Risk-standardized Readmission Rates Research Articles

Accounting For Patients' Socioeconomic Status Does Not Change Hospital Readmission Rates.

There is an active public debate about whether patients' socioeconomic status should be included in the readmission measures used to determine penalties in Medicare's Hospital Readmissions Reduction Program (HRRP). Using the current Centers for Medicare and Medicaid Services methodology, we compared risk-standardized readmission rates for hospitals caring for high and low proportions of patients of low socioeconomic status (as defined by their Medicaid status or neighborhood income). We then calculated risk-standardized readmission rates after additionally adjusting for patients' socioeconomic status. Our results demonstrate that hospitals caring for large proportions of patients of low socioeconomic status have readmission rates similar to those of other hospitals. Moreover, readmission rates calculated with and without adjustment for patients' socioeconomic status are highly correlated. Readmission rates of hospitals caring for patients of low socioeconomic status changed by approximately 0.1percent with adjustment for patients' socioeconomic status, and only 3-4percent fewer such hospitals reached the threshold for payment penalty in Medicare's HRRP. Overall, adjustment for socioeconomic status does not change hospital results in meaningful ways.

Community Level Association between Home Health and Nursing Home Performance on Quality and Hospital 30-day Readmissions for Medicare Patients.

We evaluated whether community-level home health agencies and nursing home performance is associated with community-level hospital 30-day all-cause risk-standardized readmission rates for Medicare patients used data from the Centers for Medicare & Medicaid Service from 2010 to 2012. Our final sample included 2,855 communities that covered 4,140 hospitals with 6,751,713 patients, 13,060 nursing homes with 1,250,648 residents, and 7,613 home health agencies providing services to 35,660 zipcodes. Based on a mixed effect model, we found that increasing nursing home performance by one star for all of its 4 measures and home health performance by 10 points for all of its 6 measures is associated with decreases of 0.25% (95% CI 0.17-0.34) and 0.60% (95% CI 0.33-0.83), respectively, in community-level risk-standardized readmission rates.

일반 질 지표로서의 위험도 표준화 재입원율의 적절성

The hospital readmission rate has been widely used as an indicator of the quality of hospital care in many countries. However, the transferrability of this indicator that has been developed in a different health care system can be questioned. We reviewed what should be considered when using the risk-standardized readmission rate (RSRR) as a generic quality indicator in the Korean setting. We addressed the relationship between RSRR and the quality of hospital care, methodological aspects of RSRR, and use of RSRR for external purposes. These issues can influence the validity of the readmission rate as a generic quality indicator. Therefore RSRR should be used with care and further studies are needed to enhance the validity of the readmission rate indicator.

Indication for Lower Extremity Revascularization and Hospital Profiling of Readmissions

Surgical readmissions are common, costly, and the focus of national quality improvement efforts. Given the relatively high readmission rates among vascular patients, pay-for-performance initiatives such as Medicare's Hospital Readmissions Reduction Program (HRRP) have targeted vascular surgery for increased scrutiny in the near future. Yet, the extent to which institutional case mix influences hospital profiling remains unexplored. We sought to evaluate whether higher readmission rates in vascular surgery are a reflection of worse performance or of treating sicker patients. This retrospective observational cohort study of the national Medicare population includes 479,047 beneficiaries undergoing lower extremity revascularization (LER) in 1,701 hospitals from 2005 to 2009. We employed hierarchical logistic regression to mimic Center for Medicare and Medicaid Services methodology accounting for age, gender, preexisting comorbidities, and differences in hospital operative volume. We estimated 30-day risk-standardized readmission rates (RSRR) for each hospital when including (1) all LER patients; (2) claudicants; or (3) high-risk patients (rest pain, ulceration, or tissue loss). We stratified hospitals into quintiles based on overall RSRR for all LERs and examined differences in RSRR for claudicants and high-risk patients between and within quintiles. Next, we evaluated differences in case mix (the proportion of claudicants and high-risk patients treated) across quintiles. Finally, we simulated differences in the receipt of penalties before and after adjusting for hospital case mix. Readmission rates varied widely by indication: 7.3% (claudicants) vs. 19.5% (high risk). Even after adjusting for patient demographics, length of stay, and discharge destination, high-risk patients were significantly more likely to be readmitted (odds ratio 1.76, 95% confidence interval 1.71-1.81). The Best hospitals (top quintile) under the HRRP treated a much lower proportion of high-risk patients compared with the Worst hospitals (bottom quintile) (20% vs. 56%, P<0.001). In the absence of case-mix adjustment, we observed a stepwise increase in the proportion of hospitals penalized as the proportion of high-risk patients treated increased (35-60%, P<0.001). However, after case-mix adjustment, there were no differences between quintiles in the proportion of hospitalized penalized (50-46%, P=0.30). Our findings suggest that the differences in readmission rates following LER are largely driven by hospital case mix rather than true differences in quality.

