Medicare readmission rates showed meaningful decline in 2012

Identifying the process and agency characteristics associated with poor utilization outcomes in home healthcare

This study identified the process and agency characteristics associated with poor utilization outcomes - higher percentages of patients (i) admitted to an acute care organization and (ii) visited an emergency room (ER) unplanned without hospitalization - for home health agencies (HHAs) in the United States. We conducted a secondary analysis of data about HHAs' various characteristics, process adherence levels, and utilization outcomes collected from disparate public repositories for 2010-2022. We developed descriptive tree-based models using HHAs' hospital admission or ER visit percentages as response variables. Across the board, hospital admission percentages have steadily improved while ER percentages deteriorated for an extended period. Recently, checking for fall risks and depression was associated with improved outcomes for urban agencies. In general, rural HHAs had worse utilization outcomes than urban HHAs. Targeted investments and improvement initiatives can help rural HHAs close the urban-rural gap in the future.

The Long-Term Effect of Financial Penalties on 30-Day Hospital Readmission Rates

BACKGROUND

Although the immediate effect of financial penalties imposed by the Hospital Readmissions Reduction Program (HRRP) was a decrease in 30-day hospital readmission rates, the long-term effects are unclear. The authors studied 30-day readmissions before and immediately after HRRP penalties and during the most recent period before the COVID-19 pandemic and examined whether readmission trends differed between penalized and non-penalized hospitals.

METHODS

Centers for Medicare & Medicaid Services hospital archive data and US Census Bureau data were used to analyze hospital characteristics, including readmission penalty status, and hospital service area (HSA) demographic information, respectively. These two datasets were matched by HSA crosswalk files, available through the Dartmouth Atlas files. Using data from 2005-2008 as baseline, the authors examined hospital readmission trends before (2008-2011) and after penalties (during three periods: 2011-2014, 2014-2017, 2017-2019). Mixed linear models were used to examine readmission trends through periods, and differences by hospital penalty status without and with adjustment for hospital characteristics and HSA demographic information.

RESULTS

For all hospitals combined, rates for 2008-2011 vs. 2011-2014 were as follows: pneumonia, 18.6% vs. 17.0%; heart failure (HF), 24.8% vs. 22.0%; acute myocardial infarction (AMI), 19.7% vs. 17.0% (p < 0.001 for all three conditions). Rates for 2014-2017 vs. 2017-2019 were as follows: pneumonia, 16.8% vs. 16.8% (p = 0.87), HF, 21.7% vs. 21.9% (p < 0.001); AMI, 16.0% vs. 15.8% (p < 0.001). Compared to penalized hospitals, using difference-in-differences, non-penalized hospitals had a significantly greater increase for two conditions between the 2014-2017 and 2017-2019 periods: pneumonia 0.34%, p < 0.001; and HF 0.24%, p = 0.002.

CONCLUSION

Long-term readmission rates are lower than pre-HRRP rates, with recent trends decreasing further for AMI, stabilizing for pneumonia, and increasing for HF.

Predicting and Validating 30-day Hospital Readmission in Adults With Diabetes Whose Index Admission Is Diabetes-related

OBJECTIVE

The primary objective is to develop a prediction model of 30-day hospital readmission among adults with diabetes mellitus (DM) whose index admission was DM-related. The secondary aims are to internally and externally validate the prediction model and compare its performance with 2 existing models.

RESEARCH DESIGN AND SETTING

Data of inpatients aged ≥ 18 years from 2008 to 2015 were extracted from the electronic medical record system of the National University Hospital, Singapore. Unplanned readmission within 30 days was calculated from the discharge date of the index hospitalization. Multivariable logistic regression and 10-fold cross-validation were performed. For external validation, simulations based on prevalence of 30-day readmission, and the regression coefficients provided by referenced papers were conducted.

RESULTS

Eleven percent of 2355 patients reported 30-day readmission. The prediction model included 4 predictors: length of stay, ischemic heart disease, peripheral vascular disease, and number of drugs. C-statistics for the prediction model and 10-fold cross-validation were 0.68 (95% CI 0.66, 0.70) and 0.67 (95% CI 0.63 to 0.70), respectively. Those for the 3 simulated external validation data sets ranged from 0.64 to 0.68.

CONCLUSION

The prediction model performs well with good internal and external validity for identifying patients with DM at risk of unplanned 30-day readmission.

Effect of health information technology (HIT)-based discharge transition interventions on patient readmissions and emergency room visits: a systematic review

OBJECTIVE

To systematically synthesize and appraise the evidence on the effectiveness of health information technology (HIT)-based discharge care transition interventions (CTIs) on readmissions and emergency room visits.

MATERIALS AND METHODS

We conducted a systematic search on multiple databases (MEDLINE, CINAHL, EMBASE, and CENTRAL) on June 29, 2020, targeting readmissions and emergency room visits. Prospective studies evaluating HIT-based CTIs published as original research articles in English language peer-reviewed journals were eligible for inclusion. Outcomes were pooled for narrative analysis.

RESULTS

Eleven studies were included for review. Most studies (n = 6) were non-RCTs. Several studies (n = 9) assessed bridging interventions comprised of at least 1 pre- and 1 post-discharge component. The narrative analysis found improvements in patient experience and perceptions of discharge care.

DISCUSSION

Given the statistical and clinical heterogeneity among studies, we could not ascertain the cumulative effect of CTIs on clinical outcomes. Nevertheless, we found gaps in current research and its implications for future work, including the need for a HIT-based care transition model for guiding theory-driven design and evaluation of HIT-based discharge CTIs.

CONCLUSIONS

We appraised and aggregated empirical evidence on the cumulative effectiveness of HIT-based interventions to support discharge transitions from hospital to home, and we highlighted the implications for evidence-based practice and informatics research.

Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina

Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010-2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985-0.999), have Medicare insurance (1.013, 1.005-1.020), and have hypertension (1.014, 1.005-1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions.

Post Discharge mHealth and Teach-Back Communication Effectiveness on Hospital Readmissions: A Systematic Review

Hospital readmissions pose a threat to the constrained health resources, especially in resource-poor low-and middle-income countries. In such scenarios, appropriate technologies to reduce avoidable readmissions in hospitals require innovative interventions. mHealth and teach-back communication are robust interventions, utilized for the reduction in preventable hospital readmissions. This review was conducted to highlight the effectiveness of mHealth and teach-back communication in hospital readmission reduction with a view to provide the best available evidence on such interventions. Two authors independently searched for appropriate MeSH terms in three databases (PubMed, Wiley, and Google Scholar). After screening the titles and abstracts, shortlisted manuscripts were subjected to quality assessment and analysis. Two authors checked the manuscripts for quality assessment and assigned scores utilizing the QualSyst tool. The average of the scores assigned by the reviewers was calculated to assign a summary quality score (SQS) to each study. Higher scores showed methodological vigor and robustness. Search strategies retrieved a total of 1932 articles after the removal of duplicates. After screening titles and abstracts, 54 articles were shortlisted. The complete reading resulted in the selection of 17 papers published between 2002 and 2019. Most of the studies were interventional and all the studies focused on hospital readmission reduction as the primary or secondary outcome. mHealth and teach-back communication were the two most common interventions that catered for the hospital readmissions. Among mHealth studies (11 out of 17), seven studies showed a significant reduction in hospital readmissions while four did not exhibit any significant reduction. Among the teach-back communication group (6 out of 17), the majority of the studies (5 out of 6) showed a significant reduction in hospital readmissions while one publication did not elicit a significant hospital readmission reduction. mHealth and teach-back communication methods showed positive effects on hospital readmission reduction. These interventions can be utilized in resource-constrained settings, especially low- and middle-income countries, to reduce preventable readmissions.

Enhancing the value to users of machine learning-based clinical decision support tools: A framework for iterative, collaborative development and implementation

BACKGROUND

Health care organizations are integrating a variety of machine learning (ML)-based clinical decision support (CDS) tools into their operations, but practitioners lack clear guidance regarding how to implement these tools so that they assist end users in their work.

