Variation in the risk of readmission among hospitals: the relative contribution of patient, hospital and inpatient provider characteristics

Hospital variation of outcomes in status epilepticus

OBJECTIVE

Understanding factors driving variation in status epilepticus outcomes would be critical to improve care. We evaluated the degree to which patient and hospital characteristics explained hospital-to-hospital variability in intubation and postacute outcomes.

METHODS

This was a retrospective cohort study of Medicare beneficiaries admitted with status epilepticus between 2009 and 2019. Outcomes included intubation, discharge to a facility, and 30- and 90-day readmissions and mortality. Multilevel models calculated percent variation in each outcome due to hospital-to-hospital differences.

RESULTS

We included 29 150 beneficiaries. The median age was 68 years (interquartile range [IQR] = 57-78), and 18 084 (62%) were eligible for Medicare due to disability. The median (IQR) percentages of each outcome across hospitals were: 30-day mortality 25% (0%-38%), any 30-day readmission 14% (0%-25%), 30-day status epilepticus readmission 0% (0%-3%), 30-day facility stay 40% (25%-53%), and intubation 46% (20%-61%). However, after accounting for many hospitals with small sample size, hospital-to-hospital differences accounted for 2%-6% of variation in all unadjusted outcomes, and approximately 1%-5% (maximally 8% for 30-day readmission for status epilepticus) after adjusting for patient, hospitalization, and/or hospital characteristics. Although many characteristics significantly predicted outcomes, the largest effect size was cardiac arrest predicting death (odds ratio = 10.1, 95% confidence interval = 8.8-11.7), whereas hospital characteristics (e.g., staffing, accreditation, volume, setting, services) all had lesser effects.

SIGNIFICANCE

Hospital-to-hospital variation explained little variation in studied outcomes. Rather, certain patient characteristics (e.g., cardiac arrest) had greater effects. Interventions to improve outcomes after status epilepticus may be better focused on individual or prehospital factors, rather than at the inpatient systems level.

Multimorbidity, 30-Day Readmissions, and Postdischarge Mortality Among Medicare Beneficiaries Using a New ICD-Coded Multimorbidity-Weighted Index

BACKGROUND

Medically complex, disabled adults have high 30-day readmission rates. However, physical functioning is not routinely included in risk-adjustment models. We examined the association between multimorbidity with readmissions and mortality using a physical functioning weighted International Classification of Diseases (ICD)-coded multimorbidity-weighted index (MWI-ICD) representing 84 conditions.

METHODS

We included Medicare beneficiaries with ≥1 hospitalization 2000-2015 who participated in a Health and Retirement Study interview before admission. We computed MWI-ICD by summing physical functioning weighted conditions from Medicare claims. We examined 30-, 90-, and 365-day postdischarge mortality using multivariable logistic regression and length of stay through zero-inflated negative binomials. Models adjusted for age, sex, race/ethnicity, body mass index, smoking status, physical activity, education, net worth, and marital status/living arrangement.

RESULTS

The final sample of 10 737 participants had mean ± standard deviation (SD) age 75.9 ± 8.7 years, MWI-ICD 14.9 ± 9.0, and 20% had a 30-day readmission. Adults in the highest versus lowest quartile MWI-ICD had 92% increased odds of 30-day readmission (odds ratio [OR] = 1.92, 95% confidence interval [CI]: 1.65-2.22). A 1-point increase in MWI-ICD was associated with 24% increased odds of 30-day readmission (OR = 1.24, 95% CI: 1.18-1.31). A 1-point increase in MWI-ICD was associated with 32% increased odds of death within 365-day postdischarge (OR = 1.32, 95% CI: 1.25-1.40). Readmitted participants with the highest versus lowest quartile MWI-ICD had 37% increased number of expected hospitalized days (incidence rate ratio = 1.37, 95% CI: 1.17-1.59).

CONCLUSION

Among Medicare beneficiaries, multimorbidity using MWI-ICD is associated with an increased risk of readmissions, mortality, and longer length of stay. MWI-ICD appears to be a valid measure of multimorbidity that embeds physical functioning and presents an opportunity to incorporate functional status into claims-based risk-adjustment models.

Association of Academic Medical Center Presence With Clinical Outcomes at Neighboring Community Hospitals Among Medicare Beneficiaries

IMPORTANCE

Studies suggest that academic medical centers (AMCs) have better outcomes than nonteaching hospitals. However, whether AMCs have spillover benefits for patients treated at neighboring community hospitals is unknown.

OBJECTIVE

To examine whether market-level AMC presence is associated with outcomes for patients treated at nonteaching hospitals within the same markets.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, population-based cohort study assessed traditional Medicare beneficiaries aged 65 years and older discharged from US acute care hospitals between 2015 and 2017 (100% sample). Data were analyzed from August 2021 to December 2022.

EXPOSURES

The primary exposure was market-level AMC presence. Health care markets (ie, hospital referral regions) were categorized by AMC presence (percentage of hospitalizations at AMCs) as follows: no presence (0%), low presence (>0% to 20%), moderate presence (>20% to 35%), and high presence (>35%).

MAIN OUTCOMES AND MEASURES

The primary outcomes were 30-day and 90-day mortality and healthy days at home (HDAH), a composite outcome reflecting mortality and time spent in facility-based health care settings.

RESULTS

There were 22 509 824 total hospitalizations, with 18 865 229 (83.8%) at non-AMCs. The median (IQR) age of patients was 78 (71-85) years, and 12 568 230 hospitalizations (55.8%) were among women. Of 306 hospital referral regions, 191 (62.4%) had no AMCs, 61 (19.9%) had 1 AMC, and 55 (17.6%) had 2 or more AMCs. Markets characteristics differed significantly by category of AMC presence, including mean population, median income, proportion of White residents, and physicians per population. Compared with markets with no AMC presence, receiving care at a non-AMC in a market with greater AMC presence was associated with lower 30-day mortality (9.5% vs 10.1%; absolute difference, -0.7%; 95% CI, -1.0% to -0.4%; P < .001) and 90-day mortality (16.1% vs 16.9%; absolute difference, -0.8%; 95% CI, -1.2% to -0.4%; P < .001) and more HDAH at 30 days (16.49 vs 16.12 HDAH; absolute difference, 0.38 HDAH; 95% CI, 0.11 to 0.64 HDAH; P = .005) and 90 days (61.08 vs 59.83 HDAH; absolute difference, 1.25 HDAH; 95% CI, 0.58 to 1.92 HDAH; P < .001), after adjustment. There was no association between market-level AMC presence and mortality for patients treated at AMCs themselves.

CONCLUSIONS AND RELEVANCE

AMCs may have spillover effects on outcomes for patients treated at non-AMCs, suggesting that they have a broader impact than is traditionally recognized. These associations are greatest in markets with the highest AMC presence and persist to 90 days.

Post-Acute Care Use Associated with Medicare Shared Savings Program and Disparities

OBJECTIVES

Medicare Shared Savings Program (MSSP) was implemented in 2012, but the impact of the MSSP on institutional post-acute care (PAC) use, and by race/ethnicity and payer status is less studied. We studied the impact of hospital participation in the MSSP on institutional PAC use and variations by race/ethnicity and payer status among 3 Medicare patient groups: ischemic stroke, hip fracture, and elective total joint arthroplasty (TJA).

DESIGN

A retrospective analysis of 2010-2016 Medicare Provider Analysis and Review files.

SETTING AND PARTICIPANTS

Medicare fee-for-service patients originally admitted for ischemic stroke, hip fracture, or elective TJA in MSSP-participating hospitals or nonparticipating hospitals.

METHODS

Patient-level linear probability models with difference-in-differences approach were used to compare the changes in institutional PAC use in MSSP-participating hospitals with nonparticipating hospitals as well as to compare the changes in differences by race/ethnicity and payer status in institutional PAC use over time.

RESULTS

Hospital participation in MSSP was significantly associated with increased institutional PAC use for the ischemic stroke cohort by 1.5 percentage points [95% confidence interval (CI) 0.00-0.3, P < .05] compared with non-MSSP participating hospitals. Regarding variations by race/ethnicity and payer status, for the elective TJA patients, racial minority patients in MSSP-participating hospitals had 3.8 percentage points greater (95% CI 0.01-0.06, P < .01) in institutional PAC use than white patients. Also, for ischemic stroke cohort, dual-eligible patients in MSSP-participating hospitals had 2.0 percentage points greater (95% CI 0.00-0.04, P < .10) in institutional PAC use than Medicare-only patients.

CONCLUSIONS AND IMPLICATIONS

This study found that hospital participation in the MSSP was associated with slightly increased institutional PAC use for ischemic stroke Medicare patients. Also, compared to non-MSSP participating hospitals, MSSP-participating hospitals were more likely to discharge racial minority patients for elective TJA and dual-eligible patients for ischemic stroke to institutional PAC.

Development of a 30-Day Readmission Risk Calculator for the Inpatient Rehabilitation Setting

OBJECTIVES

Readmission to acute care from the inpatient rehabilitation facility (IRF) setting is potentially preventable and an important target of quality improvement and cost savings. The objective of this study was to develop a risk calculator to predict 30-day all-cause readmissions from the IRF setting.

DESIGN

Retrospective database analysis using the Uniform Data System for Medical Rehabilitation (UDSMR) from 2015 through 2019.

SETTING AND PARTICIPANTS

In total, 956 US inpatient rehabilitation facilities and 1,849,768 IRF discharges comprising patients from 14 impairment groups.

METHODS

Logistic regression models were developed to calculate risk-standardized 30-day all-cause hospital readmission rates for patients admitted to an IRF. Models for each impairment group were assessed using 12 common clinical and demographic variables and all but 4 models included various special variables. Models were assessed for discrimination (c-statistics), calibration (calibration plots), and internal validation (bootstrapping). A readmission risk scoring system was created for each impairment group population and was graphically validated.