The Impact of Hospital Size on CMS Hospital Profiling.

The Centers for Medicare & Medicaid Services (CMS) profile hospitals using a set of 30-day risk-standardized mortality and readmission rates as a basis for public reporting. These measures are affected by hospital patient volume, raising concerns about uniformity of standards applied to providers with different volumes. To quantitatively determine whether CMS uniformly profile hospitals that have equal performance levels but different volumes. Retrospective analysis of patient-level and hospital-level data using hierarchical logistic regression models with hospital random effects. Simulation of samples including a subset of hospitals with different volumes but equal poor performance (hospital effects=+3 SD in random-effect logistic model). A total of 1,085,568 Medicare fee-for-service patients undergoing 1,494,993 heart failure admissions in 4930 hospitals between July 1, 2005 and June 30, 2008. CMS methodology was used to determine the rank and proportion (by volume) of hospitals reported to perform "Worse than US National Rate." Percent of hospitals performing "Worse than US National Rate" was ∼40 times higher in the largest (fifth quintile by volume) compared with the smallest hospitals (first quintile). A similar gradient was seen in a cohort of 100 hospitals with simulated equal poor performance (0%, 0%, 5%, 20%, and 85% in quintiles 1 to 5) effectively leaving 78% of poor performers undetected. Our results illustrate the disparity of impact that the current CMS method of hospital profiling has on hospitals with higher volumes, translating into lower thresholds for detection and reporting of poor performance.

Abstract 4: Impact of Financial Penalties in Medicare’s Hospital Readmissions Reductions Program

Background: The Affordable Care Act (ACA) created financial penalties applied to reimbursements after October 1, 2012 for US hospitals for excess risk-standardized readmission rates (RSRRs) for specific conditions. In that context, we sought to determine the effects of health reform financial penalties on hospital readmission rates. Methods: We used a quasi-experimental interrupted time series design with a control group (no penalty hospitals) to analyze the Medicare FFS datafiles. We divided hospitals by initial penalty category. We fitted a piecewise linear model with a change point at passage of the ACA in March 2010 to hospital-specific RSSRs for every quarter from January 2000 to December 2013. We evaluated (1) if RSRRs were declining before and after the law, (2) if any decline accelerated post-law compared to pre-law, and (3) whether the penalized hospitals experienced a faster acceleration in improvement post-law than hospitals with no penalty (control group). Results: In the initial phase of penalties (October 2012-October 2013), 30.1% of hospitals received no penalty, 44.0% received a low penalty (less than half maximum), 16.8% received a high penalty (more than half maximum), and 9.0% received the maximum penalty. Compared to no penalty hospitals, hospitals with the maximum penalty were more likely to be major teaching hospitals (11.2% vs. 6.1%, p &lt; 0.001) and were less likely to be in rural settings (22.9% vs. 29.9%, p &lt; 0.001). Pre-law, combined-condition RSRRs were declining in the no penalty group (p &lt; 0.0001) and increasing for the high penalty and maximum penalty groups (p &lt; 0.0001). Post-law, combined-condition RSRRs declined for all penalty groups (p &lt; 0.0001 for all). Rates of decline accelerated post-law for all penalty groups (p &lt; 0.001 for all), with the greatest acceleration occurring in hospitals sustaining the highest penalties (p &lt; 0.0001 for all comparisons of higher vs. lower penalty groups). Conclusions and Relevance: After health reform, hospital risk-standardized 30-day readmission rates for myocardial infarction, heart failure and pneumonia declined nationally faster than before health reform. Improvement in readmission rates was most marked for hospitals incurring the highest financial penalties.

Relationship between hospital financial performance and publicly reported outcomes.

