Readmissions Reduction Program Research Articles

Systematic Review and Meta-Analysis of the Financial Impact of 30-Day Readmissions for Selected Medical Conditions: A Focus on Hospital Quality Performance.

The Patient Protection and Affordable Care Act (ACA) established the Hospital Quality Initiative in 2010 to enhance patient safety, reduce hospital readmissions, improve quality, and minimize healthcare costs. In response, this study aims to systematically review the literature and conduct a meta-analysis to estimate the average cost of procedure-specific 30-day risk-standardized unplanned readmissions for Acute Myocardial Infarction (AMI), Heart Failure (HF), Pneumonia, Coronary Artery Bypass Graft (CABG), and Total Hip Arthroplasty and/or Total Knee Arthroplasty (THA/TKA). Eligibility Criteria: This study included English language original research papers from the USA, encompassing various study designs. Exclusion criteria comprise studies lacking empirical evidence on hospital financial performance. A comprehensive search using relevant keywords was conducted across databases from January 1990 to December 2019 (updated in March 2021), covering peer-reviewed articles and gray literature. Risk of Bias: Bias in the included studies was assessed considering study design, adjustment for confounding factors, and potential effect modifiers. The review adhered to PRISMA guidelines. Employing Monte Carlo simulations, a meta-analysis was conducted with 100,000 simulated samples. Results indicated mean 30-day readmission costs: USD 16,037.08 (95% CI, USD 15,196.01-16,870.06) overall, USD 6852.97 (95% CI, USD 6684.44-7021.08) for AMI, USD 9817.42 (95% CI, USD 9575.82-10,060.43) for HF, and USD 21,346.50 (95% CI, USD 20,818.14-21,871.85) for THA/TKA. Despite the financial challenges that hospitals face due to the ACA and the Hospital Readmissions Reduction Program, this meta-analysis contributes valuable insights into the consistent cost trends associated with 30-day readmissions. This systematic review and meta-analysis provide comprehensive insights into the financial implications of 30-day readmissions for specific medical conditions, enhancing our understanding of the nexus between healthcare quality and financial performance.

Cloud-Based Machine Learning Platform to Predict Clinical Outcomes at Home for Patients With Cardiovascular Conditions Discharged From Hospital: Clinical Trial.

Hospitalizations account for almost one-third of the US $4.1 trillion health care cost in the United States. A substantial portion of these hospitalizations are attributed to readmissions, which led to the establishment of the Hospital Readmissions Reduction Program (HRRP) in 2012. The HRRP reduces payments to hospitals with excess readmissions. In 2018, >US $700 million was withheld; this is expected to exceed US $1 billion by 2022. More importantly, there is nothing more physically and emotionally taxing for readmitted patients and demoralizing for hospital physicians, nurses, and administrators. Given this high uncertainty of proper home recovery, intelligent monitoring is needed to predict the outcome of discharged patients to reduce readmissions. Physical activity (PA) is one of the major determinants for overall clinical outcomes in diabetes, hypertension, hyperlipidemia, heart failure, cancer, and mental health issues. These are the exact comorbidities that increase readmission rates, underlining the importance of PA in assessing the recovery of patients by quantitative measurement beyond the questionnaire and survey methods. This study aims to develop a remote, low-cost, and cloud-based machine learning (ML) platform to enable the precision health monitoring of PA, which may fundamentally alter the delivery of home health care. To validate this technology, we conducted a clinical trial to test the ability of our platform to predict clinical outcomes in discharged patients. Our platform consists of a wearable device, which includes an accelerometer and a Bluetooth sensor, and an iPhone connected to our cloud-based ML interface to analyze PA remotely and predict clinical outcomes. This system was deployed at a skilled nursing facility where we collected >17,000 person-day data points over 2 years, generating a solid training database. We used these data to train our extreme gradient boosting (XGBoost)-based ML environment to conduct a clinical trial, Activity Assessment of Patients Discharged from Hospital-I, to test the hypothesis that a comprehensive profile of PA would predict clinical outcome. We developed an advanced data-driven analytic platform that predicts the clinical outcome based on accurate measurements of PA. Artificial intelligence or an ML algorithm was used to analyze the data to predict short-term health outcome. We enrolled 52 patients discharged from Stanford Hospital. Our data demonstrated a robust predictive system to forecast health outcome in the enrolled patients based on their PA data. We achieved precise prediction of the patients' clinical outcomes with a sensitivity of 87%, a specificity of 79%, and an accuracy of 85%. To date, there are no reliable clinical data, using a wearable device, regarding monitoring discharged patients to predict their recovery. We conducted a clinical trial to assess outcome data rigorously to be used reliably for remote home care by patients, health care professionals, and caretakers.