RESULTS

The mean age of the cohort was 68.7 (15.2) years, 50.7% were women, and 78.3% were Caucasian. Medicare was the primary payer for 73.1% of the study population. The final models for each impairment group included between 4 and 13 total predictor variables. Model c-statistics ranged from 0.65 to 0.70. There was good calibration represented for most models up to a readmission risk of 30%. Internal validation of the models using bootstrap samples revealed little bias. Point systems for determining risk of 30-day readmission were developed for each impairment group.

CONCLUSIONS AND IMPLICATIONS

Multivariable risk factor algorithms based upon administrative data were developed to assess 30-day readmission risk for patients admitted from IRF. This report represents the development of a readmission risk calculator for the IRF setting, which could be instrumental in identifying high risk populations for readmission and targeting resources towards a diverse group of IRF impairment groups.

Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study

BACKGROUND

Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR).

OBJECTIVE

We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Adult patients discharged from a CCHS hospital April 2017-September 2020.

MAIN MEASURES

Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic.

RESULTS

The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories.

CONCLUSIONS

The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

Postpartum Hospital Readmissions and Emergency Department Visits Among High-Risk, Medicaid-Insured Women in New York City

Objectives: To describe the incidence of and characteristics associated with postpartum emergency department (ED) visits and hospital readmissions among high-risk, low-income, predominantly Black and Latina women in New York City (NYC). Methods: We conducted a secondary analysis of detailed survey and medical chart data from an intervention to improve timely postpartum visits among Medicaid-insured, high-risk women in NYC from 2015 to 2016. Among 380 women who completed surveys at baseline (bedside postpartum) and 3 weeks after delivery, we examined the incidence of having an ED visit or readmission within 3 weeks postpartum. We used logistic regression to examine unadjusted and adjusted associations between patient demographic, clinical, and psychosocial characteristics and the odds of postpartum hospital use. Results: In total, 12.8% (n = 48) of women reported an ED visit or readmission within 3 weeks postpartum. Unadjusted odds of postpartum hospital use were higher among women who self-identified as Black versus Latina, U.S. born versus foreign born, and English versus Spanish speaking. Clinical and psychosocial characteristics associated with increased unadjusted odds of postpartum hospital use included cesarean delivery, hypertensive disorders of pregnancy, and positive depression or anxiety screen, and we found preliminary evidence of decreased hospital use among women breastfeeding at three weeks postpartum. The odds of seeking postpartum hospital care remained roughly 2.5 times higher among women with hypertension or depression/anxiety in adjusted analyses. Conclusions: We identified characteristics associated with ED visits and hospital readmissions among a high-risk subset of postpartum women in NYC. These characteristics, including depressive symptoms and hypertension, suggest women who may benefit from additional postpartum support to prevent maternal complications and reduce health disparities.

Development and validation of a social vulnerabilities survey for medical inpatients

OBJECTIVES

Our objective was to validate a Social Vulnerabilities Survey that was developed to identify patient barriers in the following domains: (1) salience or priority of health; (2) social support; (3) transportation; and (4) finances.

DESIGN

Cross-sectional psychometric study.Questions for one domain (health salience) were developed de novo while questions for the other domains were derived from national surveys and/or previously validated questionnaires. We tested construct (ie, convergent and discriminative) validity for these new questions through hypothesis testing of correlations between question responses and patient characteristics. Exploratory factor analysis was conducted to determine structural validity of the survey as a whole.

SETTING

Patients admitted to the inpatient internal medicine service at a tertiary care hospital in Calgary, Canada.

PARTICIPANTS

A total of 406 patients were included in the study.

RESULTS

The mean age of respondents was 55.5 (SD 18.6) years, with the majority being men (55.4%). In feasibility testing of the first 107 patients, the Social Vulnerabilities Survey was felt to be acceptable, comprehensive and met face validity. Hypothesis testing of the health salience questions revealed that the majority of observed correlations were exactly as predicted. Exploratory factor analysis of the global survey revealed the presence of five factors (eigenvalue >1): social support, health salience, drug insurance, transportation barriers and drug costs. All but four questions loaded to these five factors.

CONCLUSIONS

The Social Vulnerabilities Survey has face, construct and structural validity. It can be used to measure modifiable social vulnerabilities, such that their effects on health outcomes can be explored and understood.

Trends and variation in benzodiazepine use in nursing homes in the USA

PURPOSE

Because of toxicities, benzodiazepines are not usually recommended in older adults. We therefore sought to describe the trends in benzodiazepine use in long-term care and examine the variation in benzodiazepine use among nursing homes.

METHODS

In this retrospective repeated cross-sectional analysis of Medicare Parts A, B, and D claims data linked to the Minimum Data Set from 2013 to 2018, we included long-term residents who stayed in a nursing home for at least one entire quarter of a calendar year in 2013-2018. The outcome was whether residents were prescribed a benzodiazepine drug for at least 30 days during each quarter stay. We use mixed effects logistic regression models to assess the variation in benzodiazepine use among nursing homes, adjusting for patient and nursing home characteristics.

RESULTS

The cohort for the time trend analysis included 270,566 unique residents and 1,843,580 quarter stays for 2013-2018. Prescribing rates for short-acting benzodiazepines were stable over 2013-2016, then declined from 12.1% in 2016 to 10.6% in 2018. The rate of long-acting benzodiazepine use remained relatively steady at around 4% over 2013-2018. During 2017-2018, the variation among nursing homes in benzodiazepine use was 7.2% for short-acting vs. 9.3% for long-acting benzodiazepines, after controlling for resident characteristics.

CONCLUSION

Prescribing for short-acting benzodiazepines in long-term care declined after 2016, while long-acting benzodiazepine use did not change. The variation in benzodiazepine use among nursing homes is substantial. Identifying factors that explain this variation may help in developing strategies for deprescribing benzodiazepines in nursing home residents.

Contributors to disparities in postpartum readmission rates between safety-net and non-safety-net hospitals: A decomposition analysis

BACKGROUND

Safety-net hospitals (SNHs) treat more maternal patients with risk factors for postpartum readmission.

OBJECTIVE

To assess how patient, hospital, and community characteristics explain the SNH/non-SNH disparity in postpartum readmission rates.

DESIGN

A linear probability model assessed covariates associated with postpartum readmissions. Oaxaca-Blinder decomposition estimates quantified the contribution of covariates to the SNH/non-SNH disparity in postpartum readmission rates.

SETTING

Healthcare Cost and Utilization Project 2016-2018 State Inpatient Databases from 25 states.

PARTICIPANTS

3.5 million maternal delivery stays.

MEASUREMENTS

The outcome was inpatient readmission within 42 days of delivery. SNHs had a share of Medicaid/uninsured stays in the top quartile. A range of patient, hospital, and community characteristics was considered as covariates.

RESULTS

The unadjusted postpartum readmission rate was 4.2 per 1000 index deliveries higher at SNHs than at non-SNHs (19.1 vs. 14.9, p < .001). Adjustment reduced the risk difference to 0.65 per 1000 (95% confidence interval [CI]: -0.14, 1.44). Patient (66%), hospital (14%), and community (4%) characteristics explained 84% of the disparity. The single largest contributors to the disparity were race/ethnicity (20%), hypertension (12%), hospital preterm delivery rate (10%), and preterm delivery (7%). Collectively, patient comorbidities explained 31% of the disparity.

CONCLUSION

Higher postpartum readmission rates at SNHs versus non-SNHs were largely due to differences in the patient mix rather than hospital factors. Hospital initiatives are needed to reduce the risk of postpartum readmissions among SNH patients. Improving factors that contribute to the disparity, including underlying health conditions and health inequities associated with race, requires enduring investments in public health.

Luck of the draw: Role of chance in the assignment of medicare readmissions penalties

Pay-for-performance programs are one strategy used by health plans to improve the efficiency and quality of care delivered to beneficiaries. Under such programs, providers are often compared against their peers in order to win bonuses or face penalties in payment. Yet luck has the potential to affect performance assessment through randomness in the sorting of patients among providers or through random events during the evaluation period. To investigate the impact luck can have on the assessment of performance, we investigated its role in assigning penalties under Medicare's Hospital Readmissions Reduction Policy (HRRP), a program that penalizes hospitals with excess readmissions. We performed simulations that estimated program hospitals' 2015 readmission penalties in 1,000 different hypothetical fiscal years. These hypothetical fiscal years were created by: (a) randomly varying which patients were admitted to each hospital and (b) randomly varying the readmission status of discharged patients. We found significant differences in penalty sizes and probability of penalty across hypothetical fiscal years, signifying the importance of luck in readmission performance under the HRRP. Nearly all of the impact from luck arose from events occurring after hospital discharge. Luck played a smaller role in determining penalties for hospitals with more beds, teaching hospitals, and safety-net hospitals.

Variation in 30-Day Readmission Rates from Inpatient Rehabilitation Facilities to Acute Care Hospitals

OBJECTIVES

To quantify the rate of readmission from inpatient rehabilitation facilities (IRFs) to acute care hospitals (ACHs) during the first 30 days of rehabilitation stay. To measure variation in 30-day readmission rate across IRFs, and the extent that patient and facility characteristics contribute to this variation.

DESIGN

Retrospective analysis of an administrative database.

SETTING AND PARTICIPANTS

Adult IRF discharges from 944 US IRFs captured in the Uniform Data System for Medical Rehabilitation database between October 1, 2015 and December 31, 2017.

METHODS

Multilevel logistic regression was used to calculate adjusted rates of readmission within 30 days of IRF admission and examine variation in IRF readmission rates, using patient and facility-level variables as predictors.