PURPOSE

We designed this study to identify how health care organizations can facilitate collaborative development of ML-based CDS tools to enhance their value for health care delivery in real-world settings.

METHODOLOGY/APPROACH

We utilized qualitative methods, including 37 interviews in a large, multispecialty health system that developed and implemented two operational ML-based CDS tools in two of its hospital sites. We performed thematic analyses to inform presentation of an explanatory framework and recommendations.

RESULTS

We found that ML-based CDS tool development and implementation into clinical workflows proceeded in four phases: iterative solution coidentification, iterative coengagement, iterative coapplication, and iterative corefinement. Each phase is characterized by a collaborative back-and-forth process between the technology's developers and users, through which both users' activities and the technology itself are transformed.

CONCLUSION

Health care organizations that anticipate iterative collaboration to be an integral aspect of their ML-based CDS tools' development and implementation process may have more success in deploying ML-based CDS tools that assist end users in their work than organizations that expect a traditional technology innovation process.

PRACTICE IMPLICATIONS

Managers developing and implementing ML-based CDS tools should frame the work as a collaborative learning opportunity for both users and the technology itself and should solicit constructive feedback from users on potential changes to the technology, in addition to potential changes to user workflows, in an ongoing, iterative manner.

Association between Medicare's Hospital Readmission Reduction Program and readmission rates across hospitals by medicare bed share

Background

Medicare’s Hospital Readmissions Reduction Program (HRRP), implemented beginning in 2013, seeks to incentivize Inpatient Prospective Payment System (IPPS) hospitals to reduce 30-day readmissions for selected inpatient cohorts including acute myocardial infarction, heart failure, and pneumonia. Performance-based penalties, which take the form of a percentage reduction in Medicare reimbursement for all inpatient care services, have a risk of unintended financial burden on hospitals that care for a larger proportion of Medicare patients. To examine the role of this unintended risk on 30-day readmissions, we estimated the association between the extent of their Medicare share of total hospital bed days and changes in 30-day readmissions.

Methods

We used publicly available nationwide hospital level data for 2009–2016 from the Centers for Medicare and Medicaid Services (CMS) Hospital Compare program, CMS Final Impact Rule, and the American Hospital Association Annual Survey. Using a quasi-experimental difference-in-differences approach, we compared pre- vs. post-HRRP changes in 30-day readmission rate in hospitals with high and moderate Medicare share of total hospital bed days (“Medicare bed share”) vs. low Medicare bed share hospitals.

Results

We grouped the 1904 study hospitals into tertiles (low, moderate and high) by Medicare bed share; the average bed share in the three tertile groups was 31.2, 47.8 and 59.9%, respectively. Compared to low Medicare bed share hospitals, high bed share hospitals were more likely to be non-profit, have smaller bed size and less likely to be a teaching hospital. High bed share hospitals were more likely to be in rural and non-large-urban areas, have fewer lower income patients and have a less complex patient case-mix profile. At baseline, the average readmissions rate in the low Medicare bed share (control) hospitals was 20.0% (AMI), 24.7% (HF) and 18.4% (pneumonia). The observed pre- to post-program change in the control hospitals was − 1.35% (AMI), − 1.02% (HF) and − 0.35% (pneumonia). Difference in differences model estimates indicated no differential change in readmissions among moderate and high Medicare bed share hospitals.

Conclusions

HRRP penalties were not associated with any change in readmissions rate. The CMS should consider alternative options – including working collaboratively with hospitals – to reduce readmissions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06253-2.

Improving evidence-based grouping of transitional care strategies in hospital implementation using statistical tools and expert review

BACKGROUND

As health systems transition to value-based care, improving transitional care (TC) remains a priority. Hospitals implementing evidence-based TC models often adapt them to local contexts. However, limited research has evaluated which groups of TC strategies, or transitional care activities, commonly implemented by hospitals correspond with improved patient outcomes. In order to identify TC strategy groups for evaluation, we applied a data-driven approach informed by literature review and expert opinion.

METHODS

Based on a review of evidence-based TC models and the literature, focus groups with patients and family caregivers identifying what matters most to them during care transitions, and expert review, the Project ACHIEVE team identified 22 TC strategies to evaluate. Patient exposure to TC strategies was measured through a hospital survey (N = 42) and prospective survey of patients discharged from those hospitals (N = 8080). To define groups of TC strategies for evaluation, we performed a multistep process including: using ACHIEVE'S prior retrospective analysis; performing exploratory factor analysis, latent class analysis, and finite mixture model analysis on hospital and patient survey data; and confirming results through expert review. Machine learning (e.g., random forest) was performed using patient claims data to explore the predictive influence of individual strategies, strategy groups, and key covariates on 30-day hospital readmissions.

RESULTS

The methodological approach identified five groups of TC strategies that were commonly delivered as a bundle by hospitals: 1) Patient Communication and Care Management, 2) Hospital-Based Trust, Plain Language, and Coordination, 3) Home-Based Trust, Plain language, and Coordination, 4) Patient/Family Caregiver Assessment and Information Exchange Among Providers, and 5) Assessment and Teach Back. Each TC strategy group comprises three to six, non-mutually exclusive TC strategies (i.e., some strategies are in multiple TC strategy groups). Results from random forest analyses revealed that TC strategies patients reported receiving were more important in predicting readmissions than TC strategies that hospitals reported delivering, and that other key co-variates, such as patient comorbidities, were the most important variables.

CONCLUSION

Sophisticated statistical tools can help identify underlying patterns of hospitals' TC efforts. Using such tools, this study identified five groups of TC strategies that have potential to improve patient outcomes.

Avoided Hospitalization Criteria: Validating the Impact of a Community-Based Palliative Care Program

Objective: This report describes the experiences of a community-based palliative care (CBPC) program's efforts to understand the patterns of hospital utilization, specifically utilization reduction experienced by admitted patients. Efforts to quantify and describe an avoided hospitalization and opportunities to use these data to strengthen partnerships with local insurance payers to assure sustainability of the CBPC will be discussed. Background: Patients with serious chronic illness experience emergency room care and hospitalizations with increasing frequency as their health deteriorates. CBPC programs are well positioned to decrease hospital utilization by early involvement and improved care management. Methods: Arizona Palliative Home Care (AZPHC) program is a free standing CBPC in Maricopa County, Arizona, serving 3300 patients annually. An interdisciplinary team was formed within the CBPC to facilitate the identification of avoided hospital events and communicate these data to community partners in an effective and consistent manner. The processes developed by this team are described. Results: AZPHC has enhanced its hospitalization avoidance strategies by communicating the rate of hospitalization avoidance events in a consistent and strategic manner. Providing instances of avoided hospitalizations with accompanying patient narratives to payers has enabled AZPHC to demonstrate the impact the CBPC has on improving quality of care and reducing overall costs. Discussion: CBPC programs require payment for sustainability; therefore, partnerships with local insurance payers are essential. Presenting data that validate the impact of a program from a clinical and financial perspective will advance the growth of payer-CBPC provider relationships and secure a future for funded CBPC programs.

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

BACKGROUND

Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance.

OBJECTIVE

We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system.

MATERIALS & METHODS

We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance.

RESULTS

Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85.

DISCUSSION

We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.

Timely estimation of National Admission, readmission, and observation-stay rates in medicare patients with acute myocardial infarction, heart failure, or pneumonia using near real-time claims data

Background

To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia.

Methods

The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays.

Results

These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia.

Conclusions

This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

Hospital Readmissions Reduction Program and Post-Acute Care: Implications for Service Delivery and 30-Day Hospital Readmission

OBJECTIVES

Examine whether the introduction of the Hospital Readmissions Reduction Program (HRRP) is associated with changes in post-acute care (PAC) use and 30-day readmission.

DESIGN

A retrospective cohort study examined data prepassage, preimplementation, and postimplementation of the HRRP.

SETTING AND PARTICIPANTS

In total, 7,851,430 Medicare beneficiaries discharged from 5116 acute hospitals to PAC settings including inpatient rehabilitation, skilled nursing, home health, or a long-term care hospital during 2007‒2015. We examined HRRP-targeted conditions (acute myocardial infarction, heart failure, and pneumonia) and nontargeted conditions (ischemic stroke, total hip arthroplasty/total knee arthroplasty, and hip/femur fractures).