Hospitals that have robust financial performance may have improved publicly reported outcomes. To assess the relationship between hospital financial performance and publicly reported outcomes of care, and to assess whether improved outcome metrics affect subsequent hospital financial performance. Observational cohort study. Hospital financial data from the Office of Statewide Health Planning and Development in California in 2008 and 2012 were linked to data from the Centers for Medicare and Medicaid Services Hospital Compare website. Hospital financial performance was measured by net revenue by operations, operating margin, and total margin. Outcomes were 30-day risk-standardized mortality and readmission rates for acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia (PNA). Among 279 hospitals, there was no consistent relationship between measures of financial performance in 2008 and publicly reported outcomes from 2008 to 2011 for AMI and PNA. However, improved hospital financial performance (by any of the 3 measures) was associated with a modest increase in CHF mortality rates (ie, 0.26% increase in CHF mortality rate for every 10% increase in operating margin [95% confidence interval: 0.07%-0.45%]). Conversely, there were no significant associations between outcomes from 2008 to 2011 and subsequent financial performance in 2012 (P > 0.05 for all). Robust financial performance is not associated with improved publicly reported outcomes for AMI, CHF, and PNA. Financial incentives in addition to public reporting, such as readmissions penalties, may help motivate hospitals with robust financial performance to further improve publicly reported outcomes. Reassuringly, improved mortality and readmission rates do not necessarily lead to loss of revenue. Journal of Hospital Medicine 2016;11:481-488. © 2016 Society of Hospital Medicine.

Association of Admission to Veterans Affairs Hospitals vs Non–Veterans Affairs Hospitals With Mortality and Readmission Rates Among Older Men Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia

ImportanceLittle contemporary information is available about comparative performance between Veterans Affairs (VA) and non-VA hospitals, particularly related to mortality and readmission rates, 2 important outcomes of care.ObjectiveTo assess and compare mortality and readmission rates among men in VA and non-VA hospitals. Design, Setting, and ParticipantsCross-sectional analysis involving male Medicare fee-for-service beneficiaries aged 65 years or older hospitalized between 2010 and 2013 in VA and non-VA acute care hospitals for acute myocardial infarction (AMI), heart failure (HF), or pneumonia using the Medicare Standard Analytic Files and Enrollment Database together with VA administrative claims data. To avoid confounding geographic effects with health care system effects, we studied VA and non-VA hospitals within the same metropolitan statistical area (MSA).ExposuresHospitalization in a VA or non-VA hospital in MSAs that contained at least 1 VA and non-VA hospital.Main Outcomes and MeasuresFor each condition, 30-day risk-standardized mortality rates and risk-standardized readmission rates for VA and non-VA hospitals. Mean aggregated within-MSA differences in mortality and readmission rates were also assessed.ResultsWe studied 104 VA and 1513 non-VA hospitals, with each condition-outcome analysis cohort for VA and non-VA hospitals containing at least 7900 patients (men; ≥65 years), in 92 MSAs. Mortality rates were lower in VA hospitals than non-VA hospitals for AMI (13.5% vs 13.7%, P = .02; −0.2 percentage-point difference) and HF (11.4% vs 11.9%, P = .008; −0.5 percentage-point difference), but higher for pneumonia (12.6% vs 12.2%, P = .045; 0.4 percentage-point difference). In contrast, readmission rates were higher in VA hospitals for all 3 conditions (AMI, 17.8% vs 17.2%, 0.6 percentage-point difference; HF, 24.7% vs 23.5%, 1.2 percentage-point difference; pneumonia, 19.4% vs 18.7%, 0.7 percentage-point difference, all P < .001). In within-MSA comparisons, VA hospitals had lower mortality rates for AMI (percentage-point difference, −0.22; 95% CI, −0.40 to −0.04) and HF (−0.63; 95% CI, −0.95 to −0.31), and mortality rates for pneumonia were not significantly different (−0.03; 95% CI, −0.46 to 0.40); however, VA hospitals had higher readmission rates for AMI (0.62; 95% CI, 0.48 to 0.75), HF (0.97; 95% CI, 0.59 to 1.34), or pneumonia (0.66; 95% CI, 0.41 to 0.91).Conclusions and RelevanceAmong older men with AMI, HF, or pneumonia, hospitalization at VA hospitals, compared with hospitalization at non-VA hospitals, was associated with lower 30-day risk-standardized all-cause mortality rates for AMI and HF, and higher 30-day risk-standardized all-cause readmission rates for all 3 conditions, both nationally and within similar geographic areas, although absolute differences between these outcomes at VA and non-VA hospitals were small.