RESULTS

There were a total of 104,303 ACH readmissions out of a total of 1,102,785 IRFs discharges. The range of 30-day readmission rates to ACHs was 0.0%‒28.9% (mean = 8.7%, standard deviation = 4.4%). The adjusted readmission rate variation narrowed to 2.8%‒17.5% (mean = 8.7%, standard deviation = 1.8%). Twelve patient-level and 3 facility-level factors were significantly associated with 30-day readmission from IRF to ACH. A total of 82.4% of the variance in 30-day readmission rate was attributable to the model predictors.

CONCLUSIONS AND IMPLICATIONS

Fifteen patient and facility factors were significantly associated with 30-day readmission from IRF to ACH and explained the majority of readmission variance. Most of these factors are nonmodifiable from the IRF perspective. These findings highlight that adjusting for these factors is important when comparing readmission rates between IRFs.

The Effect of Medicare Shared Savings Program on Readmissions and Variations by Race/Ethnicity and Payer Status (December 9, 2020)

OBJECTIVE

Accountable care organizations in the Medicare Shared Savings Program (MSSP) in the United States attempt to reduce cost and improve quality for their patients by improving care coordination across care settings. We examined the impact of hospital participation in the MSSP on 30-day readmissions for several groups of Medicare inpatients, and by race/ethnicity and payer status.

MAIN DATA SOURCE

A 2010-2016 Medicare Provider Analysis and Review files.

RESEARCH DESIGN

With propensity score matched sample of MSSP and non-MSSP-participating hospitals, patient-level linear probability models with difference-in-differences approach were used to compare the changes in readmission rates for Medicare fee-for-service patients initially admitted for ischemic stroke, hip fracture, or total joint arthroplasty in MSSP-participating hospitals with non-MSSP-participating hospitals as well as to compare the changes in disparities in readmission rates over time.

PRINCIPAL FINDINGS

Hospital participation in MSSP was associated with further reduced readmission rate by 1.1 percentage points (95% confidence interval: -0.02 to 0.00, P<0.05) and 1.5 percentage points (95% confidence interval: -0.03 to 0.00, P=0.08) for ischemic stroke and hip fracture cohorts, respectively, compared with non-MSSP-participating hospitals, after the third year of hospital participation in the MSSP. There was no evidence that MSSP had an impact on racial/ethnic disparities, but increased disparity by payer status (dual vs. Medicare-only) was observed. These findings together suggest that MSSP accountable care organizations may take at least 3 years to achieve reduced readmissions and may increase disparities by payer status.

Risk Factors Associated With SARS-CoV-2 Infections, Hospitalization, and Mortality Among US Nursing Home Residents

Importance

Nursing home residents account for approximately 40% of deaths from SARS-CoV-2.

Objective

To identify risk factors for SARS-CoV-2 incidence, hospitalization, and mortality among nursing home residents in the US.

Design, Setting, and Participants

This retrospective longitudinal cohort study was conducted in long-stay residents aged 65 years or older with fee-for-service Medicare residing in 15 038 US nursing homes from April 1, 2020, to September 30, 2020. Data were analyzed from November 22, 2020, to February 10, 2021.

Main Outcomes and Measures

The main outcome was risk of diagnosis with SARS-CoV-2 (per International Statistical Classification of Diseases, Tenth Revision, Clinical Modification [ICD-10-CM] codes) by September 30 and hospitalization or death within 30 days after diagnosis. Three-level (resident, facility, and county) logistic regression models and competing risk models conditioned on nursing home facility were used to determine association of patient characteristics with outcomes.

Results

Among 482 323 long-stay residents included, the mean (SD) age was 82.7 (9.2) years, with 326 861 (67.8%) women, and 383 838 residents (79.6%) identifying as White. Among 137 119 residents (28.4%) diagnosed with SARS-CoV-2 during follow up, 29 204 residents (21.3%) were hospitalized, and 26 384 residents (19.2%) died within 30 days. Nursing homes explained 37.2% of the variation in risk of infection, while county explained 23.4%. Risk of infection increased with increasing body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) (eg, BMI>45 vs BMI 18.5-25: adjusted hazard ratio [aHR], 1.19; 95% CI, 1.15-1.24) but varied little by other resident characteristics. Risk of hospitalization after SARS-CoV-2 increased with increasing BMI (eg, BMI>45 vs BMI 18.5-25: aHR, 1.40; 95% CI, 1.28-1.52); male sex (aHR, 1.32; 95% CI, 1.29-1.35); Black (aHR, 1.28; 95% CI, 1.24-1.32), Hispanic (aHR, 1.20; 95% CI, 1.15-1.26), or Asian (aHR, 1.46; 95% CI, 1.36-1.57) race/ethnicity; impaired functional status (eg, severely impaired vs not impaired: aHR, 1.15; 95% CI, 1.10-1.22); and increasing comorbidities, such as renal disease (aHR, 1.21; 95% CI, 1.18-1.24) and diabetes (aHR, 1.16; 95% CI, 1.13-1.18). Risk of mortality increased with age (eg, age >90 years vs 65-70 years: aHR, 2.55; 95% CI, 2.44-2.67), impaired cognition (eg, severely impaired vs not impaired: aHR, 1.79; 95% CI, 1.71-1.86), and functional impairment (eg, severely impaired vs not impaired: aHR, 1.94; 1.83-2.05).

Conclusions and Relevance

These findings suggest that among long-stay nursing home residents, risk of SARS-CoV-2 infection was associated with county and facility of residence, while risk of hospitalization and death after SARS-CoV-2 infection was associated with facility and individual resident characteristics. For many resident characteristics, there were substantial differences in risk of hospitalization vs mortality. This may represent resident preferences, triaging decisions, or inadequate recognition of risk of death.

Hospital variation of 30-day readmission rate following transcatheter aortic valve implantation

OBJECTIVES

Thirty-day readmission rate is one of the hospital quality metrics. Outcomes of transcatheter aortic valve implantation (TAVI) have improved significantly, but it remains unclear whether hospital-level variance in 30-day readmission rate exists in the contemporary TAVI era.

METHODS

From the 2017 US Nationwide Readmission Database, endovascular TAVI were identified. The unadjusted 30-day readmission rate and 30-day risk-standardised readmission rate (RSRR) were calculated and we then conducted model testing to determine the relative contribution of hospital characteristics, patient-level covariates and economic status to the variation in readmission rates observed between the hospitals.

RESULTS

A total of 44 899 TAVI from 338 hospitals were identified. The range of unadjusted 30-day readmission rate and 30-day RSRR was 2.0%-33.3% and 9.4%-15.3%, respectively. Median 30-day RSRR was 11.8% and there was a significant hospital-level variation (median OR 1.22, 95% CI 1.16 to 1.32, p<0.01) and this was similar in both readmissions caused due to major cardiac and non-cardiac conditions. Patient, hospital and economic factors accounted for 9.6%, 1.9% and 3.8% of the variability in hospital readmission rate, respectively.

CONCLUSIONS

There was significant hospital-level variation in 30-day RSRR following TAVI. Further measures are required to mitigate this variance in the readmission rate.

Multi-Level Predictors of Discharges Against Medical Advice: Identifying Contributors to Variation Using an All-Payer Database

ABSTRACT

There is increasing evidence of the role of non-patient-level factors on discharge against medical advice (DAMA), but limited quantitative information regarding the extent of their impact. This study quantifies the contribution of discharge-level and hospital-level factors to the variation in DAMA. We grouped variables from the 2014 National Inpatient Sample data and ran incremental mixed-effects logit models with grouping at the level of the discharge, the hospital, and the census region. We obtained the intraclass correlation coefficients (ICCs), and evaluated the incremental change in ICC. The final sample included 2,687,430 discharges. 12.8% of the identified variation in the probability of DAMA was associated with the hospital, and 1.2% of the variation was associated with the census division in which the hospital was located. The final, fully-adjusted model had 7.3% of variation in DAMA associated with the hospital-level, with the greatest percentage reductions because of the addition of patient demographics. Even after adjusting for measured patient-level characteristics, there was a contribution of non-patient-level factors to DAMA outcomes. The findings identify a role for a multi-level approach to addressing DAMA.

Are Improvements Still Needed to the Modified Hospital Readmissions Reduction Program: a Health and Retirement Study (2000-2014)?

BACKGROUND

To address concerns that the Hospital Readmissions Reduction Program (HRRP) unfairly penalized safety net hospitals treating patients with high social and functional risks, Medicare recently modified HRRP to compare hospitals with similar proportions of high-risk, dual-eligible patients ("peer group hospitals"). Whether the change fully accounts for patients' social and functional risks is unknown.

OBJECTIVE

Examine risk-standardized readmission rates (RSRRs) and hospital penalties after adding patient-level social and functional and community-level risk factors.

DESIGN

Using 2000-2014 Medicare hospital discharge, Health and Retirement Study, and community-level data, latent factors for patient social and functional factors and community factors were identified. We estimated RSRRs for peer groups and by safety net status using four hierarchical logistic regression models: "base" (HRRP model); "patient" (base plus patient factors); "community" (base plus community factors); and "full" (all factors). The proportion of hospitals penalized was calculated by safety net status.

PATIENTS

20,255 fee-for-service Medicare beneficiaries (65+) with eligible index hospitalizations MAIN MEASURES: RSRRs KEY RESULTS: Half of safety net hospitals are in peer group 5. Compared with other hospitals, peer group 5 hospitals (most dual-eligibles) treated sicker, more functionally limited patients from socially disadvantaged groups. RSRRs decreased by 0.7% for peer groups 2 and 4 and 1.3% for peer group 5 under the patient and full (versus base) models. Measured performance improved after adjusting for patient risk factors for hospitals in peer group 4 and 5 hospitals, but worsened for those in peer groups 1, 2, and 3. Under the patient (versus base) model, fewer safety net hospitals (48.7% versus 51.3%) but more non-safety net hospitals (50.0% versus 49.1%) were penalized.