Do Quality Measures or Hospital Characteristics Predict Readmission Penalties for Hip and Knee Arthroplasty?

BackgroundQuality rating systems exist to grade the value of care provided by hospitals, but the extent to which these rating systems correlate with patient outcomes is unclear. The association of quality rating systems and hospital characteristics with excess readmission penalties for total hip arthroplasty (THA) and total knee arthroplasty (TKA) was studied. MethodsThe fiscal year 2022 Inpatient Prospective Payment System final rule was used to identify 2,286 hospitals subject to the Hospital Readmissions Reduction Program. Overall, 6 hospital quality rating systems and 5 hospital characteristics were obtained. These factors were analyzed to determine the effect on hospital penalties for THA and TKA excess readmissions. ResultsHospitals that achieved a higher Medicare Overall Hospital Quality Star Rating demonstrated a significantly lower likelihood of receiving THA and TKA readmission penalties (Cramer’s V = 0.236 and Rp = −0.233; P < .001 for both). Hospitals ranked among the US News & World Report’s top 50 best hospitals for orthopaedics were significantly less likely to be penalized (V = 0.042; P = .043). The remaining 4 quality rating systems were not associated with readmission penalties. Penalization was more likely for hospitals with fewer THA and TKA discharges (Rp = −0.142; P < .001), medium-sized institutions (100 to 499 beds; V = 0.075; P = .002), teaching hospitals (V = 0.049; P = .019), and safety net hospitals (V = 0.043; P = .039). Penalization was less likely for West and Midwest hospitals (V = 0.112; P < .001). ConclusionsA higher Overall Hospital Quality Star Rating and recognition among the US News & World Report’s top 50 orthopaedic hospitals were associated with a reduced likelihood of THA and TKA readmission penalties. The other 4 widely accepted quality rating systems did not correlate with readmission penalties. Teaching and safety net hospitals may be biased toward higher readmission rates.

Effect of Discharge Readiness on 30-Day Readmissions Among Older Adults Living With Multiple Chronic Conditions.

Despite decreases in readmissions among Medicare beneficiaries after the implementation of the Hospital Readmissions Reduction Program, older adults living with multiple chronic conditions (MCCs) continue to experience higher readmission rates. Few strategies leverage nursing to identify patients at risk for readmission. Examine the effect of nurse assessments of discharge readiness on 30-day readmissions. Cross-sectional study linking 3 secondary data sources (ie, nurse survey, hospital survey, and Medicare claims data) representing 424 hospitals. A total of 188,806 Medicare surgical patients with MCCs. Discharge readiness was derived from the 2016 RN4CAST-US survey. Medicare claims data was used to determine the MCC count. The outcome was 30-day readmissions across the MCC count. The average discharge readiness score was 0.45 (range=0-0.86) indicating that, in the average hospital, <50% of nurses were confident their patient or caregiver could manage their care after discharge. Nearly 8% of patients were readmitted within 30 days of discharge; the highest rates of readmissions were among individuals with ≥5 MCCs (4293, 13.50%). For each 10% increase in the proportion of nurses in a hospital who were confident in their patients' discharge readiness, the odds of 30-day readmission decreased by 2% (95% CI: 0.96-1.00; P =0.028) for patients with 2-4 MCCs and 3% (95% CI: 0.94-0.99; P =0.015) for patients with ≥5 MCCs, relative to patients with 0-1 MCCs. Nurse assessments of discharge readiness may be a useful signal for hospitals to reduce readmissions and examine factors interfering with discharge processes.

