Hospital Characteristics Associated With Risk-standardized Readmission Rates.
Ontology highlight
ABSTRACT:
SUBMITTER: Horwitz LI
PROVIDER: S-EPMC5426655 | biostudies-literature | 2017 May
REPOSITORIES: biostudies-literature
Publications
Hospital Characteristics Associated With Risk-standardized Readmission Rates.
Horwitz Leora I LI Bernheim Susannah M SM Ross Joseph S JS Herrin Jeph J Grady Jacqueline N JN Krumholz Harlan M HM Drye Elizabeth E EE Lin Zhenqiu Z
Medical care 20170501 5
<h4>Background</h4>Safety-net and teaching hospitals are somewhat more likely to be penalized for excess readmissions, but the association of other hospital characteristics with readmission rates is uncertain and may have relevance for hospital-centered interventions.<h4>Objective</h4>To examine the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR).<h4>Design</h4>This is a retrospective cross-sectional multivariable analysis ...[more]
Similar Datasets
Project description:Medicare penalizes hospitals with higher than expected readmission rates by up to 3% of annual inpatient payments. Expected rates are adjusted only for patients' age, sex, discharge diagnosis, and recent diagnoses.To assess the extent to which a comprehensive set of patient characteristics accounts for differences in hospital readmission rates.Using survey data from the nationally representative Health and Retirement Study (HRS) and linked Medicare claims for HRS participants enrolled in Medicare who were hospitalized from 2009 to 2012 (n = 8067 admissions), we assessed 29 patient characteristics from survey data and claims as potential predictors of 30-day readmission when added to standard Medicare adjustments of hospital readmission rates. We then compared the distribution of these characteristics between participants admitted to hospitals with higher vs lower hospital-wide readmission rates reported by Medicare. Finally, we estimated differences in the probability of readmission between these groups of participants before vs after adjusting for the additional patient characteristics.All-cause readmission within 30 days of discharge.Of the additional 29 patient characteristics assessed, 22 significantly predicted readmission beyond standard adjustments, and 17 of these were distributed differently between hospitals in the highest vs lowest quintiles of publicly reported hospital-wide readmission rates (P ? .04 for all comparisons). Almost all of these differences (16 of 17) indicated that participants admitted to hospitals in the highest quintile of readmission rates were more likely to have characteristics that were associated with a higher probability of readmission. The difference in the probability of readmission between participants admitted to hospitals in the highest vs lowest quintile of hospital-wide readmission rates was reduced by 48% from 4.41 percentage points with standard adjustments used by Medicare to 2.29 percentage points after adjustment for all patient characteristics assessed (reduction in difference: -2.12; 95% CI, -3.33 to -0.67; P =?.003).Patient characteristics not included in Medicare's current risk-adjustment methods explained much of the difference in readmission risk between patients admitted to hospitals with higher vs lower readmission rates. Hospitals with high readmission rates may be penalized to a large extent based on the patients they serve.
| S-EPMC4991542 | biostudies-literature
Project description:ImportanceThe Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.ObjectiveTo identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.Design, setting, and participantsThis cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.ExposuresPresent-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.Main outcomes and measuresHospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no).ResultsIncluded in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001).Conclusions and relevanceThis cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.
| S-EPMC7361656 | biostudies-literature
Project description:BACKGROUND:Whether types of hospitals with high readmission rates also have high overall postdischarge acute care utilization (including emergency department and observation care) is unknown. DESIGN:Cross-sectional analysis. SUBJECTS:Nonfederal United States acute care hospitals. MEASURES:Using methodology established by the Centers for Medicare & Medicaid Services, we calculated each hospital's "excess days in acute care" for fee-for-service (FFS) Medicare beneficiaries aged over 65 years discharged after hospitalization for acute myocardial infarction, heart failure (HF), or pneumonia, representing the mean difference between predicted and expected total days of acute care utilization in the 30 days following hospital discharge, per 100 discharges. We assessed the multivariable association of 8 hospital characteristics with excess days in acute care and the proportion of hospitals with each characteristic that were statistical outliers (95% credible interval estimate does not include 0). RESULTS:We included 2184 hospitals for acute myocardial infarction [228 (10.4%) better than expected, 549 (25.1%) worse than expected], 3720 hospitals for HF [484 (13.0%) better and 840 (22.6%) worse], and 4195 hospitals for pneumonia [673 (16.0%) better, 1005 (24.0%) worse]. Results for all conditions were similar. Worse than expected outliers for pneumonia included: 18.8% of safety net hospitals versus 26.1% of nonsafety net hospitals; 16.7% of public hospitals versus 33.1% of for-profit hospitals; 19.5% of nonteaching hospitals versus 52.2% of major teaching hospitals; 7.9% of rural hospitals versus 42.1% of large urban hospitals; 5.9% of hospitals with 24-<50 beds versus 58% of hospitals with >500 beds; and 29.0% of hospitals with nurse-to-bed ratios >1.0-1.5 versus 21.7% of hospitals with ratios >2.0. CONCLUSIONS:Including emergency department and observation stays in measures of postdischarge utilization produces similar results as measuring only readmissions in that major teaching, urban and for-profit hospitals still perform disproportionately poorly versus nonteaching or public hospitals. However, it enables identification of more outliers and a more granular assessment of the association of hospital factors and outcomes.