CONCLUSIONS

Patient-level risk adjustment decreased RSRRs for hospitals serving more at-risk patients and proportion of safety net hospitals penalized, while modestly increasing RSRRs and proportion of non-safety net hospitals penalized. Results suggest HRRP modifications may not fully account for hospital variation in patient-level risk.

Non-parametric recurrent events analysis with BART and an application to the hospital admissions of patients with diabetes

Much of survival analysis is concerned with absorbing events, i.e., subjects can only experience a single event such as mortality. This article is focused on non-absorbing or recurrent events, i.e., subjects are capable of experiencing multiple events. Recurrent events have been studied by many; however, most rely on the restrictive assumptions of linearity and proportionality. We propose a new method for analyzing recurrent events with Bayesian Additive Regression Trees (BART) avoiding such restrictive assumptions. We explore this new method via a motivating example of hospital admissions for diabetes patients and simulated data sets.

Determinants of Medical Practice Variation Among Primary Care Physicians: Protocol for a Three Phase Study

BACKGROUND

One of the greatest challenges of modern health systems is the choice and use of resources needed to diagnose and treat patients. Medical practice variation (MPV) is a broad term which entails the differences between health care providers inclusive of both the overuse and underuse. In this paper, we describe a 3-phase research protocol examining MPV in primary care.

OBJECTIVE

We aim to identify the potential targets for behavioral modification interventions to reduce the variation in practice patterns and thus improve health care, decrease costs, and prevent disparities in care.

METHODS

The first phase will delineate the variation in primary care practice over a wide range of services and long follow-up period (2003-2017), the second will examine the 3 determinants of variation (ie, patient, physician, and clinic characteristics), and attempt to derive the unexplained variance. In the third phase, we will assess a novel component that might contribute to the previously unexplained variance - the physicians' personal behavioral characteristics (such as risk aversion, fear of malpractice, stress from uncertainty, empathy, and burnout).

RESULTS

This work was supported by the research grant from Israel National Institute for Health Policy Research (Grant No. 2014/134). Soroka University Medical Center Institutional Ethics Committee has approved the updated version of the study protocol (SOR-14-0063) in February 2019. All relevant data for phases 1 and 2, including patient, physician, and clinic, were collected from the Clalit Health Services data set in 2019 and are currently being analyzed. The evaluation of the individual physician characteristics (eg, risk aversion) by the face-to-face questionnaires was started on 2018 and remains in progress. We intend to publish the results during 2020-2021.

CONCLUSIONS

Based on the results of our study, we aim to propose a list of potential targets for focused behavioral intervention. Identifying new targets for such an intervention can potentially lead to a decrease in the unwarranted variation in the medical practice. We suggest that such an intervention will result in optimization of the health system, improvement of health outcomes, reduction of disparities in care and savings in cost.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/18673.

Medicare Shared Savings Program and readmission rate among patients with ischemic stroke

OBJECTIVE

Hospitals participating in the Medicare Shared Savings Program (MSSP) share with the Centers for Medicare and Medicaid Services (CMS) the savings generated by reduced cost of care. Our aim was to determine whether MSSP is associated with changes in readmissions and mortality for Medicare patients hospitalized with ischemic stroke, and whether MSSP has a different impact on safety net hospitals (SNHs) compared to non-SNHs.

METHODS

This study was based on the CMS Hospital Compare data for risk-standardized 30-day readmission and mortality rates for Medicare patients hospitalized with ischemic strokes between 2010 and 2017. With a propensity score-matched sample, hospital-level difference-in-difference analysis was used to determine whether MSSP was associated with changes in hospital readmission and mortality as well as to examine the impact of MSSP on SNHs compared to non-SNHs.

RESULTS

MSSP-participating hospitals had slightly greater reductions in readmission rates compared to matched nonparticipating hospitals (difference, 0.25 percentage points; 95% confidence interval [CI], -0.42 to -0.08). Mortality rates decreased among all hospitals, but mortality reduction was not significantly different between MSSP-participating hospitals and matched hospitals (difference, 0.06 percentage points; 95% CI, -0.28 to 0.17). Prior to MSSP, readmission rates in SNHs were higher compared to non-SNHs, but MSSP did not have significantly different impact on hospital readmission and mortality rates for SNHs and non-SNHs.

CONCLUSION

MSSP led to slightly fewer readmissions without increases in mortality for Medicare patients hospitalized with ischemic stroke. Similar reductions in readmission rates were observed in SNHs and non-SNHs participating in MSSP, indicating persistent gaps between SNHs and non-SNHs.

The Financial Impact of an Avoided Readmission for Teaching and Safety-Net Hospitals Under Medicare's Hospital Readmission Reduction Program

We examined the financial incentives to avoid readmissions under Medicare's Hospital Readmission Reduction Program for safety-net hospitals (SNHs) and teaching hospitals (THs) compared with other hospitals. Using Medicare's FY2016 Hospital Compare and readmissions data for 2,465 hospitals, we tested for differential revenue gains for SNHs (n = 658) relative to non-SNHs (n = 1,807), and for major (n = 231) and minor (n = 591) THs relative to non-THs (n = 1,643). We examined hospital-level factors predicting differences in revenue gains by hospital type. The revenue gains of an avoided readmission were 10% to 15% greater for major THs compared with non-THs ($18,047 vs. $15,478 for acute myocardial infarction) but no different for SNHs compared with non-SNHs. The greater revenue gains for THs were strongly positively predicted by hospitals' poor initial readmission performance. We found little evidence that the Hospital Readmission Reduction Program creates disincentives for SNHs and THs to invest in readmission reduction efforts, and THs have greater returns from readmissions avoidance than non-THs.

Trends in Readmissions and Length of Stay for Patients Hospitalized With Heart Failure in Canada and the United States

Importance

Over the past decade, reducing 30-day readmission rates has been emphasized in the United States (including via the implementation of the Hospital Readmissions Reduction Program) but not Canada.

Objective

To examine changes that occurred from April 1, 2005, to December 31, 2015, in the United States and Canada for hospitalization length of stay and 30-day readmission rates of patients with heart failure.

Design, Setting, and Participants

This cohort study included patients admitted with a primary diagnosis of heart failure to Canadian and US hospitals between April 1, 2005, and December 31, 2015, using International Classification of Diseases, Ninth Revision code 428.xx and Tenth Revision code I50. The study examined secular trends in length of stay and readmissions in both countries and tested for changes after implementation of the Hospital Readmissions Reduction Program using segmented regression models and the association between length of stay and readmissions using patient-level and hospital-level multivariable logistic regression models. Data analysis was completed from February 2018 to August 2018.

Main Outcomes and Measures

Thirty-day readmissions.

Results

Between 2005 and 2015, mean length of stay declined marginally in Canadian hospitals (from a mean [SD] of 7.5 [5.7] to 7.3 [5.6] days; P < .001) but remained stable in US hospitals (mean [SD], 4.9 [3.7] days to 4.9 [3.5] days). Thirty-day readmission rates declined similarly in Canada (from 4088 of 20 758 patients [19.7%] to 3823 of 21 733 patients [17.6%] for all-cause readmissions; P < .001; and from 1743 of 20 758 patients [8.4%] to 1490 of 21 733 patients [6.9%] for heart failure-specific readmissions; P < .001) and the United States (from 21.2% to 18.5% for all-cause readmissions; from 7.6% to 5.7% for heart failure-specific readmissions; both P < .001). There were small but statistically significant positive correlations between length of stay and 30-day readmissions in both Canada (odds ratio, 1.01 [95% CI, 1.01-1.01]) and the United States (odds ratio, 1.01 [95% CI, 1.01-1.01]). Interrupted time-series analysis comparing readmission rates before and after the Hospital Readmissions Reduction Program implementation revealed no significant difference in either country for all-cause readmission rates before and after October 2012. There was also no change in the slope of the temporal trends; in Canada, all-cause readmissions were decreasing 1.1% per year before implementation and 1.3% after implementation (P = .84 for slope change) compared with 1.6% per year in the United States before implementation and 1.8% per year after October 2012 (P = .60 for slope change).

Conclusions and Relevance

Both Canada and the United States exhibited similar temporal declines in 30-day all-cause readmissions over the past decade. These findings suggest that the Hospital Readmissions Reduction Program did not appear to be associated with this secular trend or length of stay for heart failure in the United States.

A Cross-sectional Study of Hospital Performance on ICU Utilization Practices for Patients with Chronic Obstructive Pulmonary Disease

PURPOSE

Intensive care unit (ICU) resources are a costly but effective commodity used in the management of critically ill patients with chronic obstructive pulmonary disease (COPD). ICU admission decisions are determined by patient diagnosis and severity of illness, but also may be affected by hospital differences in quality and performance. We investigate the variability in ICU utilization for patients with COPD and its association with hospital characteristics.

METHODS

Using a 3M administrative dataset spanning 2008-2013, we conducted a retrospective cohort study of adult patients discharged with COPD at hospitals in three state to determine variability in ICU utilization. Quality metrics were calculated for each hospital using observed-to-expected (O/E) ratios for overall mortality and length of stay. Logistic and multilevel multivariate regression models were constructed, estimating the association between hospital quality metrics on ICU utilization, after adjustment for available clinical factors and hospital characteristics.

RESULTS

In 434 hospitals with 570,517 COPD patient visits, overall ICU admission rate was 33.1% [range 0-89%; median (IQR) 24% (8, 54)]. The addition of patient, hospital, and quality characteristics decreased the overall variability attributable to individual hospital differences seen within our cohort from 40.9 to 33%. Odds of ICU utilization were increased for larger hospitals and those seeing lower pulmonary case volume. Hospitals with better overall O/E ratios for length of stay or mortality had lower ICU utilization.

CONCLUSIONS

Hospital characteristics, including quality metrics, are associated with variability in ICU utilization for COPD patients, with higher ICU utilization seen for lower performing hospitals.