Social Determinants of Health Associated with Inpatient Admissions for Congestive Heart Failure, Diabetes, Chronic Obstructive Pulmonary Disease, and Asthma

Introduction: The CDC and American Lung Association estimate that congestive heart failure (CHF), diabetes, chronic obstructive pulmonary disorder (COPD), and asthma (COPD/asthma) cost Americans $30.7 billion, $327 billion, and $50 billion respectively each year. They account for most inpatient readmissions at St. Mary Medical Center (SMMC), an urban hospital in Northwest Indiana. There is need for further research on the social, behavioral, and demographic determinants associated with these conditions. This study examined the social, behavioral, and demographic determinants associated with inpatient admission for CHF, diabetes, COPD/asthma in SMMC’s service area.&#x0D; Methods: This retrospective study was part of a multi-phased Community-Based Participatory Research partnership between SMMC and Indiana University School of Medicine Northwest. SMMC implemented a pilot screening and referral program to assess social determinants of health in their service area as part of their Hospital Readmission Reduction Program. This study included data from 10,953 inpatient admissions between January 2021 to March 2023, majority of whom were transferred from the emergency department. Data analysis consisted ofunivariate, bivariate (Chi-square), and multivariate (binary logistic regression) analysis in SPSS 29.0.&#x0D; Results: Bivariate analysis revealed a statistically significant association between CHF and smoking, age, insurance type, and income. Diabetes was significantly associated with smoking, smokeless tobacco use, age group, race, income, and sex. COPD/asthma was significantly associated with smoking, age group, transportation needs, stress, insurance, ethnicity, and sex. Multivariate analysis found the following significant associations: age group with both CHF (p&lt;0.001) and diabetes (p&lt;0.001), former smoking with both CHF (p = 0.007) and COPD/asthma (p = 0.049), current smoking with COPD/asthma (p = 0.016), and sex with diabetes (p &lt;0.001).&#x0D; Conclusions: These findings indicate significant associations between multiple sociobehavioral factors and admission for CHF, diabetes, COPD/asthma. Multi-risk-factor interventions may address these interactions and contribute to reducing readmission.

Social Determinants of Health and 30-Day Readmissions in an Urban Community Hospital in Northwest Indiana

Background: Hospital readmission within 30 days of discharge is a quality of care indicator with implications for healthcare systems, payers, and patients. The Hospital Readmission Reduction Program enacted in 2012 aimed to reduce preventable readmissions. Yet in 2018, there were 3.8 million adult 30-day readmissions with an average rate of 14% and estimated cost of $15,200 per readmission. This study examined the influence of social determinants of health (SDOH), demographics, and health behaviors on 30-day readmissions at an urban hospital in Northwest Indiana. This is part of a Community-Based Participatory Research (CBPR) partnership between Indiana University School of Medicine-Northwest and St. Mary Medical Center (SMMC) to address SDOH.&#x0D; Methods: This retrospective study analyzed a limited dataset generated by SMMC in EPIC™ with SDOH, demographics, health behaviors, and health outcomes measures from inpatient admissions between January 2021 to March 2023. Data analysis consisted of descriptive, bivariate (Chi-Square; p&lt;0.05), and multivariate (binary logistic regression; p&lt;0.05) analyses in SPSS 29.0. This study was exempted by the Indiana University Human Research Protection Program (IRB #14040).&#x0D; Results: The sample consisted of 7445 patients, majority 65 years and above (56.5%), white (77.47%), and publicly insured (76.83%). 30-day readmissions represented 10.5% of admissions. The bivariate analysis revealed statistically significant associations between 30-dayreadmissions and age (p&lt;0.001), language (p=0.008), insurance type (p&lt;0.001), veteran status (p=0.017), and smoking (p&lt;0.001). The multivariate analysis found that age (OR=1.008; p=0.004), being a non-English speaker (OR=1.866; p=0.009), public insurance (OR=2.096; p&lt;0.001), and former smoking (OR=1.243; p=0.011) remained significantly associated with 30-day readmission.&#x0D; Conclusions: Social and behavioral factors were associated with 30-day readmissions in an urban community hospital. Incorporating SDOH and behavioral interventions into hospital readmission reduction programs may reinforce these programs. The upcoming CBPR phase will conduct advanced analysis on these findings to uncover new relationships relevant to SMMC’s objectives.

Utilization of a multidisciplinary hospital-based approach to reduce readmission rates.