Abstract 15: Trends in Performance and Outcomes of Carotid Endarterectomy Among Elderly Medicare Beneficiaries, 2003-2010

Background: Carotid endarterectomy (CEA) is the leading procedure for carotid stenosis, yet national data on trends in rates and outcomes are limited. We determined CEA rates among Medicare beneficiaries and evaluated mortality and readmission over 8 years. Methods: We used Medicare fee-for-service data to identify beneficiaries aged ≥65y who had their first CEA (ICD-9 38.12) from 2003-2010 and calculated annual rates per 100,000 person-years (PY). We fit mixed models to assess trends in patient-level outcomes, adjusting for demographics, comorbidities, and symptomatic status. We also evaluated hospital-level trends by calculating risk-standardized mortality (RSMR) and readmission (RSRR) rates. A spatial mixed model adjusted for age, sex, and race was fit to calculate county-specific risk-standardized CEA rates in 2003-2004. Results: There were 505,966 unique CEA hospitalizations. The annual number of CEA discharges decreased from 81,604 in 2003 to 47,597 in 2010 (42% decrease), though the patient characteristics remained largely similar. The national CEA rate was 283 per 100,000 PY in 2003, and there was considerable geographic variability (Figure A). This rate decreased each year to a low of 172 per 100,000 PY in 2010. The rate of stroke or death within 30 days decreased from 3.2 to 2.7%, with a significant adjusted annual reduction of 3% (Figure B). Annual reductions in other short- and long-term outcomes were similar, ranging from 2-3%. The median hospital-level 30-day RSMR decreased over time from 0.99 to 0.57%, while the variation between hospitals increased (interquartile range of 0.7-1.67 percentage points). The 30-day RSRR decreased from 11.0 to 10.1%, but there was more homogeneity across hospitals and years. Conclusions: CEA use among Medicare patients decreased dramatically from 2003-2010, while mortality and readmission outcomes improved. The relative importance of biological and sociological mechanisms for these trends merits further study.

Abstract TMP61: Outcomes After Ischemic Stroke Among “America’s Best Hospitals”

Background: The ranking of “America’s Best Hospitals” by the US News &amp; World Report is a popular hospital-profiling system, but it is not known whether the 50 top hospitals for Neurology and Neurosurgery have lower 30-day risk-standardized mortality (RSMR) and readmission rates (RSRRs) after stroke compared with other hospitals, or whether outcomes are comparable among these 50 top hospitals. Methods: The 50 top hospitals for adult Neurology and Neurosurgery were identified from the 2015-16 US News &amp; World Report rankings. CMS Hospital Compare provided RSMRs and RSRRs within 30 days of ischemic stroke for hospitals with ≥25 stroke cases/year. We fit a mixed model to assess differences in RSMRs and RSRRs between top-ranked vs other hospitals, adjusted for hospital characteristics and weighted by hospital volume. Among the 50 top-ranked hospitals, we explored whether ranking order was associated with outcomes. Results: Among 2718 hospitals, the 50 top-ranked vs other hospitals had lower mean 30-day mortality (RSMR 14.8% vs 15.3%, p&lt;.01) but higher readmission (RSRR 14.5% vs 13.4%, p&lt;.01). These patterns persisted in adjusted analyses; top hospitals had lower mortality by 0.98% (95% CI -1.36%, -0.60%) and higher readmission by 0.66% (0.41%, 0.92%). Among top hospitals, there was considerable variability in risk-standardized outcomes, ranging from 9.5-19.3% for mortality and 10.8-17.5% for readmission. RSMRs were lower than the national average for 14% of top hospitals while RSRRs were lower for only 2% and higher for 14% (figure). Hospital rank was not significantly associated with either mortality (RSMR 0.04% increase with each rank, 95% CI 0.0%, 0.08%) or readmission (RSRR -0.01%; 95% CI -0.04%, 0.02%) among the 50 top-ranked hospitals. Conclusions: Admission to a top-ranked hospital for Neurology or Neurosurgery was associated with lower 30-day risk-standardized mortality but higher readmission. There was considerable heterogeneity among the top 50 hospitals.

Does Use of a Hospital-wide Readmission Measure Versus Condition-specific Readmission Measures Make a Difference for Hospital Profiling and Payment Penalties?

