need for treatment of substance abuse was projected to cost Tennessee $772 million of excess healthcare costs in 2000, mostly incurred through repeat hospitalizations and emergency department (ED) visits.[10]

Despite this, few investigators have considered the role of psychiatric disease and/or substance abuse in medical readmission risk. The purpose of the current study was to evaluate the role of psychiatric illness and substance abuse in unselected medical patients to determine their relative contributions to 30‐day all‐cause readmissions (ACR) and potentially avoidable readmissions (PAR).

METHODS

RESULTS

There were 7984 unique patients discharged during the study period. Patients were generally white and English speaking; just over half of admissions came from the ED (Table 1). Of note, nearly all patients were insured, as are almost all patients in Massachusetts. They had high degrees of comorbid illness and large numbers of prescribed medications. Nearly 30% had at least 1 hospital admission within the prior year.

DISCUSSION

Comorbid psychiatric illness was common among patients admitted to the medicine wards. Patients with documented discharge diagnoses of depression or schizophrenia were at highest risk for a potentially avoidable 30‐day readmission, whereas those prescribed more psychiatric medications were at increased risk for ACR. These findings were independent of a comprehensive set of risk factors among medicine inpatients in this retrospective cohort study.

This study extends prior work indicating patients with psychiatric disease have increased healthcare utilization,[6, 7, 8, 9, 10, 11] by identifying at least 2 subpopulations of the psychiatrically ill (those with depression and schizophrenia) at particularly high risk for 30‐day PAR. To our knowledge, this is the first study to identify schizophrenia as a predictor of hospital readmission for medical illnesses. One prior study prospectively identified depression as increasing the 90‐day risk of readmission 3‐fold, although medication usage was not assessed,[6] and our report strengthens this association.

There are several possible explanations why these two subpopulations in particular would be more predisposed to readmissions that are potentially avoidable. It is known that patients with schizophrenia, for example, live on average 20 years less than the general population, and most of this excess mortality is due to medical illnesses.[20, 21] Reasons for this may include poor healthcare access, adverse effects of medication, and socioeconomic factors among others.[21, 22] All of these reasons may contribute to the increased PAR risk in this population, mediated, for example, by decreased ability to adhere to postdischarge care plans. Successful community‐based interventions to decrease these inequities have been described and could serve as a model for addressing the increased readmission risk in this population.[23]

Our finding that patients with a greater number of prescribed psychiatric medications are at increased risk for ACR may be expected, given other studies that have highlighted the crucial importance of medications in postdischarge adverse events, including readmissions.[24] Indeed, medication‐related errors and toxicities are the most common postdischarge adverse events experienced by patients.[25] Whether psychiatric medications are particularly prone to causing postdischarge adverse events or whether these medications represent greater psychiatric comorbidity cannot be answered by this study.

It was surprising but reassuring that substance abuse was not a predictor of short‐term readmissions as identified using our measures; in fact, a discharge diagnosis of substance abuse was associated with lower risk of ACR than comparator patients. It seems unlikely that we would have inadequate power to find such a result, as we found a statistically significant negative association in the ACR population, and 17% of our population overall was considered likely to have a substance abuse comorbidity. However, it is likely the burden of disease was underestimated given that we did not try to determine the contribution of long‐term substance abuse to medical diseases that may increase readmission risk (eg, liver cirrhosis from alcohol use). Unlike other conditions in our study, patients with substance abuse diagnoses at BWH can be seen by a dedicated multidisciplinary team while an inpatient to start treatment and plan for postdischarge follow‐up; this may have played a role in our findings.

A discharge diagnosis of anxiety was also somewhat protective against readmission, whereas a prescription of an anxiolytic (predominantly benzodiazepines) increased risk; many patients prescribed a benzodiazepine do not have a Diagnostic and Statistical Manual of Mental Disorders4th Edition (DSM‐IV) diagnosis of anxiety disorder, and thus these findings may reflect different patient populations. Discharging physicians may have used anxiety as a discharge diagnosis in patients in whom they suspected somatic complaints without organic basis; these patients may be at lower risk of readmission.

Discharge diagnoses of psychiatric illnesses were associated with ACR and PAR in our study, but outpatient diagnoses were not. This likely reflects greater severity of illness (documentation as a treated diagnosis on discharge indicates the illness was relevant during the hospitalization), but may also reflect inaccuracies of diagnosis and lack of assessment of severity in outpatient coding, which would bias toward null findings. Although many of the patients in our study were seen by primary care doctors within the Partners system, some patients had outside primary care physicians and we did not have access to these records. This may also have decreased our ability to find associations.

The findings of our study should be interpreted in the context of the study design. Our study was retrospective, which limited our ability to conclusively diagnose psychiatric disease presence or severity (as is true of most institutions, validated psychiatric screening was not routinely used at our institutions on hospital admission or discharge). However, we used a conservative scale to classify the likelihood of patients having psychiatric or substance abuse disorders, and we used other metrics to establish the presence of illness, such as the number of prescribed medications, inpatient consultation with a substance abuse service, and hospital discharge diagnoses. This approach also allowed us to quickly identify a large cohort unaffected by selection bias. Our study was single center, potentially limiting generalizability. Although we capture at least 80% of readmissions, we were not able to capture all readmissions, and we cannot rule out that patients readmitted elsewhere are different than those readmitted within the Partners system. Last, the SQLape algorithm is not perfectly sensitive or specific in identifying avoidable readmissions,[13] but it does eliminate many readmissions that are clearly unavoidable, creating an enriched cohort of patients whose readmissions are more likely to be avoidable and therefore potentially actionable.

We suggest that our study findings first be considered when risk stratifying patients before hospital discharge in terms of readmission risk. Patients with depression and schizophrenia would seem to merit postdischarge interventions to decrease their potentially avoidable readmissions. Compulsory community treatment (a feature of treatment in Canada and Australia that is ordered by clinicians) has been shown to decrease mortality due to medical illness in patients who have been hospitalized and are psychiatrically ill, and addition of these services to postdischarge care may be useful.[23] Inpatient physicians could work to ensure follow‐up not just with medical providers but with robust outpatient mental health programs to decrease potentially avoidable readmission risk, and administrators could work to ensure close linkages with these community resources. Studies evaluating the impact of these types of interventions would need to be conducted. Patients with polypharmacy, including psychiatric medications, may benefit from interventions to improve medication safety, such as enhanced medication reconciliation and pharmacist counseling.[26]

Our study suggests that patients with depression, those with schizophrenia, and those who have increased numbers of prescribed psychiatric medications should be considered at high risk for readmission for medical illnesses. Targeting interventions to these patients may be fruitful in preventing avoidable readmissions.