Physician-level variation in clinical outcomes and resource use in inpatient general internal medicine: an observational study

Background

Variations in inpatient medical care are typically attributed to system, hospital or patient factors. Little is known about variations at the physician level within hospitals. We described the physician-level variation in clinical outcomes and resource use in general internal medicine (GIM).

Methods

This was an observational study of all emergency admissions to GIM at seven hospitals in Ontario, Canada, over a 5-year period between 2010 and 2015. Physician-level variations in inpatient mortality, hospital length of stay, 30-day readmission and use of ‘advanced imaging’ (CT, MRI or ultrasound scans) were measured. Physicians were categorised into quartiles within each hospital for each outcome and then quartiles were pooled across all hospitals (eg, physicians in the highest quartile at each hospital were grouped together). We report absolute differences between physicians in the highest and lowest quartiles after matching admissions based on propensity scores to account for patient-level variation.

Results

The sample included 103 085 admissions to 135 attending physicians. After propensity score matching, the difference between physicians in the highest and lowest quartiles for in-hospital mortality was 2.4% (95% CI 0.6% to 4.3%, p<0.01); for readmission was 3.3% (95% CI 0.7% to 5.9%, p<0.01); for advanced imaging was 0.32 tests per admission (95% CI 0.12 to 0.52, p<0.01); and for hospital length of stay was 1.2 additional days per admission (95% CI 0.5 to 1.9, p<0.01). Physician-level differences in length of stay and imaging use were consistent across numerous sensitivity analyses and stable over time. Differences in mortality and readmission were consistent across most sensitivity analyses but were not stable over time and estimates were limited by sample size.

Conclusions

Patient outcomes and resource use in inpatient medical care varied substantially across physicians in this study. Physician-level variations in length of stay and imaging use were unlikely to be explained by patient factors whereas differences in mortality and readmission should be interpreted with caution and could be explained by unmeasured confounders. Physician-level variations may represent practice differences that highlight quality improvement opportunities.

The association of hospital teaching intensity with 30-day postdischarge heart failure readmission and mortality rates

OBJECTIVE

To investigate risk-adjusted, 30-day postdischarge heart failure mortality and readmission rates stratified by hospital teaching intensity.

DATA SOURCES AND STUDY SETTING

A total of 709 221 Medicare fee-for-service beneficiaries discharged from 3135 US hospitals between 1/1/2013 and 11/30/2014 with a principal diagnosis of heart failure.

STUDY DESIGN

Hospitals were classified as Council of Teaching Hospitals and Health Systems (COTH) major teaching hospitals, non-COTH teaching hospitals, and nonteaching hospitals. Hospital teaching status was linked with MedPAR patient data and FY2016 Hospital Readmission Reduction Program penalties. Index hospitalization survival probabilities were estimated with hierarchical logistic regression and used to stratify index hospitalization survivors into severity deciles. Decile-specific models were estimated for 30-day postdischarge readmission and mortality. Thirty-day postdischarge outcomes were estimated by teaching intensity and penalty categories.

PRINCIPAL FINDINGS

Averaged across deciles, adjusted 30-day COTH hospital readmission rates were, on a relative scale ([COTH minus nonteaching] ÷ nonteaching), 1.63 percent higher (95% CI: 0.89 percent, 2.25 percent) than at nonteaching hospitals, but their average adjusted 30-day postdischarge mortality rates were 11.55 percent lower (95% CI: -13.78 percent, -9.37 percent). Penalized COTH hospitals had the highest readmission rates of all categories (23.99 percent [95% CI: 23.50 percent, 24.49 percent]) but the lowest 30-day postdischarge mortality (8.30 percent [95% CI: 7.99 percent, 8.57 percent] vs 9.84 percent [95% CI: 9.69 percent, 9.99 percent] for nonpenalized, nonteaching hospitals).

CONCLUSIONS

Heart failure readmission penalties disproportionately impact major teaching hospitals and inadequately credit their better postdischarge survival.

Biomarkers and sociodemographic factors predicting one-year readmission among liver cirrhosis patients

Background

Hospital readmissions of patients with cirrhosis is a current problem in China. This study aims to estimate the readmission rate at one year after discharge and to identify associated risk factors of hospital readmission.

Methods

Between January 2012 and December 2015, 3,402 patients admitted with cirrhosis were included in the study. The primary outcome was one-year inpatient readmission. Principal components analysis was conducted on the laboratory test indicators to reduce the number of dimensions. Univariate and multivariate analyses were performed using clinical and demographic data to identify independent associated factors of readmission within one year. The odds ratio (OR) and 95% confidence interval were used to assess the strength of association for each factor.

Results

Two dimensions, namely “liver function” and “renal function”, were revealed. Patients with a longer length of stay in the index admission (OR: 1.03; 95% CI: 1.03–1.04) and a higher dimension score of liver function (OR: 1.09; 95% CI: 1.05–1.13) were more likely to be readmitted within one-year. Older patients had a significantly higher odds of one-year readmission than younger patients (OR: 1.61; 95% CI: 1.22–2.11), patients who were married had a higher odds than those who were single (OR: 1.62; 95% CI: 1.12–2.36), and patients with hepatitis C virus were more likely to be readmitted within one year than patients with hepatitis B virus (OR: 1.51; 95% CI: 1.19–1.91).

Conclusion

Biomarkers and sociodemographic factors can identify patients at high risk for readmission within one year. Our data indicates the need to emphasize proper liver care to older patients who have been hospitalized for a long time.

Factors Associated with Practice-Level Performance Indicators in Primary Health Care in Hungary: A Nationwide Cross-Sectional Study

The performance of general practitioners (GPs) is frequently assessed without considering the factors causing variability among general medical practices (GMPs). Our cross-sectional national-based study was performed in Hungary to evaluate the influence of GMP characteristics on performance indicators. The relationship between patient’s characteristics (age, gender, education) and GMP-specific parameters (practice size, vacancy of GP’s position, settlement type, and county of GMP) and the quality of care was assessed by multilevel logistic regression models. The variations attributable to physicians were small (from 0.77% to 17.95%). The education of patients was associated with 10 performance indicators. Practicing in an urban settlement mostly increased the quality of care for hypertension and diabetes care related performance indicators, while the county was identified as one of the major determinants of variability among GPs’ performance. Only a few indicators were affected by the vacancy and practice size. Thus, the observed variability in performance between GPs partially arose from demographic characteristics and education of patients, settlement type, and regional location of GMPs. Considering the real effect of these factors in evaluation would reflect better the personal performance of GPs.

Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance

BACKGROUND

Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown.

METHODS AND RESULTS

We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models.

CONCLUSIONS

Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions.

Factors associated with 30-day readmission for patients hospitalized for seizures

Background

We sought to determine the cumulative incidence of readmissions after a seizure-related hospitalization and identify risk factors and readmission diagnoses.

Methods

We performed a retrospective cohort study of adult patients hospitalized with a primary discharge diagnosis of seizure (International Classification of Diseases, Ninth Edition, Clinical Modification codes 345.xx and 780.3x) using the State Inpatient Databases across 11 states from 2009 to 2012. Hospital and community characteristics were obtained from the American Hospital Association and Robert Wood Johnson Foundation. We performed logistic regressions to explore effects of patient, hospital, and community factors on readmissions within 30 days of discharge.

Results

Of 98,712 patients, 13,929 (14%) were readmitted within 30 days. Reasons for readmission included epilepsy/convulsions (30% of readmitted patients), mood disorders (5%), schizophrenia (4%), and septicemia (4%). The strongest predictors of readmission were diagnoses of CNS tumor (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.9-2.4) or psychosis (OR 1.8, 95% CI 1.7-1.8), urgent index admission (OR 2.0, 95% CI 1.8-2.2), transfer to nonacute facilities (OR 1.7, 95% CI 1.6-1.8), long length of stay (OR 1.7, 95% CI 1.6-1.8), and for-profit hospitals (OR 1.7, 95% CI 1.6-1.8). Our main model's c-statistic was 0.66. Predictors of readmission for status epilepticus included index admission for status epilepticus (OR 3.5, 95% CI 2.6-4.7), low hospital epilepsy volume (OR 0.4, 95% CI 0.3-0.7), and rural hospitals (OR 4.8, 95% CI 2.1-10.9).

Conclusion

Readmission is common after hospitalization for seizures. Prevention strategies should focus on recurrent seizures, the most common readmission diagnosis. Many factors were associated with readmission, although readmissions remain challenging to predict.

Prevalence, Reasons, and Predisposing Factors Associated with 30-day Hospital Readmissions in Poland

There is a growing interest in quality issues associated with hospital care, with readmissions (rehospitalizations) being one of the main areas of interest. Retrospective data from a 914-bed university hospital in Bydgoszcz, Poland, was used to identify 30-day readmissions in 2015. We developed a catalogue of reasons for rehospitalization and differentiated between planned and unplanned readmissions, as well as those related and unrelated to index (initial) hospitalization. Multilevel logistic regression was used to determine factors associated with readmission risk. A total of 12.5% of patients were readmitted within 30 days of being discharged. The highest readmission rates were identified in pediatric, transplantation, and urology patients. The highest share of readmissions was due to the specific nature of a disease and its routine treatment practice. Almost two-thirds of readmission cases were classified as unplanned and related to the index hospitalization. The following characteristics were associated with a higher risk of rehospitalization: female gender, residing >35 km from the hospital, longer than average and very short stays at index admission, higher comorbidity score, and admission to a high-volume hospital sector. Due to the importance of quality issues in health policy, the topic should be further pursued to identify evidence-based practices that would improve hospitals’ performance.

Posthospital Fall Injuries and 30-Day Readmissions in Adults 65 Years and Older

IMPORTANCE

Falls are common among older adults, particularly those with previous falls and cognitive impairment and in the postdischarge period. Hospitals have financial incentives to reduce both inpatient falls and hospital readmissions, yet little is known about whether fall-related injuries (FRIs) are common diagnoses for 30-day hospital readmissions.