The Centers for Medicare and Medicaid Services (CMS) use public reporting and payment penalties as incentives for hospitals to reduce readmission rates. In contrast to the current condition-specific readmission measures, CMS recently developed an all-condition, 30-day all-cause hospital-wide readmission measure (HWR) to provide a more comprehensive view of hospital performance. We examined whether assessment of hospital performance and payment penalties depends on the readmission measure used. We used inpatient data to examine readmissions for patients discharged from VA acute-care hospitals from Fiscal Years 2007-2010. We calculated risk-standardized 30-day readmission rates for 3 condition-specific measures (heart failure, acute myocardial infarction, and pneumonia) and the HWR measure, and examined agreement between the HWR measure and each of the condition-specific measures on hospital performance. We also assessed the effect of using different readmission measures on hospitals' payment penalties. We found poor agreement between the condition-specific measures and the HWR measure on those hospitals identified as low or high performers (eg, among those hospitals classified as poor performers by the heart failure readmission measure, only 28.6% were similarly classified by the HWR measure). We also found differences in whether a hospital would experience payment penalties. The HWR measure penalized only 60% of those hospitals that would have received penalties based on at least 1 of the condition-specific measures. The condition-specific measures and the HWR measure provide a different picture of hospital performance. Future research is needed to determine which measure aligns best with CMS's overall goals to reduce hospital readmissions and improve quality.

Abstract TMP69: Association Between Skilled Nursing Facility Quality Indicators and Hospital Readmissions After Stroke

Background: Reducing readmission within 30 days of hospital discharge is a national priority. Care in high-quality skilled nursing facilities (SNFs) could lower readmission rates, but its impact on stroke readmissions at the community level has not been assessed. We evaluated the relationships of SNF quality metrics with readmissions within 30 days of stroke discharge Methods: We used 2010-2012 data from the CMS Nursing Home Compare 5-star quality rating system of 4 metrics (health inspections, quality performance, and registered nurse and total staffing hours/resident/day; each rated from 1 to 4 stars). Hospital 30-day risk-standardized readmission rates after ischemic stroke were obtained from CMS Hospital Compare. Hospital and SNF zip codes were used to aggregate data to the healthcare community level (defined by NCHS Health Service Areas). We fit mixed models, weighted by community-specific total hospital discharges, to evaluate the relationship between community-specific SNF quality ratings and readmissions. Results: There were 1,813 healthcare communities with a total of 1,170,817 residents across 11,989 SNFs and 2,603 unique hospitals. Within a community, there was a median of 4 SNFs per hospital. Among 482,124 discharges for stroke during the study period, the median community-specific hospital 30-day readmission rate was 13.3% (IQR 1.62). An increase of 1 star for each of the 4 metrics in the SNF quality composite was associated with a 0.42% decrease in the stroke readmission rate (figure). Among the individual metrics, only the registered nurse and total nurse staffing measures were associated with lower stroke readmissions. Conclusions: Hospitals located in healthcare communities with higher-performing SNFs had lower 30-day readmission rates after stroke. Efforts to increase SNF nurse staffing levels may represent an opportunity to decrease 30-day readmissions after stroke.

Integrating new practices: a qualitative study of how hospital innovations become routine.

BackgroundHospital quality improvement efforts absorb substantial time and resources, but many innovations fail to integrate into organizational routines, undermining the potential to sustain the new practices. Despite a well-developed literature on the initial implementation of new practices, we have limited knowledge about the mechanisms by which integration occurs.MethodsWe conducted a qualitative study using a purposive sample of hospitals that participated in the State Action on Avoidable Rehospitalizations (STAAR) initiative, a collaborative to reduce hospital readmissions that encouraged members to adopt new practices. We selected hospitals where risk-standardized readmission rates (RSRR) had improved (n = 7) or deteriorated (n = 3) over the course of the first 2 years of the STAAR initiative (2010–2011 to 2011–2012) and interviewed a range of staff at each site (90 total). We recruited hospitals until reaching theoretical saturation. The constant comparative method was used to conduct coding and identification of key themes.ResultsWhen innovations were successfully integrated, participants consistently reported that a small number of key staff held the innovation in place for as long as a year while more permanent integrating mechanisms began to work. Depending on characteristics of the innovation, one of three categories of integrating mechanisms eventually took over the role of holding new practices in place. Innovations that proved intrinsically rewarding to the staff, by making their jobs easier or more gratifying, became integrated through shifts in attitudes and norms over time. Innovations for which the staff did not perceive benefits to themselves were integrated through revised performance standards if the innovation involved complex tasks and through automation if the innovation involved simple tasks.ConclusionsHospitals have an opportunity to promote the integration of new practices by planning for the extended effort required to hold a new practice in place while integration mechanisms take hold. By understanding how integrating mechanisms correspond to innovation characteristics, hospitals may be able to foster integrating mechanisms most likely to work for particular innovations.