MEASURES

The hospital-level of quarterly PAC use and the association with 30-day risk-standardized readmission rates. Outcomes were calculated for HRRP-targeted and nontargeted conditions/diagnoses across 3 phases of HRRP implementation.

RESULTS

An increase in quarterly PAC use was significantly (P < .001) associated with a decrease in 30-day risk-standardized readmission rates for acute myocardial infarction, heart failure, and hip/femur fracture. In contrast, an increase in quarterly PAC use was significantly associated with an increase in readmission rate for total hip arthroplasty/total knee arthroplasty (P < 001). PAC quarterly use and readmission rates varied significantly during implementation periods for HRRP- targeted and nontargeted conditions.

CONCLUSIONS AND IMPLICATIONS

The impact on readmission after PAC for selected impairment groups may be mediated by the type of PAC services received and whether the diagnoses is included in the HRRP. Additional research is necessary to determine if a reduction in readmission is associated with inclusion in the HRRP or is a side effect related to diagnostic group and/or type of PAC services received.

Epidemiology and Predictors of all-cause 30-Day readmission in patients with sickle cell crisis

The 30-day readmission rate after hospitalization for a sickle cell crisis (SCC) is extremely high. Accurate information on readmission diagnoses, total readmission costs and factors associated with readmission is required to effectively plan resource allocation and to plan interventions to reduce readmission rates. The present study aimed to examine readmission diagnoses and factors associated with all-cause 30-day readmission after hospitalization for SCC. We analyzed 2016 nationwide readmission database (NRD) to identify patterns of 30-day readmission by patient demographic characteristics and time after hospitalization for SCC. We estimated the percentage and most common readmission diagnoses for 30-day and 7-day readmissions after discharge. We studied the relationship between risk factors and readmission and the impact of readmission on patient outcomes and resulting financial burden on health care in dollars. In 2016, of 67,887 discharges after index hospitalizations, 18099 (26.9%) were readmitted within 30-days. Of all readmissions, 5166 (7.6%) were readmitted within 7 days. The spectrum of readmission diagnoses was largely similar in both 30-day and 7-day readmission with more than 80% patients in both time periods readmitted with diagnoses related to SCC. The mean length of stay for readmitted patients was significantly longer than the index hospitalization (5.3 days (5.1–5.5) vs 4.9 days (CI 4.8–5.1, p < 0.01). Also, the mean cost of hospitalization in readmitted patients $8485 was significantly higher than the index hospitalization $8064 p < 0.01. In 2016, readmission among patients with SCC incurred an additional 95,445 hospitalization days resulting a total charge of $609 million and a total cost of $152 million in the US. On Multivariate analysis, age group 18–30 years, discharge against medical advice, higher Charlson comorbidity index, low socioeconomic status and admission at high volume centers were associated with a higher likelihood of 30-day readmission. Among patients hospitalized for SCC, 30-day readmissions were frequent throughout the month post hospitalization and resulted in an enormous financial burden on the United States healthcare system.

Is obesity a risk factor for readmission after acute myocardial infarction?

INTRODUCTION AND OBJECTIVES

Hospital readmissions are a major concern in terms of both cost and quality of care. The purpose of this study was to determine which patients were more likely to experience hospital readmissions after acute myocardial infarction in order to help develop more targeted programs and policies.

PATIENTS AND MATERIALS AND METHODS

The 2014 Nationwide Readmissions Database was used to calculate the national readmission rate by patient characteristics. All U.S. patients who presented to the hospital with acute myocardial infarction in 2014 and incurred a readmission were included in this analysis. The main outcome of interest was the rate of readmission by obesity. Obesity was measured using the comorbidity indicator found in the dataset. National secondary data of a sample of U.S. hospital discharges was used to measure hospital readmission rates. Bivariate analysis and logistic regression were used to determine if a significant relationship existed between readmissions and the patient characteristics. For this purpose odds ratio (OR) and 95% confidence interval has been calculated.

RESULTS

There were 11.66% hospital readmissions in the database. Non-obese adults were 21% less likely to be readmitted than obese adults. Non-obese patients were 21.2% less likely to be readmitted than obese patients (OR 0.788, CI 0.751-0.827, p-value <.0001). Obese patients with no insurance had significantly higher readmissions compared to obese Medicare patients.

CONCLUSIONS

The Hospital Readmissions Reduction Program has been effective at reducing hospital readmissions. However, greater focus needs to be placed on reducing hospital readmissions for patients with chronic conditions, especially obesity.

Impact of Hospital Readmissions Reduction Program Penalties on Hip and Knee Replacement Readmissions: Comparison of Hospitals at Risk of Varying Penalty Amounts

BACKGROUND

Little is known about the impact of the U.S. Centers for Medicare & Medicaid Services' Hospital Readmissions Reduction Program (HRRP) expansion to include readmissions following elective primary total hip and knee replacements; the expansion was finalized in 2013 and was implemented in 2014. We examined whether hospitals at risk of relatively large penalties from this expansion experienced greater declines in joint replacement readmissions compared with hospitals at risk of smaller penalties.

METHODS

We used Medicare's 2009 to 2016 Hospital Compare data sets to examine the impact of the HRRP's expansion in the July 2013 to June 2016 period (post-expansion) compared with the July 2009 to June 2012 period (pre-expansion). The primary outcome was the hospital-level, 30-day, risk-standardized readmission rate (hereafter called the readmission rate) following joint replacement surgical procedures. We used the percentage of a hospital's total inpatient revenue attributed to Medicare (categorized into quartiles) to represent the risk of penalties. We used hierarchical linear regression models to examine the adjusted impact of the HRRP's expansion.

RESULTS

Our study cohort included 2,326 acute care hospitals. In the pre-HRRP expansion phase, the mean readmission rate was 5.36% among hospitals with the highest proportion of Medicare revenues (quartile 4) and 5.46% among hospitals with the lowest proportion of Medicare revenues (quartile 1). With the HRRP expansion, the readmission rate declined by 18.92% (1.01 percentage points) among quartile-4 hospitals and by 17.97% (0.98 percentage point) among quartile-1 hospitals (p = 0.45). This nonsignificant difference in readmission rate declines between quartiles persisted in multivariable analysis (a decline of 18.41% [0.98 percentage point] in quartile 4 and a decline of 17.35% [0.94 percentage point] in quartile 1; p = 0.35).

CONCLUSIONS

The HRRP's expansion to include joint replacements did not lead to greater reductions in postoperative readmissions among hospitals at risk of larger penalties in comparison with hospitals at risk of smaller penalties. Readmission rates were declining at similar rates among all hospitals, before and after the HRRP's expansion.

CLINICAL RELEVANCE

Readmissions and complications following joint replacements are measures of the quality of surgical care. These events have important clinical and economic implications for patients and providers. This study is clinically relevant because it examines whether policy interventions, such as the HRRP, have the potential to reduce these unintended consequences of surgical care.

Variation in expenditure for common, high cost surgical procedures in a working age population: implications for reimbursement reform

BACKGROUND

In the move toward value-based care, bundled payments are believed to reduce waste and improve coordination. Some commercial insurers have addressed this through the use of bundled payment, the provision of one fee for all care associated with a given index procedure. This system was pioneered by Medicare, using a population generally over 65 years of age, and despite its adoption by mainstream insurers, little is known of bundled payments' ability to reduce variation or cost in a working-age population. This study uses a universally-insured, nationally-representative population of adults aged 18-65 to examine the effect of bundled payments for five high-cost surgical procedures which are known to vary widely in Medicare reimbursement: hip replacement, knee replacement, coronary artery bypass grafting (CABG), lumbar spinal fusion, and colectomy.

METHODS

Five procedures conducted on adults aged 18-65 were identified from the TRICARE database from 2011 to 2014. A 90-day period from index procedure was used to determine episodes of associated post-acute care. Data was sorted by Zip code into hospital referral regions (HRR). Payments were determined from TRICARE reimbursement records, they were subsequently price standardized and adjusted for patient and surgical characteristics. Variation was assessed by stratifying the HRR into quintiles by spending for each index procedure.