OBJECTIVE

To compare fall-related readmissions with other leading rehospitalization diagnoses, including for patients at greatest risk of readmission.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study of the Hospital Cost and Utilization Project's Nationwide Readmissions Database of nationally representative US hospital discharges among Medicare beneficiaries aged 65 years and older from January 1, 2013, to November 30, 2014. The prevalence and ranking of FRIs compared with other diagnostic factors for 30-day unplanned hospital-wide readmissions were determined, overall and for 2 acute geriatric cohorts, classified by fall injury or cognitive impairment diagnoses observed at the index admission. Analyses were also stratified by patient discharge disposition (home, home health care, skilled nursing facility). Analyses were conducted from February 1, 2018, to February 26, 2018.

MAIN OUTCOMES AND MEASURES

Unplanned hospital-wide readmission within 30 days of discharge.

RESULTS

From the database, 8 382 074 eligible index admissions were identified, including 746 397 (8.9%) in the FRI cohort and 1 367 759 (16.3%) in the cognitive impairment cohort. Among the entire 8 382 074-discharge cohort, mean (SD) age was 77.7 (7.8) years and 4 736 281 (56.5%) were female. Overall, 1 205 962 (14.4%) of index admissions resulted in readmission, with readmission rates of 12.9% for those with a previous fall and 16.0% for patients with cognitive impairment. Overall, FRIs ranked as the third-leading readmission diagnosis, accounting for 60 954 (5.1%) of all readmission diagnoses. Within the novel acute geriatric cohorts, FRIs were the second-leading diagnosis for readmission both for patients with an FRI at index admission (10.3% of all readmission diagnoses) and those with cognitive impairment (7.0% of all readmission diagnoses). For those with an FRI at index admission and discharged home or to home health care, FRIs were the leading readmission diagnosis.

CONCLUSIONS AND RELEVANCE

This study found that posthospital FRIs were a leading readmission diagnosis, particularly for patients originally admitted with a FRI or cognitive impairment. Targeting at-risk hospitalized older adults, particularly those discharged to home or home health care, is an underexplored, cost-effective mechanism with potential to reduce readmissions and improve patient care.

To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database

BACKGROUND

It is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmissions on the hospital level after adjusting for case-mix factors.

METHODS

We performed multilevel logistic regression analyses with a random intercept for the factor 'hospital' to estimate the variance on the hospital level after adjustment for case-mix variables. We used administrative data from 53 Dutch hospitals from 2010 to 2012 (58% of all Dutch hospitals; 2,577,053 admissions). We calculated models for the top ten diagnosis groups with the highest number of readmissions after an index admission for a surgical procedure. We calculated intraclass correlation coefficients (ICC) per diagnosis group in order to explore the variation in readmissions between hospitals. Furthermore, we determined C-statistics for the models with and without a random effect on the hospital level to determine the discriminative ability.

RESULTS

The ICCs on the hospital level ranged from 0.48 to 2.70% per diagnosis group. The C-statistics of the models with a random effect on the hospital level ranged from 0.58 to 0.65 for the different diagnosis groups. The C-statistics of the models that included the hospital level were higher compared to the models without this level.

CONCLUSIONS

For some diagnosis groups, a small part of the explained variation in readmissions was found on the hospital level, after adjusting for case-mix variables. However, the C-statistics of the prediction models are moderate, so the discriminative ability is limited. Readmission indicators might be useful for identifying areas for improving quality within hospitals on the level of diagnosis or specialty.

Racial/Ethnic Disparities in Readmissions in US Hospitals: The Role of Insurance Coverage

We examine differences in rates of 30-day readmissions across patients by race/ethnicity and the extent to which these differences were moderated by insurance coverage. We use hospital discharge data of patients in the 18 years and above age group for 5 US states, California, Florida, Missouri, New York, and Tennessee for 2009, the latest year prior to the start of Centers for Medicare & Medicaid Services’ Hospital Compare program of public reporting of hospital performance on 30-day readmissions. We use logistic regression models by state to estimate the association between insurance status, race, and the likelihood of a readmission within 30 days of an index hospital admission for any cause. Overall in 5 states, non-Hispanic blacks had a slightly higher risk of 30-day readmissions relative to non-Hispanic whites, although this pattern varied by state and insurance coverage. We found higher readmission risk for non-Hispanic blacks, compared with non-Hispanic whites, among those covered by Medicare and private insurance, but lower risk among uninsured and similar risk among Medicaid. Hispanics had lower risk of readmissions relative to non-Hispanic whites, and this pattern was common across subgroups with private, Medicaid, and no insurance coverage. Uninsurance was associated with lower risk of readmissions among minorities but higher risk of readmissions among non-Hispanic whites relative to private insurance. The study found that risk of readmissions by racial ethnic groups varies by insurance status, with lower readmission rates among minorities who were uninsured compared with those with private insurance or Medicare, suggesting that lower readmission rates may not always be construed as a good outcome, because it could result from a lack of insurance coverage and poor access to care, particularly among the minorities.

Influence of patient characteristics on preventive service delivery and general practitioners' preventive performance indicators: A study in patients with hypertension or diabetes mellitus from Hungary

BACKGROUND

Regular primary healthcare (PHC) performance monitoring to produce a set of performance indicators for provider effectiveness is a fundamental method for improving guideline adherence but there are potential negative impacts of the inadequate application of this approach. Since performance indicators can reflect patient characteristics and working environments, as well as PHC team contributions, inadequate monitoring practices can reduce their effectiveness in the prevention of cardiometabolic disorders.

OBJECTIVES

To describe the influence of patients' characteristics on performance indicators of PHC preventive practices in patients with hypertension or diabetes mellitus.

METHODS

This cross-sectional analysis was based on a network of 165 collaborating GPs. A random sample of 4320 adults was selected from GP's patient lists. The response rate was 97.3% in this survey. Sociodemographic status, lifestyle, health attitudes and the use of recommended preventive PHC services were surveyed by questionnaire. The relationship between the use of preventive services and patient characteristics were analysed using hierarchical regression models in a subsample of 1659 survey participants with a known diagnosis of hypertension or diabetes mellitus.

RESULTS

Rates of PHC service utilization varied from 18.0% to 97.9%, and less than half (median: 44.4%; IQR: 30.8-62.5) of necessary services were used by patients. Patient attitude was as strong of an influencing factor as demographic properties but was remarkably weaker than patient socioeconomic status.

CONCLUSION

These findings emphasize that PHC performance indicators have to be evaluated concerning patient characteristics.

External validation of the Hospital Frailty Risk Score and comparison with the Hospital-patient One-year Mortality Risk Score to predict outcomes in elderly hospitalised patients: a retrospective cohort study

Objective

Frailty is an important prognostic factor in hospitalised patients but typically requires face-to-face assessment by trained observers to detect. Thus, frail patients are not readily apparent from a systems perspective for those interested in implementing quality improvement measures to optimise their outcomes. This study was designed to externally validate and compare two recently described tools using administrative data as potential markers for frailty: the Hospital Frailty Risk Score (HFRS) and the Hospital-patient One-year Mortality Risk (HOMR) Score.

Design

Retrospective cohort study.

Setting

Ontario, Canada.

Participants

All patients over 75 with at least one urgent non-psychiatric hospitalisation between 2004 and 2010.

Main outcome measures

Prolonged hospital length of stay (>10 days), 30-day mortality after admission and 30-day postdischarge rates of urgent readmission or emergency department (ED) visits.

Results

In 452 785 patients (25.9% with intermediate or high-risk HFRS), increased HFRS was associated with higher Charlson scores, older age and decreased likelihood of baseline independence. Patients with high or intermediate HFRS had significantly increased risks of prolonged hospitalisation (70.0% (OR 8.64, 95%  CI 8.30 to 8.99) or 49.7% (OR 3.66, 95%  CI 3.60 to 3.71) vs 21.3% in low-risk HFRS group) and 30-day mortality (15.5% (OR 1.27, 95% CI 1.20 to 1.33) or 16.8% (OR 1.39, 95%  CI 1.36 to 1.41) vs 12.7% in low-risk), but decreased risks of 30-day readmission (10.0% (OR 0.74, 95%  CI 0.69 to 0.79) and 11.2% (OR 0.84, 95%  CI 0.82 to 0.86) vs 13.1%) or ED visit (7.3% (OR 0.41, 95%  CI 0.38 to 0.45) and 11.1% (OR 0.66, 95%  CI 0.38 to 0.45) vs 16.0%). Although only loosely associated (Pearson correlation coefficient 0.265, p<0.0001), both the HFRS and HOMR Score were independently associated with each outcome—HFRS was more strongly associated with prolonged length of stay (C-statistic 0.71) and HOMR Score was more strongly associated with 30-day mortality (C-statistic 0.71). Both poorly predicted 30-day readmissions (C-statistics 0.52 for HFRS and 0.54 for HOMR Score).

Conclusions

The HFRS best identified hospitalised older patients at higher risk of prolonged length of stay and the HOMR score better predicted 30-day mortality. However, neither score was suitable for predicting risk of readmission or ED visit in the 30 days after discharge. Thus, a single score is inadequate to prognosticate for all outcomes associated with frailty.

Differences Between Skilled Nursing Facilities in Risk of Subsequent Long-Term Care Placement

OBJECTIVES

To determine how the risk of subsequent long-term care (LTC) placement varies between skilled nursing facilities (SNFs) and the SNF characteristics associated with this risk.

DESIGN

Population-based national cohort study with participants nested in SNFs and hospitals in a cross-classified multilevel model.

SETTING

SNFs (N=6,680).

PARTICIPANTS

Fee-for-service Medicare beneficiaries (N=552,414) discharged from a hospital to a SNF in 2013.