건강보험 청구자료를 이용한 일반 질 지표로서의 위험도 표준화 재입원율 산출: 방법론적 탐색과 시사점

Background: The purpose of this study was to propose a method for developing a measure of hospital-wide all-cause risk-standard -ized readmissions using administrative claims data in Korea and to discuss further considerations in the refinement and implemen-tation of the readmission measure.Methods: By adapting the methodology of the United States Center for Medicare & Medicaid Services for creating a 30-day read-mission measure, we developed a 6-step approach for generating a comparable measure using Korean datasets. Using the 2010 Korean National Health Insurance (NHI) claims data as the development dataset, hierarchical regression models were fitted to calcu-late a hospital-wide all-cause risk-standardized readmission measure. Six regression models were fitted to calculate the readmission rates of six clinical condition groups, respectively and a single, weighted, overall readmission rate was calculated from the readmis-sion rates of these subgroups. Lastly, the case mix differences among hospitals were risk-adjusted using patient-level comorbidity variables. The model was validated using the 2009 NHI claims data as the validation dataset.Results: The unadjusted, hospital-wide all-cause readmission rate was 13.37%, and the adjusted risk-standardized rate was 10.90%, varying by hospital type. The highest risk-standardized readmission rate was in hospitals (11.43%), followed by general hospitals (9.40%) and tertiary hospitals (7.04%).Conclusion: The newly developed, hospital-wide all-cause readmission measure can be used in quality and performance evalua-tions of hospitals in Korea. Needed are further methodological refinements of the readmission measures and also strategies to im-plement the measure as a hospital performance indicator.Keywords: Patient readmission; Patient readmission; Quality indicators; Risk-adjustment; Hospital

Unplanned Readmissions After Hospitalization for Severe Sepsis at Academic Medical Center-Affiliated Hospitals.

In the United States, national efforts to reduce hospital readmissions have been enacted, including the application of substantial insurance reimbursement penalties for hospitals with elevated rates. Readmissions after severe sepsis remain understudied and could possibly signify lapses in care and missed opportunities for intervention. We sought to characterize 7- and 30-day readmission rates following hospital admission for severe sepsis as well as institutional variations in readmission. Retrospective analysis of 345,657 severe sepsis discharges from University HealthSystem Consortium hospitals in 2012. United States. We applied the commonly cited method described by Angus et al for identification of severe sepsis, including only discharges with sepsis present at admission. None. We identified unplanned, all-cause readmissions within 7 and 30 days of discharge using claims-based algorithms. Using mixed-effects logistic regression, we determined factors associated with 30-day readmission. We used risk-standardized readmission rates to assess institutional variations. Among 216,328 eligible severe sepsis discharges, there were 14,932 readmissions within 7 days (6.9%; 95% CI, 6.8-7.0) and 43,092 within 30 days (19.9%; 95% CI, 19.8-20.1). Among those readmitted within 30 days, 66.9% had an infection and 40.3% had severe sepsis at readmission. Patient severity, length of stay, and specific diagnoses were associated with increased odds of 30-day readmission. Observed institutional 7-day readmission rates ranged from 0% to 12.3%, 30-day rates from 3.6% to 29.1%, and 30-day risk-standardized readmission rates from 14.1% to 31.1%. Greater institutional volume, teaching status, trauma services, location in the Northeast, and lower ICU rates were associated with poor risk-standardized readmission rate performance. Severe sepsis readmission places a substantial burden on the healthcare system, with one in 15 and one in five severe sepsis discharges readmitted within 7 and 30 days, respectively. Hospitals and clinicians should be aware of this important sequela of severe sepsis.