RESULTS

After adjusting for case mix, significant inter-quintile variation was observed for all procedures, with knee replacement showing the greatest variation in both index surgery (107%) and total cost of care (75%). Readmission was a driver of variation for colectomy and CABG, with absolute cost variation of $17,257 and $13,289 respectively. Other post-acute care spending was low overall (≤$1606, for CABG).

CONCLUSIONS

This study demonstrates significant regional variation in total spending for these procedures, but much lower spending for post-acute care than previously demonstrated by similar procedures in Medicare. Targeting post-acute care spending, a common approach taken by providers in bundled payment arrangements with Medicare, may be less fruitful in working aged populations.

Continuity of care interventions for preventing hospital readmission of older people with chronic diseases: A meta-analysis

BACKGROUND

Hospital readmission after discharge is a frequent, burdensome and costly event, particularly frequent in older people with multiple chronic conditions. Few literature reviews have analysed studies of continuity of care interventions to reduce readmissions of older inpatients discharged home over the short and long term.

OBJECTIVE

To evaluate the effectiveness of continuity of care interventions in older people with chronic diseases in reducing short and long term hospital readmission after hospital discharge.

DESIGN

Meta-analysis of randomized controlled trials.

DATA SOURCES

A comprehensive literature search on the databases PubMed, Medline, CINAHL and EMBASE was performed on 27 January 2019 with no language and time limits.

REVIEW METHODS

RCTs on continuity of care interventions on older people discharged from hospital having hospital readmission as outcome, were included. Two reviewers independently screened the studies and assessed methodological quality using the Cochrane Risk of Bias tool. Selected outcome data were combined and pooled using a Mantel-Haenszel random-effects model.

RESULTS

Thirty RCTs, representing 8920 patients were included. Results were stratified by time of readmissions. At 1 month from discharge, the continuity interventions were associated with lower readmission rates in 207/1595 patients in the experimental group (12.9%), versus 264/1645 patients in the control group (16%) (Relative Risk [RR], 0.84 [95% CI, 0.71-0.99]). From 1 to 3 months, readmission rates were lower in 325/1480 patients in the experimental group (21.9%), versus 455/1523 patients in the control group (29.8%) (RR 0.74 [95% CI, 0.65-0.84]). A subgroup analysis showed that this positive effect was stronger when the interventions addressed all of the continuity dimensions. After 3 months this impact became inconclusive with moderate/high statistical heterogeneity.

CONCLUSIONS

Continuity of care interventions prevent short term hospital readmission in older people with chronic diseases. However, there is inconclusive evidence about the effectiveness of continuity interventions aiming to reduce long term readmission, and it is suggested that stronger focus on it is needed.

Care Transitions Program for High-Risk Frail Older Adults is Most Beneficial for Patients with Cognitive Impairment

BACKGROUND

Although posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success.

OBJECTIVE

To identify components of a successful homebased CTP and patient characteristics that are most predictive of reduced 30-day readmissions.

DESIGN

Retrospective cohort.

PATIENTS

A total of 315 community-dwelling, hospitalized, older adults (≥60 years) at high risk for readmission (Elder Risk Assessment score ≥16), discharged home over the period of January 1, 2011 to June 30, 2013.

SETTING

Midwest primary care practice in an integrated health system.

INTERVENTION

Enrollment in a CTP during acute hospitalization.

MEASUREMENTS

The primary outcome was all-cause readmission within 30 days of the first CTP evaluation. Logistic regression was used to examine independent variables, including patient demographics, comorbidities, number of medications, completion, and timing of program fidelity measures, and prior utilization of healthcare.

RESULTS

The overall 30-day readmission rate was 17.1%. The intensity of follow-up varied among patients, with 17.1% and 50.8% of the patients requiring one and ≥3 home visits, respectively, within 30 days. More than half (54.6%) required visits beyond 30 days. Compared with patients who were not readmitted, readmitted patients were less likely to exhibit cognitive impairment (29.6% vs 46.0%; P = .03) and were more likely to have high medication use (59.3% vs 44.4%; P = .047), more emergency department (ED; 0.8 vs 0.4; P = .03) and primary care visits (4.0 vs 3.0; P = .018), and longer cumulative time in the hospital (4.6 vs 2.5 days; P = .03) within 180 days of the index hospitalization. Multivariable analysis indicated that only cognitive impairment and previous ED visits were important predictors of readmission.

CONCLUSIONS

No single CTP component reliably predicted reduced readmission risk. Patients with cognitive impairment and polypharmacy derived the most benefit from the program.

Readmissions following hospitalisations for cardiovascular disease: a scoping review of the Australian literature

Objective International studies suggest high rates of readmissions after cardiovascular hospitalisations, but the burden in Australia is uncertain. We summarised the characteristics, frequency, risk factors of readmissions and interventions to reduce readmissions following cardiovascular hospitalisation in Australia. Methods A scoping review of the published literature from 2000-2016 was performed using Medline, EMBASE and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases and relevant grey literature. Results We identified 35 studies (25 observational, 10 reporting outcomes of interventions). Observational studies were typically single-centre (11/25) and reported readmissions following hospitalisations for heart failure (HF; 10/25), acute coronary syndrome (7/25) and stroke (6/25), with other conditions infrequently reported. The definition of a readmission was heterogeneous and was assessed using diverse methods. Readmission rate, most commonly reported at 1 month (14/25), varied from 6.3% to 27%, with readmission rates of 10.1-27% for HF, 6.5-11% for stroke and 12.7-17% for acute myocardial infarction, with a high degree of heterogeneity among studies. Of the 10 studies of interventions to reduce readmissions, most (n=8) evaluated HF management programs and three reported a significant reduction in readmissions. We identified a lack of national studies of readmissions and those assessing the cost and resource impact of readmissions on the healthcare system as well as a paucity of successful interventions to lower readmissions. Conclusions High rates of readmissions are reported for cardiovascular conditions, although substantial methodological heterogeneity exists among studies. Nationally standardised definitions are required to accurately measure readmissions and further studies are needed to address knowledge gaps and test interventions to lower readmissions in Australia. What is known about the topic? International studies suggest readmissions are common following cardiovascular hospitalisations and are costly to the health system, yet little is known about the burden of readmission in the Australian setting or the effectiveness of intervention to reduce readmissions. What does this paper add? We found relatively high rates of readmissions following common cardiovascular conditions although studies differed in their methodology making it difficult to accurately gauge the readmission rate. We also found several knowledge gaps including lack of national studies, studies assessing the impact on the health system and few interventions proven to reduce readmissions in the Australian setting. What are the implications for practitioners? Practitioners should be cautious when interpreting studies of readmissions due the heterogeneity in definitions and methods used in Australian literature. Further studies are needed to test interventions to reduce readmissions in the Australians setting.

Safe and effective person- and family-centered care practices during transitions from hospital to home-A web-based Delphi technique

Background

Research has shown that adverse events during care transitions from hospital to home can have a significant impact on patients’ outcomes, leading to readmission, delayed healing or even death. Gaps exist in the ways of monitoring care during transition periods and there is a need to help organizations better implement and monitor safe person-and family-centered care. Value statements are a way to obtain narratives in lay terms about how well care, treatment and support is organized to meet the needs and preferences of patients/families. The purpose of this study was to identify the value statements that are perceived by decision-makers and patients/families to best signify safe person- and family-centered care during transitions from hospital to home.

Methods

Between January and September 2017, a web-based Delphi was used to survey key stakeholders in acute care and home care organizations across Canada.

Results

Decision-makers (n = 22) and patients/families (n = 24) from five provinces participated in the Delphi. Following Round 1, 45 perceived value statements were identified. In Round 2, consensus was received on 33/45 (73.3%) by decision-makers, and 30/45 (66.7%) by patients/families. In Round 3, additional value statements reached consensus in the decision-makers’ survey (3) and in the patients/families’ survey (2). A total of 30 high priority value statements achieved consensus derived from both the decision-makers’ and patients/families’ perspectives.