Hospital readmissions remain a significant and pressing issue in our healthcare system. In 2010, the Affordable Care Act helped establish the Hospital Readmissions Reduction Program, which incentivized reducing readmission rates by instituting penalties. Hospital readmission, specifically unplanned, refers to a patient returning to the hospital shortly after discharge due to the same or a related medical condition, signaling potential issues in initial care, discharge processes, or post-hospitalization management. For this study, we defined readmission as a return to the hospital within 30 days. In 2018, Staten Island University Hospital started a multidisciplinary and coordinated initiative to reduce patient readmissions. The approach involved the departments of emergency medicine, medicine, cardiology, case management, nursing, pharmacy, and transitional care management. This study aimed to determine if this approach reduced 30-day readmissions. This case-control retrospective study reviewed electronic health records between January 2018 and November 2019. Readmission rates within 30 days of index discharge were compared between patients who received transitional care management before and after establishing a multidisciplinary communication of transitional care. Readmission rates were unadjusted and adjusted for patient demographics and predisposed risk for readmission and compared across demographics and select clinical characteristics. A total of 772 patients were included in the analyses; 323 were in the control group (41.8%), and 449 were in the intervention group (58.2%). After the hospital adopted the workflow for multidisciplinary communication of transitional care, there was 45.2% less adjusted incidence of readmission, or approximately seven fewer overall readmissions per 100 patients (16.4% readmission vs 9.0% readmission; incident rate ratio, 0.55; 95% CI: 0.34-0.88). Multidisciplinary communication approaches led by emergency medicine can help reduce readmissions significantly. Adopting a structured communication workflow can enhance co-managing patients with a high risk of readmission between the emergency department and hospital medicine teams.

Association of hospital and market characteristics with 30-day readmission rates from 2009 to 2015.

The US government implemented the Hospital Readmission Reduction Program on 1 October 2012 to reduce readmission rates through financial penalties to hospitals with excessive readmissions. We conducted a pooled cross-sectional analysis of US hospitals from 2009 to 2015 to determine the association of the Hospital Readmission Reduction Program with 30-day readmissions. We utilized multivariable linear regression with year and state fixed effects. The model was adjusted for hospital and market characteristics lagged by 1 year. Interaction effects of hospital and market characteristics with the Hospital Readmission Reduction Program indicator variable was also included to assess whether associations of Hospital Readmission Reduction Program with 30-day readmissions differed by these characteristics. In multivariable adjusted analysis, the main effect of the Hospital Readmission Reduction Program was a 3.80 percentage point (p < 0.001) decrease in readmission rates in 2013-2015 relative to 2009-2012. Hospitals with lower readmission rates overall included not-for-profit and government hospitals, medium and large hospitals, those in markets with a larger percentage of Hispanic residents, and population 65 years and older. Higher hospital readmission rates were observed among those with higher licensed practical nurse staffing ratio, larger Medicare and Medicaid share, and less competition. Statistically significant interaction effects between hospital/market characteristics and the Hospital Readmission Reduction Program on the outcome of 30-day readmission rates were present. Teaching hospitals, rural hospitals, and hospitals in markets with a higher percentage of residents who were Black experienced larger decreases in readmission rates. Hospitals with larger registered nurse staffing ratios and in markets with higher uninsured rate and percentage of residents with a high school education or greater experienced smaller decreases in readmission rates. Findings of the current study support the effectiveness of the Hospital Readmission Reduction Program but also point to the need to consider the ability of hospitals to respond to penalties and incentives based on their characteristics during policy development.

Including socioeconomic status reduces readmission penalties to safety-net hospitals

ObjectiveMedicare’s Hospital Readmissions Reduction Program (HRRP) financially penalizes “excessive” postoperative readmissions. Concerned with creating a double standard for institutions treating a high percentage of economically vulnerable patients, Medicare elected to exclude socioeconomic status (SES) from its risk-adjustment model. However, recent evidence suggests that safety-net hospitals (SNHs) caring for many low-SES patients are disproportionately penalized under the HRRP. We sought to simulate the impact of including SES-sensitive models on HRRP penalties for hospitals performing lower extremity revascularization (LER). MethodsThis is a retrospective, cross-sectional analysis of national data on Medicare patients undergoing open or endovascular LER procedures between 2007 and 2009. We used hierarchical logistic regression to generate hospital risk-standardized 30-day readmission rates under Medicare’s current model (adjusting for age, sex, comorbidities, and procedure type) compared with models that also adjust for SES. We estimated the likelihood of a penalty and penalty size for SNHs compared with non-SNHs under the current Medicare model and these SES-sensitive models. ResultsOur study population comprised 1708 hospitals performing 284,724 LER operations with an overall unadjusted readmission rate of 14.4% (standard deviation: 5.3%). Compared with the Centers for Medicare and Medicaid Services model, adjusting for SES would not change the proportion of SNHs penalized for excess readmissions (55.1% vs 53.4%, P = .101) but would reduce penalty amounts for 38% of SNHs compared with only 17% of non-SNHs, P < .001. ConclusionsFor LER, changing national Medicare policy to including SES in readmission risk-adjustment models would reduce penalty amounts to SNHs, especially for those that are also teaching institutions. Making further strides toward reducing the national disparity between SNHs and non-SHNs on readmissions, performance measures require strategies beyond simply altering the risk-adjustment model to include SES.

