Local Supply Of Postdischarge Care Options Tied To Hospital Readmission Rates.
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ABSTRACT:
SUBMITTER: Griffith KN
PROVIDER: S-EPMC9342702 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
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Local Supply Of Postdischarge Care Options Tied To Hospital Readmission Rates.
Griffith Kevin N KN Schwartzman David A DA Pizer Steven D SD Bor Jacob J Kolachalama Vijaya B VB Jack Brian B Garrido Melissa M MM
Health affairs (Project Hope) 20220701 7
The extent to which patients' risk for readmission after a hospitalization is influenced by local availability of postdischarge care options is not currently known. We used national, hospital-level data to assess whether the supply of postdischarge care options in hospitals' catchment areas was associated with readmission rates for Medicare patients after hospitalizations for acute myocardial infarction, heart failure, or pneumonia. Overall, readmission rates were negatively associated with per ...[more]
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Project description:ObjectiveTo investigate risk-adjusted, 30-day postdischarge heart failure mortality and readmission rates stratified by hospital teaching intensity.Data sources and study settingA 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 designHospitals 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 findingsAveraged 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).ConclusionsHeart failure readmission penalties disproportionately impact major teaching hospitals and inadequately credit their better postdischarge survival.
| S-EPMC7080393 | biostudies-literature
Project description:BackgroundWhether types of hospitals with high readmission rates also have high overall postdischarge acute care utilization (including emergency department and observation care) is unknown.DesignCross-sectional analysis.SubjectsNonfederal United States acute care hospitals.MeasuresUsing 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).ResultsWe 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.ConclusionsIncluding 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:ImportanceHealth care systems, payers, and hospitals use hospital readmission rates as a measure of quality. Although hospitals can track readmissions back to themselves (hospital A to hospital A), they lack information when their patients are readmitted to different hospitals (hospital A to hospital B). Because hospitals lack different-hospital readmission (DHR) data, they may underestimate all-hospital readmission (AHR) rates (hospital A to hospital A or B).ObjectivesTo determine the prevalence of 30-day pediatric DHRs; to assess the effect of DHR on readmission performance; and to identify patient and hospital characteristics associated with DHR.Design, setting, and participantsWe analyzed all-payer inpatient claims for 701,263 pediatric discharges (patients aged 0-17 years) from 177 acute care hospitals in New York State from January 1, 2005, through November 30, 2009, to identify 30-day same-hospital readmissions (SHRs), DHRs, and AHRs. Data analysis was performed from March 12, 2013, through April 6, 2015. We compared excess readmission ratios (calculated per the Medicare formula) using SHRs and AHRs to determine what might happen if the federal formula were applied to a specific state and to evaluate how often hospitals might accurately anticipate-using data available to them--whether they would incur penalties (excess readmission ratio >1) for readmissions. Using multivariate logistic regression, we identified patient- and hospital-level predictors of DHR vs SHR.Main outcomes and measuresThe proportion of DHRs vs SHRs, AHR and SHR rates, and excess readmissions.ResultsDifferent-hospital readmissions constituted 13.9% of 31,325 AHRs. At the individual hospital level, the median (interquartile range) percentage of DHRs was 21.6% (12.8%-39.1%). The median (interquartile range) adjusted AHR rate was 3.4% (3.0%-4.1%), 38.9% higher than the median adjusted SHR rate of 2.5% (2.0%-3.4%) (P < .001). Excess readmission ratios using SHRs inaccurately anticipated penalties (changed from >1 to ≤ 1 or vice versa) for 20 of the 177 hospitals (11.3%); all were nonchildren's hospitals and 18 of 20 (90.0%) were nonteaching hospitals. Characteristics associated with higher odds ratios (ORs) (reported with 95% CIs) of DHR in multivariate analyses included being younger (compared with age <1 year, ORs [95% CIs] for the other age categories ranged from 0.76 [0.66-0.88] to 0.85 [0.73-0.99]); being white (ORs [95% CIs] for nonwhite race/ethnicity ranged from 0.74 [0.65-0.84] to 0.88 [0.79-0.99]); having private insurance (1.14 [1.04-1.24]); having a chronic condition indicator for a mental disorder (1.33 [1.13-1.56]) or a disease of the nervous system (1.37 [1.20-1.57]) or circulatory system (1.20 [1.00-1.43]); and admission to a nonchildren's (1.62 [1.01-2.60]), urban (ORs for nonurban hospitals ranged from 0.35 [0.24-0.52] to 0.36 [0.21-0.64]), or lower-volume (0.73 [0.64-0.84]) hospital (P < .05 for each).Conclusions and relevanceDifferent-hospital readmissions differentially affect hospitals' pediatric readmission rates and anticipated performance, making SHRs an incomplete surrogate for AHRs-particularly for certain hospital types. Failing to incorporate DHRs into readmission measurement may impede quality assessment, anticipation of penalties, and quality improvement.