Conclusion

This study was an important first step in identifying key consensus-based priority value statements for monitoring care transitions from the perspective of both decision-makers and patients/families. Future research is needed to test their usability and to determine whether these value statements are actually suggestive of safe person-and family-centered care transition interventions from hospital to home.

Testosterone Replacement Therapy and Rehospitalization in Older Men With Testosterone Deficiency in a Postacute Care Setting

OBJECTIVE

The aim of the study was to examine whether receipt of testosterone replacement therapy was associated with reduced 30-day rehospitalization after postacute care among older men with testosterone deficiency.

DESIGN, PATIENTS, AND METHODS

We conducted a retrospective cohort study using a 5% national sample of Medicare beneficiaries. We identified 1290 nonsurgical inpatient postacute care discharges between January 1, 2007, and October 31, 2014, for male patients, 66 yrs or older, with a previous diagnosis of testosterone deficiency. Multivariable logistic regression was used to calculate odds ratios and 95% confidence intervals for 30-day postacute care rehospitalization related to receipt of testosterone replacement therapy.

RESULTS

In older men with testosterone deficiency, receipt of testosterone replacement therapy was not associated with rehospitalization (odds ratio = 0.87, 95% confidence interval, 0.59-1.29) in the 30 days after postacute care discharge. These findings persisted after adjustment for quintile of propensity scores (odds ratio = 0.90, 95% confidence interval = 0.62-1.30).

CONCLUSION

Testosterone replacement therapy was not associated with reduced rehospitalization after postacute care discharge in older men with testosterone deficiency. Further research in this population should examine the effects of testosterone replacement therapy on functional recovery and community independence.

Differential Impact of Hospital and Community Factors on Medicare Readmission Penalties

OBJECTIVE

To identify hospital/county characteristics and sources of regional heterogeneity associated with readmission penalties.

DATA SOURCES/STUDY SETTING

Acute care hospitals under the Hospital Readmissions Reduction Program from fiscal years 2013 to 2018 were linked to data from the Annual Hospital Association, Centers for Medicare and Medicaid Services, Medicare claims, Hospital Compare, Nursing Home Compare, Area Resource File, Health Inequity Project, and Long-term Care Focus. The final sample contained 3,156 hospitals in 1,504 counties.

DATA COLLECTION/EXTRACTION METHODS

Data sources were combined using Medicare hospital identifiers or Federal Information Processing Standard codes.

STUDY DESIGN

A two-level hierarchical model with correlated random effects, also known as the Mundlak correction, was employed with hospitals nested within counties.

PRINCIPAL FINDINGS

Over a third of the variation in readmission penalties was attributed to the county level. Patient sociodemographics and the surrounding access to and quality of care were significantly associated with penalties. Hospital measures of Medicare volume, percentage dual-eligible and Black patients, and patient experience were correlated with unobserved area-level factors that also impact penalties.

CONCLUSIONS

As the readmission risk adjustment does not include any community-level characteristics or geographic controls, the resulting endogeneity bias has the potential to disparately penalize certain hospitals.

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

BACKGROUND

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

OBJECTIVE

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

DESIGN

Retrospective study of hospitalizations between January 2009 and June 2015.

PATIENTS

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

MEASUREMENTS

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

RESULTS

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

CONCLUSIONS

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

Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients

Background

Many experts believe that hospitals with more frequent readmissions provide lower-quality care, but little is known about how the preventability of readmissions might change over the postdischarge time frame.

Objective

To determine whether readmissions within 7 days of discharge differ from those between 8 and 30 days after discharge with respect to preventability.

Design

Prospective cohort study.

Setting

10 academic medical centers in the United States.

Patients

822 adults readmitted to a general medicine service.

Measurements

For each readmission, 2 site-specific physician adjudicators used a structured survey instrument to determine whether it was preventable and measured other characteristics.

Results

Overall, 36.2% of early readmissions versus 23.0% of late readmissions were preventable (median risk difference, 13.0 percentage points [interquartile range, 5.5 to 26.4 percentage points]). Hospitals were identified as better locations for preventing early readmissions (47.2% vs. 25.5%; median risk difference, 22.8 percentage points [interquartile range, 17.9 to 31.8 percentage points]), whereas outpatient clinics (15.2% vs. 6.6%; median risk difference, 10.0 percentage points [interquartile range, 4.6 to 12.2 percentage points]) and home (19.4% vs. 14.0%; median risk difference, 5.6 percentage points [interquartile range, -6.1 to 17.1 percentage points]) were better for preventing late readmissions.

Limitation

Physician adjudicators were not blinded to readmission timing, community hospitals were not included in the study, and readmissions to nonstudy hospitals were not included in the results.

Conclusion

Early readmissions were more likely to be preventable and amenable to hospital-based interventions. Late readmissions were less likely to be preventable and were more amenable to ambulatory and home-based interventions.

Primary Funding Source

Association of American Medical Colleges.

The Impact of the Hospital Readmissions Reduction Program across Insurance Types in California

OBJECTIVE

Examine 30-day readmission rates for indicator conditions before and after adoption of the Hospital Readmissions Reduction Program (HRRP).

DATA

California hospital discharge data, 2005 to 2014.

STUDY DESIGN

Estimated difference between pre-HRRP trends and post-HRRP rates of hospital readmissions after hospitalization for indicator conditions targeted by the HRRP (heart attack, heart failure, and pneumonia) by payer among insured adults.

PRINCIPAL FINDINGS

Post-HRRP, reductions occurred for the three conditions among Fee-for-Service (FFS) Medicare. Readmissions decreased for heart attack and heart failure in Medicare Managed Care (MC). No reductions were observed in the younger commercially insured.

CONCLUSIONS

Post-HRRP, greater than expected reductions occurred in rehospitalizations for patients with Medicare FFS and Medicare MC. HRRP incentives may be influencing system-wide changes influencing care outside of traditional Medicare.

Care Transitions From Patient and Caregiver Perspectives

PURPOSE

Despite concerted actions to streamline care transitions, the journey from hospital to home remains hazardous for patients and caregivers. Remarkably little is known about the patient and caregiver experience during care transitions, the services they need, or the outcomes they value. The aims of this study were to (1) describe patient and caregiver experiences during care transitions and (2) characterize patient and caregiver desired outcomes of care transitions and the health services associated with them.

METHODS

We interviewed 138 patients and 110 family caregivers recruited from 6 health networks across the United States. We conducted 34 homogenous focus groups (103 patients, 65 caregivers) and 80 key informant interviews (35 patients, 45 caregivers). Audio recordings were transcribed and analyzed using principles of grounded theory to identify themes and the relationship between them.

RESULTS

Patients and caregivers identified 3 desired outcomes of care transition services: (1) to feel cared for and cared about by medical providers, (2) to have unambiguous accountability from the health care system, and (3) to feel prepared and capable of implementing care plans. Five care transition services or provider behaviors were linked to achieving these outcomes: (1) using empathic language and gestures, (2) anticipating the patient's needs to support self-care at home, (3) collaborative discharge planning, (4) providing actionable information, and (5) providing uninterrupted care with minimal handoffs.

CONCLUSIONS

Clear accountability, care continuity, and caring attitudes across the care continuum are important outcomes for patients and caregivers. When these outcomes are achieved, care is perceived as excellent and trustworthy. Otherwise, the care transition is experienced as transactional and unsafe, and leaves patients and caregivers feeling abandoned by the health care system.

Results of a Medicare Bundled Payments for Care Improvement Initiative for Chronic Obstructive Pulmonary Disease Readmissions

RATIONALE

Approximately 20% of Medicare beneficiaries hospitalized for acute exacerbations of chronic obstructive pulmonary disease (COPD) are readmitted within 30 days of discharge. In addition to implementing penalties for excess readmissions, the U.S. Centers for Medicare and Medicaid Services has developed Bundled Payments for Care Improvement (BPCI) initiatives to improve outcomes and control costs.

OBJECTIVES

To evaluate whether a comprehensive COPD multidisciplinary intervention focusing on inpatient, transitional, and outpatient care as part of our institution's BPCI participation would reduce 30-day all-cause readmission rates for COPD exacerbations and reduce overall costs.