Abstract 16083: How Will the Use of New Heart Failure Treatments Impact US Health System Reimbursement Under Alternative Payment Models: Measuring the Impact of Sotagliflozin Use Among Patients Hospitalized for Heart Failure

Background: In the SOLOIST-WHF clinical trial, sotagliflozin lowered rates of readmission and emergency department (ED) visits among patients hospitalized for worsening heart failure (HF), but its impact on health systems’ finances is unclear. Research Question: What is the economic impact of the adoption of sotagliflozin on health systems after incorporating reimbursement bonuses within alternative payment models (APM)? Goal: To quantify the financial impact on health systems of adopting sotagliflozin relative to standard of care (SoC) for treating patients with HF as in SOLOIST-WHF. Methods: This study created a decision tree model and estimated the clinical impact of sotagliflozin use relative to SoC from the SOLOIST-WHF trial. The model first assessed the impact of sotagliflozin on re-hospitalization, ED visit rates and mortality. Next, these clinical outcomes were translated into impacts on a health system’s finances relative to SoC within a one-year time frame under traditional fee-for-services (FFS) and three APMs: hospital readmissions reduction program (HRRP), Bundled Payments for Care Improvement (BPCI), and Accountable Care Organization (ACO). Results: An average community hospital (6,109 admissions annually) would have 83 patients per year with an index HF hospitalization. Sotagliflozin reduced the probability of re-hospitalization, ED visits, and deaths by 29.3%, 38.5%, and 17.8%, respectively vs. SoC. For hospitals not experiencing capacity constraint, sotagliflozin adoption increased financial returns by $9,365 per person (pp) ($781,340 per health system) under HRRP, $1,200 pp. ($100,106 per health system) for BPCI, and $1,078 pp. ($89,910 per health system) for ACO. (Figure 1) Conclusion: Sotagliflozin adoption decreased hospitalizations, ED visits, and deaths, which—under various APMs—led to a net positive financial impact on health systems after accounting for APM bonus payments. Sponsorship: Lexicon Pharmaceuticals, Inc.

Quality Assurance and Safety Culture in a Healthcare Organization

Patient care and quality management have as one of their primary focuses ensuring the safety of their patients. The four principles of biomedical ethics presented by Beauchamp and Childress are one quality management paradigm that emphasizes the importance of the patient. The Institute of Medicine's six improvement goals capture the essence of excellent medical treatment. Care, money, and health are the three pillars upon which the Institute for Healthcare Improvement's Triple Aim rests. The present review was written with the aforementioned contexts in mind, with the intention of highlighting the system's initiatives to address various efforts of enhancing quality and patient safety. We provide a reflective overview of healthcare legislation, policy, and regulation with special attention paid to the ideas of informed consent and informed refusal. This analysis also details the steps taken and regulations enforced by the administration and management to provide care that is focused on the individual patient. Finally, we discuss model policies like the Delivery System Reform Incentive Payment Program, which incorporates quality management frameworks, the Hospital-Acquired Conditions Reduction Program, which promotes patient safety, and the Hospital Readmissions Reduction Program, which aims to reduce hospital readmissions.

Medicare's Hospital Readmission Reduction Program reduced fall-related health care use: An unexpected benefit?