| S-EPMC6170884 | biostudies-literature
Project description:ObjectiveTo examine the relationship between community factors and hospital readmission rates.Data sources/study settingWe examined all hospitals with publicly reported 30-day readmission rates for patients discharged during July 1, 2007, to June 30, 2010, with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). We linked these to publicly available county data from the Area Resource File, the Census, Nursing Home Compare, and the Neilsen PopFacts datasets.Study designWe used hierarchical linear models to assess the effect of county demographic, access to care, and nursing home quality characteristics on the pooled 30-day risk-standardized readmission rate.Data collection/extraction methodsNot applicable.Principal findingsThe study sample included 4,073 hospitals. Fifty-eight percent of national variation in hospital readmission rates was explained by the county in which the hospital was located. In multivariable analysis, a number of county characteristics were found to be independently associated with higher readmission rates, the strongest associations being for measures of access to care. These county characteristics explained almost half of the total variation across counties.ConclusionsCommunity factors, as measured by county characteristics, explain a substantial amount of variation in hospital readmission rates.
| S-EPMC4319869 | biostudies-literature
Project description:Background Important administrative-based measures of hospital quality, including those used by Centers for Medicare and Medicaid Services, may not adequately account for patient illness and social factors that vary between hospitals and can strongly affect outcomes. Do-not-resuscitate (DNR) order on admission (within the first 24 hours) is one such factor that may reflect higher preadmission illness burden as well as patients' desire for less-intense therapeutic interventions and has been shown to vary widely between hospitals. We sought to evaluate how accounting for early DNR affected hospital quality measures for acute myocardial infarction. Methods AND RESULTS We identified all patients admitted with acute myocardial infarction using the California State Inpatient Database, which captures early DNR use within 24 hours of admission. We generated hospital risk-standardized mortality and readmissions using random-effects logistic regression, before and after including early DNR status, to examine changes in overall model fit and hospital outlier designations. We included 109?521 patients from 289 hospitals and found that 8.5% (9356) patients had early DNR. Early DNR use varied widely, with median (interquartile range) hospital rates of 7.9% (4.1%-14.0%). Including early DNR in models used to assess hospital quality resulted in improvement in the mortality model (C statistics from 0.754 [0.748-0.759] to 0.784 [0.779-0.789]) but not the readmissions model. Of the hospitals designated high outliers for mortality and readmissions by the Centers for Medicare/Medicaid Services model, and therefore destined for a financial penalty, 6/25 (24%) were reclassified as nonoutliers for mortality and 2/14 (14.3%) for readmissions after including DNR status. Agreement in outlier status between the models before and after inclusion of early DNR status was moderate for mortality (?, 0.603 [0.482-0.724]; P<0.001) and high for readmissions (?, 0.888 [0.800-0.977]; P<0.001). Conclusions Including early DNR status in risk-adjustment models significantly improved model fit and resulted in substantial reclassification of hospital performance rankings for mortality and moderate reclassification for readmissions. DNR status at hospital admission should be considered when reporting risk-standardized hospital mortality.
| S-EPMC6424127 | biostudies-literature
Project description:ImportanceThe Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.ObjectiveTo examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge.Design, setting, and participantsRetrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014.ExposureThirty-day risk-adjusted readmission rate (RARR).Main outcomes and measuresThirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition.ResultsIn 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality.Conclusions and relevanceAmong Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.