MEASUREMENTS

Participant characteristics from Medicare data and the Minimum Data Set. SNF characteristics from Medicare and Nursing Home Compare. Outcome was a stay of 90 days or longer in a LTC nursing home within 6 months of SNF admission.

RESULTS

Within 6 months of SNF admission, 10.4% of participants resided in LTC. After adjustments for participant characteristics, the SNF where a participant received care explained 7.9% of the variance in risk of LTC, whereas the prior hospital explained 1.0%. Individuals in SNFs with excellent quality ratings had 22% lower odds of transitioning to LTC than those in SNFs with poor ratings (odds ratio=0.78, 95% confidence interval=0.74-0.84). Variation between SNFs and associations with quality markers were greater in sensitivity analyses limited to individuals least likely to require LTC. Results were essentially the same in a number of other sensitivity analyses designed to reduce potential confounding.

CONCLUSION

Risk of subsequent LTC placement, an important and negatively viewed outcome for older adults, varies substantially between SNFs. Individuals in higher-quality SNFs are at lower risk.

Effect of Hospital Readmission Reduction on Patients at Low, Medium, and High Risk of Readmission in the Medicare Population

BACKGROUND

Hospitalization and readmission rates have decreased in recent years, with the possible consequence that hospitals are increasingly filled with high-risk patients.

OBJECTIVE

We studied whether readmission reduction has affected the risk profile of hospitalized patients and whether readmission reduction was similarly realized among hospitalizations with low, medium, and high risk of readmissions.

DESIGN

Retrospective study of hospitalizations between January 2009 and June 2015.

PATIENTS

Hospitalized fee-for-service Medicare beneficiaries, categorized into 1 of 5 specialty cohorts used for the publicly reported hospital-wide readmission measure.

MEASUREMENTS

Each hospitalization was assigned a predicted risk of 30-day, unplanned readmission using a risk-adjusted model similar to publicly reported measures. Trends in monthly mean predicted risk for each cohort and trends in monthly observed to expected readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of risk of readmission were assessed using time series models.

RESULTS

Of 47,288,961 hospitalizations, 16.2% (n = 7,642,161) were followed by an unplanned readmission within 30 days. We found that predicted risk of readmission increased by 0.24% (P = .03) and 0.13% (P = .004) per year for hospitalizations in the surgery/ gynecology and neurology cohorts, respectively. We found no significant increase in predicted risk for hospitalizations in the medicine (0.12%, P = .12), cardiovascular (0.32%, P = .07), or cardiorespiratory (0.03%, P = .55) cohorts. In each cohort, observed to expected readmission rates steadily declined, and at similar rates for patients at low, medium, and high risk of readmission.

CONCLUSIONS

Hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. The risk of readmission for hospitalized patients has increased for 2 of 5 clinical cohorts.

Qualitative study of perspectives concerning recent rehospitalisations among a high-risk cohort of veteran patients in Connecticut, USA

OBJECTIVES

Veterans Affairs (VA) patients are at risk for rehospitalisation due to their lower socioeconomic status, older age, poor social support or multiple comorbidities. The study explored inpatients' perceptions about factors contributing to their rehospitalisation and their recommendations to reduce this risk.

DESIGN

Thematic qualitative data analysis of interviews with 18 VA inpatients.

SETTING

VA Connecticut Healthcare System, West Haven Hospital medical inpatient units.

PARTICIPANTS

All were aged 18+ years, rehospitalised within 30 days of most recent discharge, medically stable and competent to provide consent.

MEASUREMENTS

Interviews assessed inpatients' health status after last discharge, reason for rehospitalisation, access to and support from primary care providers (PCP), medication management, home support systems and history of substance use or mental health disorders.

RESULTS

The mean age was 71.6 years (11.1 SD); all were Caucasian, living on limited budgets, and many had serious medical conditions or histories of mental health disorders. Participants considered structural barriers to accessing PCP and limited PCP involvement in medical decision-making as contributing to their rehospitalisation, although most believed that rehospitalisation had been inevitable. Peridischarge themes included beliefs about premature discharge, inadequate understanding of postdischarge plans and insufficiently coordinated postdischarge services. Most highly valued their VA healthcare but recommended increasing PCPs' involvement and reducing structural barriers to accessing primary and specialty care.

CONCLUSIONS

Increased PCP involvement in medical decision-making about rehospitalisation, expanded clinic hours, reduced travel distances, improved communications to patients and their families about predischarge and postdischarge plans and proactive postdischarge outreach to high-risk patients may reduce rehospitalisation risk.

Readmission Patterns After GI Cancer Hospitalizations: The Medical Versus Surgical Patient

PURPOSE

Readmission within 30 days has been used as a metric for quality of care received at hospitals for certain diagnoses. In the era of accountability, value-based care, and increasing cancer costs, policymakers are looking into cancer readmissions as well. It is important to describe the readmission profile of patients with cancer in the most clinically relevant approach to inform policy and health care delivery that can positively impact patient outcomes.

PATIENTS AND METHODS

We conducted a retrospective cohort study using linked Texas Cancer Registry and Medicare claims data. We included elderly Texas residents diagnosed with GI cancer and identified risk factors for unplanned readmission using generalized estimating equations, comparing medical with surgical cancer-related hospitalizations.

RESULTS

We analyzed 69,693 hospitalizations from 31,736 patients. The unplanned readmission rate was higher after medical hospitalizations than after surgical hospitalizations (21.6% v 13.4%, respectively). Shared risk factors for readmission after medical and surgical hospitalizations included advanced disease stage, high comorbidity index, and emergency room visit and radiation therapy within 30 days before index hospitalization. Several other associated factors and reasons for readmission were noted to be unique to medical or surgical hospitalizations alone.

CONCLUSION

Unplanned readmissions among elderly patients with GI cancer are more common after medical hospitalizations compared with surgical hospitalizations. There are shared risk factors and unique risk factors for these hospitalizations that can inform policy, health care delivery, and interventions to reduce readmissions. Other findings underscore the importance of care coordination and comorbidity management in this patient population.

Effect of the Hospital Elder Life Program on Risk of 30-Day Readmission

OBJECTIVES

To compare rates of 30-day readmission between hospital units with a Hospital Elder Life Program (HELP) and control units without HELP.

DESIGN

Retrospective cohort study.

SETTING

The study took place from July 1, 2013, to June 30, 2014, at the University of Pittsburgh Medical Center Shadyside, a 520-bed community teaching hospital that has used HELP since 2002. Eight medical and surgical units with HELP were compared with 10 medical and surgical units without HELP.

PARTICIPANTS

During the study period, HELP units, had 4,794 patients aged 70 and older, and usual care units had 2,834.

INTERVENTION

HELP is a multifactorial, multidisciplinary program that provides targeted interventions for delirium risk factors in at-risk individuals in collaboration with bedside staff.

MEASUREMENTS

Mixed-effects Poisson regression models were used to estimate the adjusted incident risk ratio for 30-day readmission between HELP and usual care units for the overall cohort and for the subgroup of individuals discharged home, with or without services.

RESULTS

Patients on HELP units were more likely than those in usual care units to be older, female, and black and had an unadjusted readmission rate of 16.9%, versus 18.9% for patients on control units. The adjusted risk of readmission was 0.83 (95% confidence interval (CI) = 0.73-0.94, P = .003) for HELP unit patients overall and 0.74 (95% CI = 0.63-0.87, P < .001) for HELP unit patients discharged to home with or without services.

CONCLUSION

The HELP program is associated with lower risk of 30-day hospital readmission overall and for the subgroup of individuals discharged to home. Prospective studies are needed to confirm these observations.

Hospital Variation in Rates of New Institutionalizations Within 6 Months of Discharge

OBJECTIVES

Hospitalization in community-dwelling elderly is often accompanied by functional loss, increasing the risk for continued functional decline and future institutionalization. The primary objective of our study was to examine the hospital-level variation in rates of new institutionalizations among Medicare beneficiaries.

DESIGN

Retrospective cohort study.

SETTING

Hospitals and nursing homes.

PARTICIPANTS

Medicare fee-for-service beneficiaries discharged from 4,469 hospitals in 2013 (N = 4,824,040).

MEASUREMENTS

New institutionalization, defined as new long term care nursing home residence (not skilled nursing facility) of at least 90 days duration within 6 months of hospital discharge.

RESULTS

The overall observed rate of new institutionalizations was 3.6% (N = 173,998). Older age, white race, Medicaid eligibility, longer hospitalization, and having a skilled nursing facility stay over the 6 months before hospitalization were associated with higher adjusted odds. Observed rates ranged from 0.9% to 5.9% across states. The variation in rates attributable to the hospital after adjusting for case-mix and state was 5.1%. Odds were higher for patients treated in smaller (OR = 1.36, 95% CI: 1.27-1.45, ≤50 vs >500 beds), government owned (OR = 1.15, 95% CI: 1.09-1.21 compared to for-profit), limited medical school affiliation (OR = 1.13, 95% CI: 1.07-1.19 compared to major) hospitals and lower for patients treated in urban hospitals (OR = 0.79, 95% CI: 0.76-0.82 compared to rural). Higher Summary Star ratings (OR = 0.75, 95% CI: 0.67-0.93, five vs one stars) and Overall Hospital Rating (OR = 0.62, 95% CI: 0.57-0.67, ratings of 9-10 vs 0) were associated with lower odds of institutionalization.

CONCLUSION

Hospitalization may be a critical period for preventing future institutionalization among elderly patients. The variation in rates across hospitals and its association with hospital quality ratings suggest some of these institutionalizations are avoidable and may represent targets for care improvement.

Rates and risk factors associated with unplanned hospital readmission after fusion for pediatric spinal deformity

BACKGROUND CONTEXT

Short-term readmission rates are becoming widely used as a quality and performance metric for hospitals. Data on unplanned short-term readmission after spine fusion for deformity in pediatric patients are limited.