| S-EPMC5336323 | 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:BackgroundHospital readmission within 30 days of discharge (30-d readmission) is an undesirable outcome. Readmission of patients with diabetes is common and costly. Most of the studies that have examined readmission risk factors among diabetes patients did not include potentially important clinical data.ObjectivesTo provide a more comprehensive understanding of 30-day readmission risk factors among patients with diabetes based on predischarge and postdischarge data.Research designIn this retrospective cohort study, 48 variables were evaluated for association with readmission by multivariable logistic regression.SubjectsIn total, 17,284 adult diabetes patients with 44,203 hospital discharges from an urban academic medical center between January 1, 2004 and December 1, 2012.MeasuresThe outcome was all-cause 30-day readmission. Model performance was assessed by c-statistic.ResultsThe 30-day readmission rate was 20.4%, and the median time to readmission was 11 days. A total of 27 factors were statistically significant and independently associated with 30-day readmission (P<0.05). The c-statistic was 0.82. The strongest risk factors were lack of a postdischarge outpatient visit within 30 days, hospital length-of-stay, prior discharge within 90 days, discharge against medical advice, sociodemographics, comorbidities, and admission laboratory values. A diagnosis of hypertension, preadmission sulfonylurea use, admission to an intensive care unit, sex, and age were not associated with readmission in univariate analysis.ConclusionsThere are numerous risk factors for 30-day readmission among patients with diabetes. Postdischarge factors add to the predictive accuracy achieved by predischarge factors. A better understanding of readmission risk may ultimately lead to lowering that risk.
| S-EPMC6082658 | 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:IntroductionLimited evidence is available regarding the postdischarge economic and readmission burdens of hyperkalemia.MethodsUsing the IBM MarketScan Commercial and Medicare-Supplemental Claims database (January 1, 2010-December 31, 2014), adult patients with a hospitalization with a hyperkalemia diagnosis (ICD-9-CM 276.7, hyperkalemia cohort) were 1:1 matched with patients with a hospitalization without evidence of hyperkalemia (nonhyperkalemia cohort) on age, chronic kidney disease stage, heart failure, dialysis, renin-angiotensin-aldosterone system inhibitor use, and major diagnostic categories of the hospitalization. All-cause health care costs and health care resource utilization measures were compared between cohorts during the 1-year postdischarge period. Postdischarge readmission and length of stay (LOS) were compared between hyperkalemia-related hospitalizations from the hyperkalemia cohort and matched hospitalizations unrelated to hyperkalemia from the nonhyperkalemia cohort.ResultsPatients with hyperkalemia-related hospitalizations (n = 4426) incurred $30,379 (95% confidence interval, $25,423-$35,335) higher 1-year total all-cause costs ($68,861 vs. $38,482) and had higher rates of inpatient admissions (1.0 vs. 0.4), emergency department visits (2.0 vs. 1.2), and outpatient visits (49.6 vs. 39.1) than the nonhyperkalemia cohort during the 1-year postdischarge study period (all P < 0.001). Hyperkalemia-related hospitalizations (n = 5377) were associated with significantly higher readmission rates (within 30 days: 0.15 vs. 0.09; 60 days: 0.25 vs. 0.16; 90 days: 0.36 vs. 0.23; all P < 0.001), longer LOS per readmission (8.1 vs. 7.1 days), and longer total inpatient days (10.5 vs. 5.8 days) compared with hospitalizations unrelated to hyperkalemia (all P < 0.001). Similar trends were observed across comorbidity subgroups.ConclusionHyperkalemia-related hospitalizations were associated with significant economic and readmission burdens during the 1-year postdischarge period.
| S-EPMC7403556 | biostudies-literature
Project description:Addressing social determinants of health may help hospitals improve population health and outcomes that are tied to financial incentives, such as readmission rates. Whether this strategy is effective remains unknown. The authors measure the association between nonprofit hospital spending on their community's social needs and Medicare readmission rates. This is a retrospective cohort study (July 2013 to June 2014) that combines Internal Revenue Service tax reports of community benefit spending by private, acute care, nonprofit hospitals with Medicare readmissions data for residents of their surrounding community, the hospital's zip code. Total community benefit spending and 2 of the largest subsets of spending-health care-related and community-directed spending-were measured and normalized as the proportion of each hospital's total expenditure. Associations between quintiles of community benefit spending and Medicare readmission rates were measured using discharge-level multivariate linear regression, adjusting for patient, hospital, community characteristics, and local public health department spending. The sample consisted of 1405 nonprofit hospitals with 341,913 discharges. Associations between readmission rates and total community benefit spending and the health care-related subset were not statistically significant. Discharges from hospitals in the upper quintiles of community-directed spending were associated with lower readmission rates by 0.82 to 1.21 absolute percentage points (P = 0.01 to <0.001) compared to the lowest quintile. The magnitude of associations between community-directed spending and readmissions was larger for preventable readmissions and smaller when including beneficiaries beyond the hospital's zip code. These associations suggest that community-directed spending may be associated with better health care outcomes.
| S-EPMC7041313 | biostudies-literature
Project description:The recent regulatory changes enacted by the Centers for Medicare and Medicaid Services (CMS) have identified hospital readmission rates as a critical healthcare quality metric. This research focuses on the utilization of pay-for-performance (P4P) mechanisms to cost effectively reduce hospital readmission rates and meet the regulatory standards set by CMS. Using the experimental economics laboratory we find that both of the P4P mechanisms researched, bonus and bundled payments, cost-effectively meet the performance criteria set forth by CMS. The bundled payment mechanism generates the largest reduction in patient length of stay (LOS) without altering the probability of readmission. Combined these results indicate that utilizing P4P mechanisms incentivizes cost effective reductions in hospital readmission rates.
| S-EPMC5319438 | biostudies-literature
Project description:ImportanceMedicare 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.ObjectiveTo assess the extent to which a comprehensive set of patient characteristics accounts for differences in hospital readmission rates.Design, setting, and participantsUsing 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.Main outcomes and measuresAll-cause readmission within 30 days of discharge.ResultsOf 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).Conclusions and relevancePatient 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