METHODS

We performed a pre-postintervention study comparing all-cause readmissions and costs after index hospitalization for Medicare-only patients with acute exacerbation of COPD. The primary outcome was the difference in 30-day all-cause readmission rate compared with historical control subjects; secondary outcomes included the 90-day all-cause readmission rate and also health care costs compared with BPCI target prices.

RESULTS

Seventy-eight consecutive Medicare patients were prospectively enrolled in the BPCI intervention in 2014 and compared with 109 patients in the historical group from 2012. Patients in BPCI were more likely to receive regular follow-up phone calls, pneumococcal and influenza vaccines, home health care, durable medical equipment, and pulmonary rehabilitation, and to attend pulmonary clinic. There was no difference in all-cause readmission rates at 30 days (BPCI, 12 events [15.4%] vs. non-BPCI, 19 events [17.4%]; P = 0.711), and 90 days (21 [26.9%] vs. 37 [33.9%]; P = 0.306). Compared with BPCI target prices, we incurred 4.3% lower 90-day costs before accounting for significant investment from the health system.

CONCLUSIONS

A Medicare BPCI intervention did not reduce 30-day all-cause readmission rates or overall costs after hospitalization for acute exacerbation of COPD. Although additional studies enrolling larger numbers of patients at multiple centers may demonstrate the efficacy of our BPCI initiative for COPD readmissions, this is unlikely to be cost effective at any single center.

Reducing excess hospital readmissions: Does destination matter?

Reducing excess hospital readmissions has become a high policy priority to lower health care spending and improve quality. The Affordable Care Act (ACA) penalizes hospitals with higher-than-expected readmission rates. This study tracks patient-level admissions and readmissions to Florida hospitals from 2006 to 2014 to examine whether the ACA has reduced readmission effectively. We compare not only the change in readmissions in targeted conditions to that in non-targeted conditions, but also changes in sites of readmission over time and differences in outcomes based on destination of readmission. We find that the drop in readmissions is largely owing to the decline in readmissions to the original hospital where they received operations or treatments (i.e., the index hospital). Patients readmitted into a different hospital experienced longer hospital stays. The results suggest that the reduction in readmission is likely achieved via both quality improvement and strategic admission behavior.

Age trends in 30 day hospital readmissions: US national retrospective analysis

OBJECTIVE

To assess trends in and risk factors for readmission to hospital across the age continuum.

DESIGN

Retrospective analysis.

SETTING AND PARTICIPANTS

31 729 762 index hospital admissions for all conditions in 2013 from the US Agency for Healthcare Research and Quality Nationwide Readmissions Database.

MAIN OUTCOME MEASURE

30 day, all cause, unplanned hospital readmissions. Odds of readmission were compared by patients' age in one year epochs with logistic regression, accounting for sex, payer, length of stay, discharge disposition, number of chronic conditions, reason for and severity of admission, and data clustering by hospital. The middle (45 years) of the age range (0-90+ years) was selected as the age reference group.

RESULTS

The 30 day unplanned readmission rate following all US index admissions was 11.6% (n=3 678 018). Referenced by patients aged 45 years, the adjusted odds ratio for readmission increased between ages 16 and 20 years (from 0.70 (95% confidence interval 0.68 to 0.71) to 1.04 (1.02 to 1.06)), remained elevated between ages 21 and 44 years (range 1.02 (1.00 to 1.03) to 1.12 (1.10 to 1.14)), steadily decreased between ages 46 and 64 years (range 1.02 (1.00 to 1.04) to 0.91 (0.90 to 0.93)), and decreased abruptly at age 65 years (0.78 (0.77 to 0.79)), after which the odds remained relatively constant with advancing age. Across all ages, multiple chronic conditions were associated with the highest adjusted odds of readmission (for example, 3.67 (3.64 to 3.69) for six or more versus no chronic conditions). Among children, young adults, and middle aged adults, mental health was one of the most common reasons for index admissions that had high adjusted readmission rates (≥75th centile).

CONCLUSIONS

The likelihood of readmission was elevated for children transitioning to adulthood, children and younger adults with mental health disorders, and patients of all ages with multiple chronic conditions. Further attention to the measurement and causes of readmission and opportunities for its reduction in these groups is warranted.

Readmission rates in not-for-profit vs. proprietary hospitals before and after the hospital readmission reduction program implementation

Background

The Patient Protection and Affordable Care Act established the Hospital Readmission Reduction Program (HRRP) to penalize hospitals with excessive 30-day hospital readmissions of Medicare enrollees for specific conditions. This policy was aimed at increasing the quality of care delivered to patients and decreasing the amount of money paid for potentially preventable hospital readmissions. While it has been established that the number of 30-day hospital readmissions decreased after program implementation, it is unknown whether this effect occurred equally between not-for-profit and proprietary hospitals. The aim of this study was to determine whether or not the HRRP decreased readmission rates equally between not-for-profit and proprietary hospitals between 2010 and 2012.

Methods

Data on readmissions came from the Dartmouth Atlas and hospital ownership data came from the Centers for Medicare and Medicaid Services. Data were joined using the Medicare provider number. Using a difference-in-differences approach, bivariate and regression analyses were conducted to compare readmission rates between not-for-profit and proprietary hospitals between 2010 and 2012 and were adjusted for hospital characteristics.

Results

In 2010, prior to program implementation, unadjusted readmission rates for proprietary and not-for-profit hospitals were 16.16% and 15.78%, respectively. In 2012, following program implementation, 30-day readmission rates dropped to 15.76% and 15.29% for proprietary and not-for-profit hospitals. The data suggest that the implementation of the Hospital Readmission Reduction Program had similar effects on not-for-profit and proprietary hospitals with respect to readmission rates, even after adjusting for confounders.

Conclusions

Although not-for-profit hospitals had lower 30-day readmission rates than proprietary hospitals in both 2010 and 2012, they both decreased after the implementation of the HRRP and the decreases were not statistically significantly different. Thus, this study suggests that the Hospital Readmission Reduction Program was equally effective in reducing readmission rates, despite ownership status.

Hospital Readmissions Reduction Program: Intended and Unintended Effects

This study examines whether the Hospital Readmissions Reduction Program (HRRP), which penalizes hospitals with excess readmissions for certain conditions, has reduced hospital readmissions and led to unintended consequences. Our analyses of Florida hospital administrative data between 2008 and 2014 find that the HRRP resulted in a reduction in the likelihood of readmissions by 1% to 2% for traditional Medicare (TM) beneficiaries with heart failure, pneumonia, or chronic obstructive pulmonary disease. Readmission rates for Medicare Advantage (MA) beneficiaries and privately insured patients with heart attack and heart failure decreased even more than TM patients with the same target condition (e.g., for heart attack, the likelihood for TM beneficiaries to be remitted is 2.2% higher than MA beneficiaries and 2.3% higher than privately insured patients). We do not find any evidence of cost-shifting, delayed readmission, or selection on discharge disposition or patient income. However, the HRRP reduced the likelihood of Hispanic patients with target conditions being admitted by 2% to 4%.

Rates, Causes, and Reduction of 30-Day Readmissions of Otolaryngology-Head and Neck Surgical Cases

Objectives

The aim of this study was to determine risk factors associated with 30-day readmission for patients undergoing inpatient otolaryngologic head and neck surgery.

Study Design

Retrospective cohort study analysis.

Setting

Study at 2 tertiary hospitals.

Methods

A 10-year retrospective cohort analysis was performed for 30-day readmissions of otolaryngology surgical cases between July 1, 2006, and June 30, 2016, at Assir Central Hospital and Abha Private Hospital. Data included total number of patients, type of surgical procedure, number of and reasons for readmissions, and length of hospital stay.

Results

There were 32,662 discharges for otolaryngology operations over the 10-year period of the study, of which 364 patients were readmitted, giving a rate of 11.14 readmissions per 1000 operative procedures (95% CI, 10.1-12.3). The male:female ratio was 1.4:1. Period of postoperative stay ranged from 1 to 3 days and, after readmission, 2 to 5 days. The main reasons for readmission were bleeding in otolaryngologic cases and wound hematoma in head and neck surgical cases. Overall readmission rates dropped significantly from 12.72 per 1000 operative procedures in the first 5 years to 10.16 in the second 5 years.