| S-EPMC5817448 | biostudies-literature
Project description:BackgroundA hospital's risk-standardized survival rate (RSSR) for in-hospital cardiac arrest has emerged as an important metric to benchmark and incentivize hospital resuscitation quality. We examined whether hospital performance on the RSSR metric was stable or dynamic year-over-year and whether low-performing hospitals were able to improve survival outcomes over time.Methods and resultsWe used data from 84 089 adult patients with an in-hospital cardiac arrest from 166 hospitals with continuous participation in Get With The Guidelines-Resuscitation from 2012 to 2017. A 2-level hierarchical regression model was used to compute RSSRs during a baseline (2012-2013) and two follow-up periods (2014-2015 and 2016-2017). At baseline, hospitals were classified as top-, middle-, and bottom-performing if they ranked in the top 25%, middle 50%, and bottom 25%, respectively, on their RSSR metric during 2012 to 2013. We compared hospital performance on RSSR during follow-up between top, middle, and bottom-performing hospitals' at baseline. During 2012 to 2013, 42 hospitals were identified as top-performing (median RSSR, 31.7%), 82 as middle-performing (median RSSR, 24.6%), and 42 as bottom-performing (median RSSR, 18.7%). During both follow-up periods, >70% of top-performing hospitals ranked in the top 50%, a substantial proportion remained in the top 25% of RSSR during 2014 to 2015 (54.6%) and 2016 to 2017 (40.4%) follow-up periods. Likewise, nearly 75% of bottom-performing hospitals remained in the bottom 50% during both follow-up periods, with 50.0% in the bottom 25% of RSSR during 2014 to 2015 and 40.5% in the bottom 25% during 2016 to 2017. While percentile rankings were generally consistent over time at ≈45% of study hospitals, ≈1 in 5 (21.4%) bottom-performing hospitals showed large improvement in percentile rankings over time and a similar proportion (23.7%) of top-performing hospitals showed large decline in percentile rankings compared with baseline.ConclusionsHospital performance on RSSR during baseline period was generally consistent over 4 years of follow-up. However, 1 in 5 bottom-performing hospitals had large improvement in survival over time. Identifying care and quality improvement innovations at these sites may provide opportunities to improve in-hospital cardiac arrest care at other hospitals.
| S-EPMC7539545 | biostudies-literature
Project description:ImportanceThirty-day readmission rates have been suggested as a marker for quality of care. By investigating the factors associated with readmissions in all otolaryngology subspecialties we provide data relevant for the development of risk stratification systems to improve outcomes.ObjectiveTo establish the association of surgical and hospital volume and patient characteristics with 30-day readmission rates to guide the development of otolaryngology-specific risk stratification models.Design, setting, and participantsA retrospective cohort study including adult patients who underwent inpatient otolaryngology surgery in New York State between 1995 and 2015 was conducted using the Statewide Planning and Research Cooperative System (SPARCS). Regression techniques were used to describe relationships of patient-level factors, hospital, and surgeon volume to 30-day readmission rates in New York State.Main outcomes and measuresThe main outcome measures were patient-, surgeon-, and hospital-level risk factors for readmission. Secondary outcome measures were rate of readmissions by subspecialty procedure and by diagnosis on readmission.ResultsWe identified 254 257 cases of otolaryngology surgery (147 065 women [58%], mean [SD] age 50 [17] years). The 30-day readmission rate was 6%. In a multivariable model, odds ratios (ORs) identified Medicaid insurance (OR, 1.46; 99% CI, 1.36-1.57), Medicare insurance (OR, 1.32; 99% CI, 1.24-1.42), bottom quartile income (OR, 1.08; 99% CI, 1.01-1.15), patient comorbidities measured by the Charlson Comorbidity Index (CCI) (CCI >1; OR, 2.31; 99% CI, 2.16-2.47), length of stay (LOS) (LOS >10 days; OR, 2.29; 99% CI, 2.00-2.45), rhinology (OR, 1.37; 99% CI, 1.24-1.51), laryngology (OR, 1.98; 99% CI, 1.62-2.43), and head and neck cancer (OR, 1.27; 99% CI, 1.17-1.37) procedures as readmission predictors. High-volume surgeons were protective of 30-day readmission (OR, 0.67; 99% CI, 0.635-0.708) relative to low volume. Hospital volume was not significantly associated to readmissions. The most common causes of readmission included wound- (2682 patients, 18%), respiratory- (1776 patients, 12%), cardiovascular- (1210 patients, 8%), and volume- (1089 patients, 7%) related disorders.Conclusions and relevanceThis study evaluated the combined effects of patient-, surgeon-, and hospital-level factors on 30-day readmission after otolaryngology surgery. Socioeconomic factors, patient comorbidities, surgeon volumes, and procedure were significantly associated with 30-day readmission. Though the cause of 30-day readmission is multifactorial, a large portion is driven by socioeconomic factors. Addressing these disparities at the system level is necessary to address the described readmission disparities. The development of risk-stratification models based on patient-, procedure-, and surgeon-level factors may help facilitate resource distribution.
