County-level variation in readmission rates: implications for the Hospital Readmission Reduction Program's potential to succeed

The Hospital Readmissions Reduction Program's Impact on Readmissions From Skilled Nursing Facilities

EXECUTIVE SUMMARY

Hospital readmissions have long served as an indicator of patient recovery and the effectiveness of care. The present study examines the Hospital Readmissions Reduction Program's (HRRP's) impact on hospital readmissions from skilled nursing facilities (SNFs) and the characteristics of SNFs that were predictive of lower readmission rates. Adjusted 30-day readmission rates among 14,666 SNFs in the United States from 2011 through 2015 were examined using linear regression with generalized estimating equations to determine the relationship of the HRRP mandate to readmission rates from SNFs. Findings indicate a significant downward trend in adjusted 30-day readmission rates over time, decreasing 1.4% from 2011 to 2015. Furthermore, lower readmission rates were associated with SNF characteristics including location in a hospital facility, rural designation, higher registered nurse-to-nurse ratios, and not-for-profit status. We found a substantial decrease in SNF-related readmissions associated with HRRP, which may limit the impact of the Protecting Access to Medicare Act. Policy-makers may consider these systemic and structural differences before drafting future legislation targeting hospital readmission from SNFs. In addition, acute care facility operators who do not have an SNF may consider adding one to their facility and/or consider partnering with SNFs to ensure that high-quality programs in these SNFs are in place to reduce 30-day readmissions to the acute care facilities.

Composite Outcomes of Mortality and Readmission in Patients with Heart Failure: Retrospective Review of Administrative Datasets

Background

Controlling the quality of care through readmissions and mortality for patients with heart failure (HF) is a national priority for healthcare regulators in developed countries. In this longitudinal cohort study, using administrative data such as hospital discharge forms (HDFs), emergency departments (EDs) accesses, and vital statistics, we test new covariates for predicting mortality and readmissions of patients hospitalized for HF and discuss the use of combined outcome as an alternative.

Methods

Logistic models, with a stepwise selection method, were estimated on 70% of the sample and validated on the remaining 30% to evaluate 30-day mortality, 30-day readmissions, and the combined outcome. We followed an extraction method for any-cause mortality and unplanned readmission within 30 days after incident HF hospitalization. Data on patient admission and previous history were extracted by HDFs and ED dataset.

Results

Our principal findings demonstrate that the model’s discriminant ability is consistent with literature both for mortality (AUC=0.738, CI (0.729–0.748)) and readmissions (AUC=0.578, CI (0.562–0.594)). Additionally, the discriminant ability of the composite outcome model is satisfactory (AUC=0.675, CI (0.666–0.684)).

Conclusion

Hospitalization characteristics and patient history introduced in the logistic models do not improve their discriminant ability. The composite outcome prediction is led more by mortality than readmission, without improvements for the comprehension of the readmission phenomenon.

Smoke-Free Policies and 30-Day Readmission Rates for Chronic Obstructive Pulmonary Disease

INTRODUCTION

Previous evidence has shown that smoke-free policies reduce hospital admissions due to respiratory causes, but the impact on 30-day readmission has not been determined. As 25 states in the U.S. have not adopted comprehensive smoke-free legislation, it is likely that patients return to an environment that increases risk of a secondary event. The aim of this study is to investigate the impact of smoke-free policies on 30-day readmission rates for adults aged ≥65 years following hospitalization for chronic obstructive pulmonary disease in the U.S.

METHODS

Data from the U.S. Tobacco Control Laws Database, Centers for Medicare and Medicaid Services Hospital Readmissions Reduction Program, American Hospital Association, Area Health Resource File, and U.S. Census Bureau Current Population Survey were merged at the county level for years 2013-2016 and analyzed in 2018. Hierarchical Poisson regression models were utilized to calculate incidence rate ratios to determine the impact of full, partial, and no smoke-free policies on 30-day readmission rates after chronic obstructive pulmonary disease hospitalization.

RESULTS

Multivariable analysis adjusting for both county and hospital characteristics revealed that the presence of full (incidence rate ratio=0.81, 95% CI=0.76, 0.88) and partial (incidence rate ratio=0.87, 95% CI=0.81, 0.92) smoke-free policies were associated with fewer 30-day readmissions for chronic obstructive pulmonary disease-related hospitalizations when compared with counties with no smoke-free policy.