Conclusions

This study helped to establish special policies and procedures to prevent readmission by utilizing best practices, including addressing quality care, using preadmission clinics, preventing surgical site infection, and improving communication with community physicians. Plans based on these results also include the development of national model for predicting readmission within 30 days of discharge.

The Association Between Unhealthy Alcohol Use and Acute Care Expenditures in the 30 Days Following Hospital Discharge Among Older Veterans Affairs Patients with a Medical Condition

Hospital readmissions and emergency department (ED) visits within 30 days of discharge are costly. Heavy alcohol use could predict increased risk for post-discharge acute care. This study assessed 30-day acute care utilization and expenditures for different categories of alcohol use. Veterans Affairs (VA) patients age ≥65 years with past-year alcohol screening, hospitalized for a medical condition, were included. VA and Medicare health care utilization data were used. Two-part models adjusted for patient demographics. Among 416,050 hospitalized patients, 25% had 30-day acute care use. Nondrinking patients (n = 267,746) had increased probability of acute care use, mean utilization days, and expenditures (difference of $345; 95% CI $268-$423), relative to low-risk drinkers (n = 105,023). High-risk drinking patients (n = 5,300) had increased probability of acute care use and mean utilization days, but not expenditures. Although these patients did not have greater acute care expenditures than low-risk drinking patients, they may nevertheless be vulnerable to poor post-discharge outcomes.

Hospital-Readmission Risk - Isolating Hospital Effects from Patient Effects

BACKGROUND

To isolate hospital effects on risk-standardized hospital-readmission rates, we examined readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one hospital within a given year.

METHODS

We divided the Centers for Medicare and Medicaid Services hospital-wide readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized readmission rate within 30 days for each hospital, and we classified hospitals into performance quartiles, with a lower readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed readmission rates among patients who had been admitted to hospitals in different performance quartiles.

RESULTS

In the performance-classification sample, the median risk-standardized readmission rate was 15.5% (interquartile range, 15.3 to 15.8). The study sample included 37,508 patients who had two admissions for similar diagnoses at a total of 4272 different hospitals. The observed readmission rate was consistently higher among patients admitted to hospitals in a worse-performing quartile than among those admitted to hospitals in a better-performing quartile, but the only significant difference was observed when the patients were admitted to hospitals in which one was in the best-performing quartile and the other was in the worst-performing quartile (absolute difference in readmission rate, 2.0 percentage points; 95% confidence interval, 0.4 to 3.5; P=0.001).

CONCLUSIONS

When the same patients were admitted with similar diagnoses to hospitals in the best-performing quartile as compared with the worst-performing quartile of hospital readmission performance, there was a significant difference in rates of readmission within 30 days. The findings suggest that hospital quality contributes in part to readmission rates independent of factors involving patients. (Funded by Yale-New Haven Hospital Center for Outcomes Research and Evaluation and others.).

Does It Pay to Penalize Hospitals for Excess Readmissions? Intended and Unintended Consequences of Medicare's Hospital Readmissions Reductions Program

To incentivize hospitals to provide better quality care at a lower cost, the Affordable Care Act of 2010 included the Hospital Readmissions Reduction Program (HRRP), which reduces payments to hospitals with excess 30-day readmissions for Medicare patients treated for certain conditions. We use triple difference estimation to identify the HRRP's effects in Virginia hospitals; this method estimates the difference in changes in readmission over time between patients targeted by the policy and a comparison group of patients and then compares those difference-in-differences estimates in patients treated at hospitals with readmission rates above the national average (i.e., those at risk for penalties) and patients treated at hospitals with readmission rates below or equal to the national average (those not at risk). We find that the HRRP significantly reduced readmission for Medicare patients treated for acute myocardial infarction (AMI). We find no evidence that hospitals delay readmissions, treat patients with greater intensity, or alter discharge status in response to the HRRP, nor do we find changes in the age, race/ethnicity, health status, and socioeconomic status of patients admitted for AMI. Future research on the specific mechanisms behind reduced AMI readmissions should focus on actions by healthcare providers once the patient has left the hospital. Copyright © 2016 John Wiley & Sons, Ltd.

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.

Trends in readmission rates for safety net hospitals and non-safety net hospitals in the era of the US Hospital Readmission Reduction Program: a retrospective time series analysis using Medicare administrative claims data from 2008 to 2015

OBJECTIVE

To compare trends in readmission rates among safety net and non-safety net hospitals under the US Hospital Readmission Reduction Program (HRRP).

DESIGN

A retrospective time series analysis using Medicare administrative claims data from January 2008 to June 2015.

SETTING

We examined 3254 US hospitals eligible for penalties under the HRRP, categorised as safety net or non-safety net hospitals based on the hospital's proportion of patients with low socioeconomic status.

PARTICIPANTS

Admissions for Medicare fee-for-service patients, age ≥65 years, discharged alive, who had a valid five-digit zip code and did not have a principal discharge diagnosis of cancer or psychiatric illness were included, for a total of 52 516 213 index admissions.

PRIMARY AND SECONDARY OUTCOME MEASURES

Mean hospital-level, all-condition, 30-day risk-adjusted standardised unplanned readmission rate, measured quarterly, along with quarterly rate of change, and an interrupted time series examining: April-June 2010, after HRRP was passed, and October-December 2012, after HRRP penalties were implemented.

RESULTS

58.0% (SD 15.3) of safety net hospitals and 17.1% (SD 10.4) of non-safety net hospitals' patients were in the lowest quartile of socioeconomic status. The mean safety net hospital standardised readmission rate declined from 17.0% (SD 3.7) to 13.6% (SD 3.6), whereas the mean non-safety net hospital declined from 15.4% (SD 3.0) to 12.7% (SD 2.5). The absolute difference in rates between safety net and non-safety net hospitals declined from 1.6% (95% CI 1.3 to 1.9) to 0.9% (0.7 to 1.2). The quarterly decline in standardised readmission rates was 0.03 percentage points (95% CI 0.03 to 0.02, p<0.001) greater among safety net hospitals over the entire study period, and no differential change among safety net and non-safety net hospitals was found after either HRRP was passed or penalties enacted.

CONCLUSIONS

Since HRRP was passed and penalties implemented, readmission rates for safety net hospitals have decreased more rapidly than those for non-safety net hospitals.

Return-on-Investment (ROI) Analyses of an Inpatient Lay Health Worker Model on 30-Day Readmission Rates in a Rural Community Hospital

PURPOSE

The purpose of the study was to assess the return-on-investment (ROI) of an inpatient lay health worker (LHW) model in a rural Appalachian community hospital impacting 30-day readmission rates.

METHODS

The Bridges to Home (BTH) study completed an evaluation in 2015 of an inpatient LHW model in a rural Kentucky hospital that demonstrated a reduction in 30-day readmission rates by 47.7% compared to a baseline period. Using the hospital's utilization and financial data, a validated ROI calculator specific to care transition programs was used to assess the ROI of the BTH model comparing 3 types of payment models including Diagnosis Related Group (DRG)-only payments, pay-for-performance (P4P) contracts, and accountable care organizations (ACOs).

FINDINGS

The BTH program had a -$0.67 ROI if the hospital had only a DRG-based payment model. If the hospital had P4P contracts with payers and 0.1% of its annual operating revenue was at risk, the ROI increased to $7.03 for every $1 spent on the BTH program. However, if the hospital was an ACO as was the case for this study's community hospital, the ROI significantly increased to $38.48 for every $1 spent on the BTH program.

CONCLUSIONS

The BTH model showed a viable ROI to be considered by community hospitals that are part of an ACO or P4P program. A LHW care transition model may be a cost-effective alternative for impacting excess 30-day readmissions and avoiding associated penalties for hospital systems with a value-based payment model.