PURPOSE

To characterize the rate and risk factors for short-term readmission after spine fusion for deformity in pediatric patients.

STUDY DESIGN

This is a retrospective cohort study.

PATIENT SAMPLE

Data were obtained from the State Inpatient Database from New York, Utah, Nebraska, Florida, North Carolina (years 2006-2010), and California (years 2006-2011).

OUTCOME MEASURES

Outcome measures included 30- and 90-day readmission rates.

MATERIALS AND METHODS

Inclusion criteria were patients aged 0-21 years, a primary diagnosis of spine deformity, and a primary 3+-level lumbar or thoracic fusion. Exclusion criteria included revision surgery at index admission and cervical fusion. Readmission rates were calculated and logistic analyses were used to identify independent predictors of readmission.

RESULTS

There were a total of 13,287 patients with a median age of 14 years. Sixty-seven percent were girls. The overall 30- and 90-day readmission rates were 4.7% and 6.1%. The most common reasons for readmission were infection (38% at 30 days and 33% at 90 days), wound dehiscence (19% and 17%), and pulmonary complications (12% and 13%). On multivariate analysis, predictors of 30-day readmission included male sex (p=.008), neuromuscular (p<.0001) or congenital scoliosis (p=.006), Scheuermann kyphosis (p=.003), Medicaid insurance (p<.0001), length of stay of ≤3 days or ≥6 days (p<.0001), and surgery at a teaching hospital (p=.011). Surgery at a hospital performing >80 operations/year was associated with a 34% reduced risk of 30-day readmission (95% confidence interval 12%-50%, p=.005) compared with hospitals performing <20 operations/year.

CONCLUSIONS

The short-term readmission rate for pediatric spine deformity surgery is driven by patient-related factors, as well as several risk factors that may be modified to reduce this rate.

Risk factors for acute care hospital readmission in older persons in Western countries: a systematic review

BACKGROUND

Hospital readmission in older persons is common and reported as a post-discharge adverse outcome from hospitalization. Readmission relates to a mix of factors associated with increasing age, living conditions, progression of disease as well as factors related to the processes of care. To allow health professionals to focus more intensively on patients at risk of readmission, there is a need to identify the characteristics of those patients.

OBJECTIVES

To identify and synthesize the best available evidence on risk factors for acute care hospital readmission within one month of discharge in older persons in Western countries.

INCLUSION CRITERIA TYPES OF PARTICIPANTS

Participants were older persons from Western countries, hospitalized and discharged home or to residential care facilities.

TYPES OF INTERVENTION(S)/PHENOMENA OF INTEREST

The factors of interest considered generic factors related to socio-demographics, health characteristics and clinical and organizational factors related to the care pathway.

TYPES OF STUDIES

The current review considered analytical and descriptive epidemiological study designs that evaluated risk factors for acute care hospital readmission.

OUTCOMES

The outcome was readmission to an acute care hospital within one month of discharge.

SEARCH STRATEGY

A three-step search was utilized to find published and unpublished studies in English, French, German, Norwegian, Swedish or Danish. Five electronic databases were searched from 2004 to 2013, followed by a manual search for additional studies.

METHODOLOGICAL QUALITY

Methodological quality was assessed independently by two reviewers, using the standardized Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) critical appraisal tool.

DATA EXTRACTION

Data were extracted verbatim using a data extraction form, which identified the components from the standardized JBI data extraction tool from JBI-MAStARI and was adapted to the needs of the present review.

DATA SYNTHESIS

Due to the clinical and methodological heterogeneity of the studies included, a narrative summary and metasynthesis of the quantitative findings was conducted.

RESULTS

Based on a review of nine studies from ten Western countries, we found several significant risk factors pertaining to readmission to an acute care hospital within one month of discharge in persons aged 65 years and over. Factors associated with higher risk of hospital readmission covered socio-demographics such as higher age, male gender, ethnicity, living conditions, health characteristics such as poor overall condition and functional disability as well as prior admissions. Organizational factors including length of hospital stay, method of referral and discharge destination were associated with increased risk of acute care hospital readmission.

CONCLUSION

We found several significant, but inconsistent, associations between readmission to an acute care hospital within one month of discharge in persons aged 65 years and over. These associations involved a mix of socio-demographic factors, factors related to health and illness, previous hospitalizations, length of stay as well as clinical and organizational determinants related to the index admission. Although more studies concluded that certain diagnoses or comorbid conditions affected the risk of readmission, they did not agree on any disease in particular.Due to the breadth and diversity of variables examined and the lack of comparability of findings, the impact of these varying factors and their value as risk adjusters and application in different settings and populations are limited.

For many patients who use large amounts of health care services, the need is intense yet temporary

Patients who accumulate multiple emergency department visits and hospital admissions, known as super-utilizers, have become the focus of policy initiatives aimed at preventing such costly use of the health care system through less expensive community- and primary care-based interventions. We conducted cross-sectional and longitudinal analyses of 4,774 publicly insured or uninsured super-utilizers in an urban safety-net integrated delivery system for the period May 1, 2011-April 30, 2013. Our analysis found that consistently 3 percent of adult patients met super-utilizer criteria and accounted for 30 percent of adult charges. Fewer than half of super-utilizers identified as such on May 1, 2011, remained in the category seven months later, and only 28 percent remained at the end of a year. This finding has important implications for program design and for policy makers because previous studies may have obscured this instability at the individual level. Our study also identified clinically relevant subgroups amenable to different interventions, along with their per capita utilization and costs before and after being identified as super-utilizers. Future solutions include improving predictive modeling to identify individuals likely to experience sustained levels of avoidable utilization, better classifying subgroups for whom interventions are needed, and implementing stronger program evaluation designs.

Understanding patient-centred readmission factors: a multi-site, mixed-methods study

IMPORTANCE

Patient concerns at or before discharge inform many transitional care interventions; few studies examine patients' perceptions of self-care and other factors related to readmission.

OBJECTIVES

To characterise patient-reported or caregiver-reported factors contributing to readmission.

DESIGN, SETTING AND PARTICIPANTS

Cross-sectional, national study of general medicine patients readmitted within 30 days at 12 US hospitals. Interviews included multiple-choice survey and open-ended survey questions of patients or their caregivers.

MEASUREMENTS

Multiple-choice survey quantified post-discharge difficulty in seven domains of self-care: medication use, contacting providers, transportation, basic needs (eg, food and shelter), diet, social support and substance abuse. Open-ended responses were coded into themes that added depth to the domains above or captured additional patient-centred concerns.

RESULTS

We interviewed 1066 readmitted patients. 91% reported understanding their discharge plan; however, only 37% reported that providers asked about barriers to carrying out the plan. 52% reported experiencing difficulty in ≥1 self-care domains ranging in frequency from 22% (diet) to 7% (substance use); 26% experienced difficulty in two or more domains. Among 508 patients (48% overall) who reported no difficulties in these domains, two-thirds either could not attribute their readmission to any specific difficulty (34%) or attributed their readmission to progression or persistence of their disease despite following their discharge plan (31%). Only 20% attributed their readmission to early discharge (8%), poor-quality hospital care (6%) or issues such as inadequate discharge instructions or follow-up care (6%).

LIMITATIONS

The study population included only patients readmitted at academic medical centres and may not be representative of community-based care.

CONCLUSION

Patients readmitted within 30 days reported understanding their discharge plans, but frequent difficulties in self-care and low anticipatory guidance for resolving these issues after discharge.

Reliability of 30-Day Readmission Measures Used in the Hospital Readmission Reduction Program

OBJECTIVE

To assess the reliability of risk-standardized readmission rates (RSRRs) for medical conditions and surgical procedures used in the Hospital Readmission Reduction Program (HRRP).

DATA SOURCES

State Inpatient Databases for six states from 2011 to 2013 were used to identify patient cohorts for the six conditions used in the HRRP, which was augmented with hospital characteristic and HRRP penalty data.

STUDY DESIGN

Hierarchical logistic regression models estimated hospital-level RSRRs for each condition, the reliability of each RSRR, and the extent to which socioeconomic and hospital factors further explain RSRR variation. We used publicly available data to estimate payments for excess readmissions in hospitals with reliable and unreliable RSRRs.

PRINCIPAL FINDINGS

Only RSRRs for surgical procedures exceeded the reliability benchmark for most hospitals, whereas RSRRs for medical conditions were typically below the benchmark. Additional adjustment for socioeconomic and hospital factors modestly explained variation in RSRRs. Approximately 25 percent of payments for excess readmissions were tied to unreliable RSRRs.

CONCLUSIONS

Many of the RSRRs employed by the HRRP are unreliable, and one quarter of payments for excess readmissions are associated with unreliable RSRRs. Unreliable measures blur the connection between hospital performance and incentives, and threaten the success of the HRRP.

Self-reflection as a Tool to Increase Hospitalist Participation in Readmission Quality Improvement

BACKGROUND

Reducing 30-day readmissions is a national priority. Although multipronged programs have been shown to reduce readmissions, the role of the individual hospitalist physician in reducing readmissions is not clear.

OBJECTIVES

We evaluated the effect of physicians' self-review of their own readmission cases on the 30-day readmission rate.

METHODS

Over a 1-year period, hospitalists were sent their individual readmission rates and cases on a weekly basis. They reviewed their cases and completed a data abstraction tool. In addition, a facilitator led small group discussion about common causes of readmission and ways to prevent such readmissions.

RESULTS

Our preintervention readmission rate was 16.16% and postintervention was 14.99% (P = .76). Among hospitalists on duty, nearly all participated in scheduled facilitated discussions. Self-review was completed in 67% of the cases.

CONCLUSIONS

A facilitated reflective practice intervention increased hospitalist participation and awareness in the mission to reduce readmissions and this intervention resulted in a nonsignificant trend in readmission reduction.