Validation of the Readmission Risk Score in Heart Failure Patients at a Tertiary Hospital

BackgroundThe Readmission Risk score (RR score) is a software application developed to identify patients at increased risk for readmission. This score was developed to improve on the methodology for 30-day risk-standardized all-cause readmission rates (RSRRs) used by the Centers for Medicare and Medicaid Services for its quality reporting system. However, the utility of the RR score in clinical practice has not been independently validated. Methods and ResultsWe included patients admitted with the primary discharge diagnosis of congestive heart failure (CHF) from September 2011 to August 2013. Data on individual components of the RR score were obtained by means of detailed chart review. We calculated the RR score of all admissions and examined its ability to predict 30-day all-cause readmission. We repeated the analysis by randomly selecting 1 admission per patient and also by including only those ≥65 years old. A total of 1,046 admissions met the inclusion criteria. Of these, 369 (35.28%) were readmitted within 30 days of discharge. The performance of the RR score was poor, with an area under the receiver operating characteristic curve (AUC) of 0.61 (95% confidence interval [CI] 0.57–0.64) for all age groups and 0.59 (95% CI 0.53–0.64) for patients aged ≥65 years. The AUC for the RR score was 0.58 (95% CI 0.50–0.65) in a randomly selected patient-level model. However, patients in the highest quartile of RR score were twice as likely to be readmitted as those in the lowest quartile (47.24% vs 24.69%; P < .001). The sensitivity and specificity of the RR score in predicting all cause readmissions were poor. ConclusionBased on our single-institution data, patients with CHF readmitted within 30 days had a higher RR score than those not readmitted. The ability of the RR score to predict future all-cause readmission was modest at best.

Hospital readmissions reduction program.

Hospital readmission measures have been touted not only as a quality measure but also as a means to bend the healthcare cost curve. The Affordable Care Act (ACA) established the Hospital Readmission Reduction Program (HRRP) in 2012. Under this program, hospitals are financially penalized if they have higher-than-expected risk-standardized 30-day readmission rates for acute myocardial infarction, heart failure, and pneumonia. The HRRP has garnered significant attention from the medical community, both positive and negative. Here, we describe the reasons the HRRP was implemented, the penalties levied, the impact it has had on transitional care and readmissions, the pros and cons of the policy, and its future. Hospital readmissions are associated with unfavorable patient outcomes and high financial costs.1,2 Causes of readmissions are multifactorial, and rates vary substantially by institution.3,4 Historically, nearly 20% of all Medicare discharges had a readmission within 30 days.1 The Medicare Payment Advisory Commission has estimated that 12% of readmissions are potentially avoidable. Preventing even 10% of these readmissions could save Medicare $1 billion.5 Therefore, reducing hospital readmissions has been made a national priority. In 2008, the Medicare Payment Advisory Commission recommended to Congress that the Centers for Medicare & Medicaid Services (CMS) begin confidentially reporting readmission rates and resource use to hospitals and physicians.6 In 2009, CMS began publicly reporting hospital-level readmission rates, which were added to the Hospital Compare Web site.7 Before 2012, hospitals had little direct financial incentive to reduce readmissions. For Medicare beneficiaries with inpatient stays, hospitals receive payment with the inpatient prospective payment system (IPPS). This payment, based on a diagnosis-related group (DRG), covers the inpatient stay and any outpatient diagnostic and admission-related outpatient nondiagnostic services provided by the institution on the date of the patient’s admission or within 3 days immediately …

Abstract 371: Proportion of Readmissions to Non-Index Hospitals is Associated with All-Cause Risk-Adjusted Readmission Rate for Medicare Patients with AMI and CHF

Background: The Affordable Care Act introduced the Hospital Readmissions Reduction Program (HRRP), implementing financial penalties for hospitals with excess risk-standardized readmission rates (RSRRs) for specific diagnoses including congestive heart failure (CHF) and acute myocardial infarction (AMI). Hospitals may have more ability to reduce readmission rates for readmissions at their own hospitals as opposed to other hospitals. We hypothesize that hospitals with higher proportions of patients re-admitted to a different (“non index”) hospital also have higher all-cause 30-day RSRRs. Methods: We used a national sample of Medicare data to identify hospitalizations for CHF and AMI and subsequent all-cause 30-day readmissions using data from 5,291 hospitals for the period 1999 to 2010. For each hospital we calculated the proportion of readmissions at a different (“non-index”) hospital and the all-cause 30-day risk-adjusted readmission rate (RSRR). We then fitted a regression model to estimate the relationship between the proportion of admissions to the non-index hospital and all-cause RSRRs across hospitals. Results: We identified 2,863,696 discharges for AMI and 6,981,895 discharged for CHF for the period 1999 to 2010. On average across hospitals, the percentage of readmissions to non-index hospitals was 28.0% (SD 25.4%) for AMI and 22.7% (SD 17.8%) for CHF. Hospitals with higher RSRRs had higher percentages of their readmissions occurring at a different hospital for both AMI and CHF (p &lt; 0.001 for both conditions). Due to this association, hospitals in the highest decile for 30-day RSRR had 29.1% and 20.7% of readmissions occur at non-index hospitals for AMI and CHF, respectively, while the corresponding proportions for hospitals in the lowest decile were 19.1% and 18.4%. Conclusions: Compared to high performing hospitals, hospitals with the worst 30-day readmission rates for AMI and CHF also have the highest percentage of those readmissions occurring at hospitals other than their own. The relationship was particularly strong for AMI. These results suggest that the proportion of readmissions to other hospitals might be a structural disadvantage associated with higher readmission rates.