Description and Impact Evaluation of a Statewide Collaboration to Reduce Preventable Hospital Readmissions

This article describes how a Medicare-funded Quality Improvement Organization collaborated with a hospital association and multiple cross-continuum partners on a statewide effort to reduce hospital readmissions. Interventions included statewide education on quality improvement strategies and community-specific technical assistance on collaboration approaches, data collection and analysis, and selection and implementation of interventions. Fifteen communities, comprising 16 acute care hospitals, 119 nursing homes, 70 home health agencies, and 32 other health care or social service providers, actively participated over a 4.5-year period. Challenges included problems with end-of-life discussions (80.0%), physician engagement (70.0%), staffing (70.0%), and communication between settings (60.0%). Thirty-day all-cause readmission rates in fee-for-service Medicare patients decreased in most hospital service areas across the state (22/24), and the aggregate statewide readmission rate dropped from 15.2/1000 to 12.1/1000, a relative decrease of 20.3% ( P < .001). Despite these positive findings, the specific impact of this collaboration could not be determined because of multiple confounding interventions.

Improving coronary artery bypass grafting readmission outcomes from 2000 to 2012 in the Medicare population

OBJECTIVE

The study objective was to examine trends in 30-day readmission after coronary artery bypass grafting in the Medicare population over 13 years.

METHODS

The study included isolated coronary artery bypass grafting procedures in the Medicare population from January 2000 to November 2012. Comorbidities and causes of readmission were determined using Internal Classification of Diseases, 9th Revision, Clinical Modification diagnostic codes.

RESULTS

The cohort included 1,116,991 patients. Readmission rates decreased from 19.5% in 2000 to 16.6% in 2012 (P = .0001). There was significant improvement across all categories of admission status, age, race, gender, and hospital annual coronary artery bypass grafting volume that were analyzed. Adjusted odds of readmission in 2000 compared with 2012 was 1.28 (95% confidence interval, 1.24-1.32). Median length of stay for the readmission episode was 5 days, which improved to 4 days by 2012. Hospital mortality during the readmission episode was 2.8% overall and declined to 2.4% in 2012 (P = .0001). The most common primary readmission diagnoses were heart failure (12.6%), postoperative wound infection/nonhealing wound (8.9%), arrhythmias (6.4%), and pleural effusions (3.7%). Readmission for wound infections/nonhealing wounds decreased significantly over time, from 9.8% to 6.5% (P = .0001).

CONCLUSIONS

In a large cohort of Medicare patients undergoing coronary artery bypass grafting over 13 years, there was a significant decrease in 30-day readmission rates, a reduction in readmission for wound infections, and reduced mortality during the readmission episode, despite an increase in patient comorbidities. The improvement in readmission rates was seen regardless of patient variables examined.

Association Between Hospitals' Engagement in Value-Based Reforms and Readmission Reduction in the Hospital Readmission Reduction Program

IMPORTANCE

Medicare is experimenting with numerous concurrent reforms aimed at improving quality and value for hospitals. It is unclear if these myriad reforms are mutually reinforcing or in conflict with each other.

OBJECTIVE

To evaluate whether hospital participation in voluntary value-based reforms was associated with greater improvement under Medicare's Hospital Readmission Reduction Program (HRRP).

DESIGN, SETTING, AND PARTICIPANTS

Retrospective, longitudinal study using publicly available national data from Hospital Compare on hospital readmissions for 2837 hospitals from 2008 to 2015. We assessed hospital participation in 3 voluntary value-based reforms: Meaningful Use of Electronic Health Records; the Bundled Payment for Care Initiative episode-based payment program (BPCI); and Medicare's Pioneer and Shared Savings accountable care organization (ACO) programs. We used an interrupted time series design to test whether hospitals' time-varying participation in these value-based reforms was associated with greater improvement in Medicare's HRRP.

MAIN OUTCOMES AND MEASURES

Thirty-day risk standardized readmission rates for acute myocardial infarction (AMI), heart failure, and pneumonia.

RESULTS

Among the 2837 hospitals in this study, participation in value-based reforms varied considerably over the study period. In 2010, no hospitals were participating in the meaningful use, ACO, or BPCI programs. By 2015, only 56 hospitals were not participating in at least 1 of these programs. Among hospitals that did not participate in any voluntary reforms, the association between the HRRP and 30-day readmission was -0.76 percentage points for AMI (95% CI, -0.93 to -0.60), -1.30 percentage points for heart failure (95% CI, -1.47 to -1.13), and -0.82 percentage points for pneumonia (95% CI, -0.97 to -0.67). Participation in the meaningful use program alone was associated with an additional change in 30-day readmissions of -0.78 percentage points for AMI (95% CI, -0.89 to -0.67), -0.97 percentage points for heart failure (95% CI, -1.08 to -0.86), and -0.56 percentage points for pneumonia (95% CI, -0.65 to -0.47). Participation in ACO programs alone was associated with an additional change in 30-day readmissions of -0.94 percentage points for AMI (95% CI, -1.29 to -0.59), -0.83 percentage points for heart failure (95% CI, -1.26 to -0.41), and -0.59 percentage points for pneumonia (95% CI, -1.00 to -0.18). Participation in multiple reforms led to greater improvement: participation in all 3 programs was associated with an additional change in 30-day readmissions of -1.27 percentage points for AMI (95% CI, -1.58 to -0.97), -1.64 percentage points for heart failure (95% CI, -2.02 to -1.26), and -1.05 percentage points for pneumonia (95% CI, -1.32 to -0.78).

CONCLUSIONS AND RELEVANCE

Hospital participation in voluntary value-based reforms was associated with greater reductions in readmissions. Our findings lend support for Medicare's multipronged strategy to improve hospital quality and value.

Ankle fractures in the elderly: risks and management challenges

Ankle fractures are the third most common osseous injury in the elderly, behind hip and distal radius fractures. While there is a rich history of clinical advancement in the timing, technique, perioperative management, and associated risks of hip fractures, similar evaluations are only more recently being undertaken for ankle fractures. Traditionally, elderly patients were treated more conservatively; however, nonoperative management has been found to be associated with increased mortality. As such, older and less healthy patients have become operative candidates. The benefits of geriatric/orthopedic inpatient comanagement that have been well elucidated in the hip fracture literature also seem to improve outcomes in elderly patients with ankle fractures. One of the orthopedist’s roles is to recognize the complexities of osteoporotic bone fixation and optimize wound healing potential. Though the immediate cost of this surgical approach is inevitably higher, the ultimate cost of long-term care has been found to be substantially reduced. It is important to consider the mortality and morbidity benefits and cost reductions of operative intervention and proper inpatient care of geriatric ankle fractures when they present to the emergency department or the office.

Hospital Characteristics Associated With Risk-standardized Readmission Rates

BACKGROUND

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.

OBJECTIVE

To examine the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR).

DESIGN

This is a retrospective cross-sectional multivariable analysis.

SUBJECTS

US hospitals.

MEASURES

Centers for Medicare and Medicaid Services specification of hospital-wide RSRR from July 1, 2013 through June 30, 2014 with race and Medicaid dual-eligibility added.

RESULTS

We included 6,789,839 admissions to 4474 hospitals of Medicare fee-for-service beneficiaries aged over 64 years. In multivariable analyses, there was regional variation: hospitals in the mid-Atlantic region had the highest RSRRs [0.98 percentage points higher than hospitals in the Mountain region; 95% confidence interval (CI), 0.84-1.12]. For-profit hospitals had an average RSRR 0.38 percentage points (95% CI, 0.24-0.53) higher than public hospitals. Both urban and rural hospitals had higher RSRRs than those in medium metropolitan areas. Hospitals without advanced cardiac surgery capability had an average RSRR 0.27 percentage points (95% CI, 0.18-0.36) higher than those with. The ratio of registered nurses per hospital bed was not associated with RSRR. Variability in RSRRs among hospitals of similar type was much larger than aggregate differences between types of hospitals.

CONCLUSIONS

Overall, larger, urban, academic facilities had modestly higher RSRRs than smaller, suburban, community hospitals, although there was a wide range of performance. The strong regional effect suggests that local practice patterns are an important influence. Disproportionately high readmission rates at for-profit hospitals may highlight the role of financial incentives favoring utilization.