To assess whether Medicare's Hospital Readmissions Reduction Program (HRRP) was associated with a reduction in severe fall-related injuries (FRIs). Secondary data from Medicare were used. Using an event study design, among older (≥65) Medicare fee-for-service beneficiaries, we assessed changes in 30- and 90-day FRI readmissions before and after HRRP's announcement (April 2010) and implementation (October 2012) for conditions targeted by the HRRP (acute myocardial infarction [AMI], congestive heart failure [CHF], and pneumonia) versus "non-targeted" (gastrointestinal) conditions. We tested for modification by hospitals with "high-risk" before HRRP and accounted for potential upcoding. We also explored changes in 30-day FRI readmissions involving emergency department (ED) or outpatient care, care processes (length of stay, discharge destination, and primary care visit), and patient selection (age and comorbidities). Not applicable. We identified 1.5 million (522,596 pre-HRRP, 514,844 announcement, and 474,029 implementation period) index discharges. After its announcement, HRRP was associated with 12%-20% reductions in 30- and 90-day FRI readmissions for patients with CHF (-0.42 percentage points [ppt], p = 0.02; -1.53 ppt, p < 0.001) and AMI (-0.35, p = 0.047; -0.97, p = 0.001). Two years after implementation, HRRP was associated with reductions in 90-day FRI readmission for AMI (-1.27 ppt, p = 0.01) and CHF (-0.98 ppt, p = 0.02) patients. Results were similar for hospitals at higher versus lower baseline risk of FRI readmission. After HRRP's announcement, decreases were observed in home health (AMI: -2.43 ppt, p < 0.001; CHF: -8.83 ppt, p < 0.001; pneumonia: -1.97 ppt, p < 0.001) and skilled nursing facility referrals (AMI: -5.95 ppt, p < 0.001; CHF: -3.19 ppt, p < 0.001; pneumonia: -10.27 ppt, p < 0.001). HRRP was associated with reductions in FRIs, primarily for HF and pneumonia patients. These decreases may reflect improvements in transitional care including changes in post-acute referral patterns that benefit patients at risk for falls.

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

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

Preventing Heart Failure Readmission in Patients with Low Socioeconomic Position.

This review aims to summarize the current burden of heart failure (HF) in the United States, specifically in patients with low socioeconomic position (SEP), and synthesize recommendations to prevent HF-related hospital readmissions in this vulnerable population. As treatments have improved, HF-related mortality has declined over time, resulting in more patients living with HF. This has led to an increase in hospitalizations, however, putting excess strain on our healthcare system. HF patients with low SEP are a particularly vulnerable group, as they experience higher rates of hospitalization and readmission compared to their high SEP counterparts. The Hospital Readmission Reduction Program (HRRP) was created to motivate interventions that reduce hospital readmissions across diseases, with HF being a primary target. Numerous readmission prevention efforts have been suggested to target the pre-hospitalization, hospitalization, and post-hospitalization phases, including addressing social determinants of health (SDoH), improving coordination of care, optimizing discharge plans, and improving adherence to follow-up care and medication regimens. Many of these proposed interventions show promise in reducing HF-related readmissions and issues surrounding adequate caregiver support may be particularly important to reduce readmissions among persons in low SEP. Reducing HF-related hospital readmissions is possible, even in vulnerable populations like those with low SEP, but this will require coordinated efforts across the healthcare system and throughout the life course of these patients. Caregiver support is a necessary part of optimized care for low SEP HF patients and future efforts should consider interventions that support these caregivers.

Current approaches to preventing heart failure readmissions and decompensated disease.

Heart failure is a resource-intensive condition to manage and typically involves a multi-disciplinary and multi-modality approach leading to an expensive treatment paradigm. It is worth noting that hospital admissions constitute over 80% of heart failure management costs. In the past two decades, healthcare systems have developed new ways of following patients remotely to prevent them from being readmitted to the hospital. However, despite these efforts, hospital admissions have still increased. Many successful readmission reduction programs prioritize education and self-care to increase patients' awareness of their disease and promote lasting lifestyle changes. While socioeconomic factors impact success, interventions tend to be effective when medication adherence and guideline-directed medical therapy are emphasized. Monitoring intracardiac pressure can improve resource allocation efficiency and has demonstrated significant reductions in readmissions with improved quality of life in outpatient and remote settings. Data from several studies focused on remote monitoring devices strongly suggest that understanding congestion using physiological biomarkers is an effective management strategy. Since most cases of heart failure are first presented in acute hospitalization settings, immediate access to intracardiac pressure for treatment and decision-making purposes could result in substantial management improvements. However, a notable technology gap needs to be addressed to enable this at a low cost with less reliability on scarce specialist care resources. Contemporary evidence is conclusive that direct hemodynamic are the vital signs in heart failure with the highest clinical utility. Therefore, future ability to obtain these insights reliably using non-invasive methods will be a paradigm-changing technology.