CONCLUSIONS

The implementation of smoke-free policies is an effective measure for reducing 30-day readmissions following hospitalization due to chronic obstructive pulmonary disease, with stronger policies resulting in decreased risk. Efforts to reduce chronic obstructive pulmonary disease-related 30-day readmissions should include the implementation of smoke-free policies.

Impact of hospitals' Referral Region racial and ethnic diversity on 30-day readmission rates of older adults

Background: The Hospital Readmissions Reduction Program (HRRP) began decreasing Medicare payments to hospitals reporting high readmission rates for individuals over 65. Thus, financially incentivizing hospitals to improve quality performance on preventable readmissions. Well-established research indicates that minorities are more frequently readmitted to hospitals, but it is unknown if community diversity is associated with 30-day readmission rates. Objectives: To investigate the association between racial/ethnic diversity and hospitals' 30-day readmission rates. Methods: We linked the 2017 HRRP, American Hospital Association (AHA) database, Area Health Resource File, US Census Bureau Current Population Survey, and the Dartmouth Atlas HRR dataset to examine 30-day readmission rate for heart failure (HF), pneumonia (PN), acute myocardial infarction (AMI), and hip replacement (HR) surgery of 4,299 hospitals across 306 HRRs. Results: Our findings indicate a statistically significant negative relationship between diversity and 30-day readmission rates for HF, PN, AMI, and HR with a hospital referral region (HRR). Thus, hospitals located in HRRs with diverse populations are more likely to have higher 30-day readmission rates for all conditions under Medicare's HRRP Conclusion: Better discharge follow-up, interventions, and use of support staff aimed at meeting needs associated with differences in communities and cultures are likely to prove more fruitful than traditional one-size fits all approaches to care.

Hospital Readmission and Costs of Total Knee Replacement Surgery in 2009 and 2014: Potential Implications for Health Care Managers

The purpose of this article is to describe changes in hospital readmissions and costs for US hospital patients who underwent total knee replacement (TKR) in 2009 and 2014. Data came from the Healthcare Cost and Utilization Project net-Nationwide Readmissions Database. Compared with 2009, overall 30-day rates of readmissions after TKR decreased by 15% in 2014. Rates varied by demographics: readmission rates were lower for younger patients, males, Medicare recipients, and those with higher incomes. Overall, costs rose 20% across TKR groups. This report is among the first to describe changes in hospital readmissions and costs for TKR patients in a national sample of US acute care hospitals. Findings offer hospital managers a mechanism to benchmark their facilities' performances.

A robust analysis of hospital efficiency and factors affecting variability

The objectives of this paper are to use data envelopment analysis to measure hospital inefficiency in a way that accounts for patient outcomes and to study the association between organizational factors, such as hospital-physicians integration level and teaching status, and market competition with hospital inefficiency. We apply the robust data envelopment analysis approach to a sample of private (both not-for-profit and for-profit) hospitals operating in the United States. Our data envelopment analysis model includes mortality and readmission rates as bad outputs and admissions, surgeries, emergency room, and other visits as good outputs. Therefore, our measurement of hospital inefficiency accounts for quality. We then use a subsampling regression analysis to determine the predictors of hospital inefficiency. For-profit, fully integrated and teaching hospitals were more efficient than their counterparts. Also hospitals located in more competitive markets were more efficient than those located in less competitive markets. Incorporating quality in the measurement of hospital efficiency is key for producing valid efficiency scores. Hospitals in less competitive markets need to improve their efficiency levels. Moreover, high levels of hospital physician integration might be instrumental in ensuring that hospitals achieve their efficiency goals.

Pay-for-Performance: Disappointing Results or Masked Heterogeneity?

Research on the effects of pay-for-performance (P4P) in health care indicates largely disappointing results. This central finding, however, may mask important heterogeneity in the effects of P4P. We conducted a literature review to assess whether hospital and physician performance in P4P vary by patient and catchment area factors, organizational and structural capabilities, and P4P program characteristics. Several findings emerged: organizational size, practice type, teaching status, and physician age and gender modify performance in P4P. For physician practices and hospitals, a higher proportion of poor and minority patients is consistently associated with worse performance. Other theoretically influential characteristics-including information technology and staffing levels-yield mixed results. Inconsistent and contradictory effects of bonus likelihood, bonus size, and marginal costs on performance in P4P suggest organizations have not responded strategically to financial incentives. We conclude that extant heterogeneity in the effects of P4P does not fundamentally alter current assessments about its effectiveness.