Using joint registry data from FORCE-TJR to improve the accuracy of risk-adjustment prediction models for thirty-day readmission after total hip replacement and total knee replacement.

In 2009, the National Quality Forum (NQF) published the rates of complications following primary total hip replacement and total knee replacement; these complication rates across U.S. hospitals ranged from a mean of 2.2% to 8.9%, with a median prevalence of 4.2%1. The NQF concluded that “this variation likely indicates differences in quality of care received across hospitals.” In September 2012, the Centers for Medicare & Medicaid Services (CMS) issued preliminary hospital-specific reports of thirty-day risk-standardized readmission rates after elective primary total hip and/or knee arthroplasty and total adverse event rates2. CMS is now implementing routine, hospital-specific, public total joint replacement quality reports based on these measures to compare risk-adjusted thirty-day outcomes. The recent release of publicly reported total joint replacement outcomes illustrates the growing importance that public health officials place on measuring and comparing outcomes after total joint replacement across hospitals and surgeons. While the CMS methods represent an important first step, the CMS risk-adjustment methodology raises a number of concerns. First, the CMS models are based solely on administrative billing data and International Classification of Diseases, Ninth Revision (ICD-9) comorbidity codes and lack surgeon and patient-reported health information. Surgeon-reported clinical risk factors are often central to quality measurement systems, as highlighted in the coronary artery bypass grafting (CABG) outcome reports of the Society of Thoracic Surgeons3. To advocate for the addition of clinical risk factors in comparative data for total joint replacement, arthroplasty surgeons need a clearer understanding of key preoperative risk factors that influence postoperative outcomes. From the public perspective, it is important that patients with severe knee or hip conditions have full access to expert surgeons and be aware of the impact of their preoperative condition on outcomes. The second major limitation of the CMS reports is that the analyses are …

Readmissions After Carotid Artery Revascularization in the Medicare Population

BackgroundIn appropriately selected patients with severe carotid stenosis, carotid revascularization reduces ischemic stroke. Prior clinical research has focused on the efficacy and safety of carotid revascularization, but few investigators have considered readmission as a clinically important outcome. ObjectivesThe aims of this study were to examine frequency, timing, and diagnoses of 30-day readmission following carotid revascularization; to assess differences in 30-day readmission between patients undergoing carotid endarterectomy (CEA) and carotid artery stenting (CAS); to describe hospital variation in risk-standardized readmission rates (RSRR); and to examine whether hospital variation in the choice of procedure (CEA vs. CAS) is associated with differences in RSRRs. MethodsWe used Medicare fee-for-service administrative claims data to identify acute care hospitalizations for CEA and CAS from 2009 to 2011. We calculated crude 30-day all-cause hospital readmissions following carotid revascularization. To assess differences in readmission after CAS compared with CEA, we used Kaplan-Meier survival curves and fitted mixed-effects logistic regression. We estimated hospital RSRRs using hierarchical generalized logistic regression. We stratified hospitals into 5 groups by their proportional CAS use and compared hospital group median RSRRs. ResultsOf 180,059 revascularizations from 2,287 hospitals, CEA and CAS were performed in 81.5% and 18.5% of cases, respectively. The unadjusted 30-day readmission rate following carotid revascularization was 9.6%. Readmission risk after CAS was greater than that after CEA. There was modest hospital-level variation in 30-day RSRRs (median: 9.5%; range 7.5% to 12.5%). Variation in proportional use of CAS was not associated with differences in hospital RSRR (range of median RSRR across hospital groups 9.49% to 9.55%; p = 0.771). ConclusionsAlmost 10% of Medicare patients undergoing carotid revascularization were readmitted within 30 days of discharge. Compared with CEA, CAS was associated with a greater readmission risk. However, hospitals' RSRR did not differ by their proportional CAS use.