| S-EPMC6481432 | biostudies-literature
Project description:Despite recent improvements in survival after acute myocardial infarction (AMI), U.S. hospitals vary 2-fold in their 30-day risk-standardized mortality rates (RSMRs). Nevertheless, information is limited on hospital-level factors that may be associated with RSMRs.To identify hospital strategies that were associated with lower RSMRs.Cross-sectional survey of 537 hospitals (91% response rate) and weighted multivariate regression by using data from the Centers for Medicare & Medicaid Services to determine the associations between hospital strategies and hospital RSMRs.Acute care hospitals with an annualized AMI volume of at least 25 patients.Patients hospitalized with AMI between 1 January 2008 and 31 December 2009.Hospital performance improvement strategies, characteristics, and 30-day RSMRs.In multivariate analysis, several hospital strategies were significantly associated with lower RSMRs and in aggregate were associated with clinically important differences in RSMRs. These strategies included holding monthly meetings to review AMI cases between hospital clinicians and staff who transported patients to the hospital (RSMR lower by 0.70 percentage points), having cardiologists always on site (lower by 0.54 percentage points), fostering an organizational environment in which clinicians are encouraged to solve problems creatively (lower by 0.84 percentage points), not cross-training nurses from intensive care units for the cardiac catheterization laboratory (lower by 0.44 percentage points), and having physician and nurse champions rather than nurse champions alone (lower by 0.88 percentage points). Fewer than 10% of hospitals reported using at least 4 of these 5 strategies.The cross-sectional design demonstrates statistical associations but cannot establish causal relationships.Several strategies, which are currently implemented by relatively few hospitals, are associated with significantly lower 30-day RSMRs for patients with AMI.The Agency for Healthcare Research and Quality, the United Health Foundation, and the Commonwealth Fund.
| S-EPMC3386642 | biostudies-literature
Project description:ImportanceThe Hospital Readmissions Reduction Program publicly reports and financially penalizes hospitals according to 30-day risk-standardized readmission rates (RSRRs) exclusively among traditional Medicare (TM) beneficiaries but not persons with Medicare Advantage (MA) coverage. Exclusively reporting readmission rates for the TM population may not accurately reflect hospitals' readmission rates for older adults.ObjectiveTo examine how inclusion of MA patients in hospitals' performance is associated with readmission measures and eligibility for financial penalties.Design, setting, and participantsThis is a retrospective cohort study linking the Medicare Provider Analysis and Review file with the Healthcare Effectiveness Data and Information Set at 4070 US acute care hospitals admitting both TM and MA patients. Participants included patients admitted and discharged alive with a diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia between 2011 and 2015. Data analyses were conducted between April 1, 2018, and November 20, 2020.ExposuresAdmission to an acute care hospital.Main outcomes and measuresThe outcome was readmission for any reason occurring within 30 days after discharge. Each hospital's 30-day RSRR was computed on the basis of TM, MA, and all patients and estimated changes in hospitals' performance and eligibility for financial penalties after including MA beneficiaries for calculating 30-day RSRRs.ResultsThere were 748 033 TM patients (mean [SD] age, 76.8 [83] years; 360 692 [48.2%] women) and 295 928 MA patients (mean [SD] age, 77.5 [7.9] years; 137 422 [46.4%] women) hospitalized and discharged alive for AMI; 1 327 551 TM patients (mean [SD] age, 81 [8.3] years; 735 855 [55.4%] women) and 457 341 MA patients (mean [SD] age, 79.8 [8.1] years; 243 503 [53.2%] women) for CHF; and 2 017 020 TM patients (mean [SD] age, 80.7 [8.5] years; 1 097 151 [54.4%] women) and 610 790 MA patients (mean [SD] age, 79.6 [8.2] years; 321 350 [52.6%] women) for pneumonia. The 30-day RSRRs for TM and MA patients were correlated (correlation coefficients, 0.31 for AMI, 0.40 for CHF, and 0.41 for pneumonia) and the TM-based RSRR systematically underestimated the RSRR for all Medicare patients for each condition. Of the 2820 hospitals with 25 or more admissions for at least 1 of the outcomes of AMI, CHF, and pneumonia, 635 (23%) had a change in their penalty status for at least 1 of these conditions after including MA data. Changes in hospital performance and penalty status with the inclusion of MA patients were greater for hospitals in the highest quartile of MA admissions.Conclusions and relevanceIn this cohort study, the inclusion of data from MA patients changed the penalty status of a substantial fraction of US hospitals for at least 1 of 3 reported conditions. This suggests that policy makers should consider including all hospital patients, regardless of insurance status, when assessing hospital quality measures.
| S-EPMC7890527 | biostudies-literature