Redefining Readmission Risk Factors

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.

Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5

To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.

To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.

Methods

Results

Discussion

In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.

Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31

Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.

Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.

Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.

While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.

This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.

Conclusions

Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.