RWD148 A Comparative Evaluation of Medicare's Hospital Readmissions Reduction Program (HRRP) Effectiveness between New York State and the Nation

To compare and analyze the available HRRP 30-day inpatient readmissions data (2014-2019) longitudinally between NYS hospitals and the rest of the country and evaluate NYS hospital performance relative to six HRRP targeted health measures.

RWD114 Is Medicare's Hospital Readmissions Reduction Program (HRRP) Delivering on the Decade's Promise?: A Trend Analysis

To analyze nationwide trends in hospitalization for six targeted CMS health measures under HRRP and evaluate the program’s success with respect to reduction of preventable readmissions.

Health care policy that relies on poor measurement is ineffective: Lessons from the hospital readmissions reduction program.

Health care policy that relies on poor measurement is ineffective: Lessons from the hospital readmissions reduction program.

Effect of Medicare Advantage on Hospital Readmission and Mortality Rankings.

Medicare links hospital performance on readmissions and mortality to payment solely on the basis of outcomes among fee-for-service (FFS) beneficiaries. Whether including Medicare Advantage (MA) beneficiaries, who account for nearly half of all Medicare beneficiaries, in the evaluation of hospital performance affects rankings is unknown. To determine if the inclusion of MA beneficiaries in readmission and mortality measures reclassifies hospital performance rankings compared with current measures. Cross-sectional. Population-based. Hospitals participating in the Hospital Readmissions Reduction Program or Hospital Value-Based Purchasing Program. Using the 100% Medicare files for FFS and MA claims, the authors calculated 30-day risk-adjusted readmissions and mortality for acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, and pneumonia on the basis of only FFS beneficiaries and then both FFS and MA beneficiaries. Hospitals were divided into quintiles of performance based on FFS beneficiaries only, and the proportion of hospitals that were reclassified to a different performance group with the inclusion of MA beneficiaries was calculated. Of the hospitals in the top-performing quintile for readmissions and mortality based on FFS beneficiaries, between 21.6% and 30.2% were reclassified to a lower-performing quintile with the inclusion of MA beneficiaries. Similar proportions of hospitals were reclassified from the bottom performance quintile to a higher one across all measures and conditions. Hospitals with a higher proportion of MA beneficiaries were more likely to improve in performance rankings. Hospital performance measurement and risk adjustment differed slightly from those used by Medicare. Approximately 1 in 4 top-performing hospitals is reclassified to a lower performance group when MA beneficiaries are included in the evaluation of hospital readmissions and mortality. These findings suggest that Medicare's current value-based programs provide an incomplete picture of hospital performance. Laura and John Arnold Foundation.

Hospital and surgeon experience and patient health outcomes after coronary artery bypass graft surgery

AbstractThe Hospital Readmissions Reduction Program imposes financial penalties on hospitals with excess readmission rates for various conditions, including coronary artery bypass graft (CABG) surgery. While prior research has focused mainly on hospital‐specific factors and their impact on patient mortality, surgeon risk factors and patient outcomes, such as readmission risk, have received less attention. We study three drivers of CABG readmission risk—hospital and surgeon case volume, variation in surgeon volume, and surgeon familiarity with hospitals. Drawing on unique patient data sets of CABG surgeries spanning 3 years, we study the relationships between hospital and surgeon case volume, readmission risk, and postoperative length of stay (PLOS). We find evidence of an inverted U‐shaped relationship between case volume and readmission risk and a U‐shaped relationship with PLOS. Our results also indicate that surgeon volume variation moderates the relationship between surgeon volume and readmission risk, where readmission risk increases with greater variation in surgeon volume. We observe that the impact of surgeon experience on readmission risk at the focal hospital outweighs their experience at other hospitals. Our study has significant implications for hospital resource management as it highlights the role of surgeon and hospital experience in improving patient outcomes after CABG surgery.