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

Higher Readmissions at Safety-Net Hospitals and Potential Policy Solutions

The Hospital Readmissions Reduction Program (HRRP), established by the Affordable Care Act, ties a hospital's payments to its readmission rates -- with penalties for hospitals that exceed a national benchmark -- to encourage hospitals to reduce avoidable readmissions. This new Commonwealth Fund analysis uses publicly reported 30-day hospital readmission rate data to examine whether safety-net hospitals are more likely to have higher readmission rates, compared with other hospitals. Results of this analysis find that safety-net hospitals are 30 percent more likely to have 30-day hospital readmission rates above the national average, compared with non-safety-net hospitals, and will therefore be disproportionately impacted by the HRRP. Policy solutions to help safety-net hospitals reduce their readmission rates include targeting quality improvement initiatives for safety-net hospitals; ensuring that broader delivery system improvements include safety-net hospitals and care delivery systems; and enhancing bundled payment rates to account for socioeconomic risk factors

Readmission Rates and Their Impact on Hospital Financial Performance: A Study of Washington Hospitals

This longitudinal study examines whether readmission rates, made transparent through Hospital Compare, affect hospital financial performance by examining 98 hospitals in the State of Washington from 2012 to 2014. Readmission rates for acute myocardial infarction (AMI), pneumonia (PN), and heart failure (HF) were examined against operating revenues per patient, operating expenses per patient, and operating margin. Using hospital-level fixed effects regression on 276 hospital year observations, the analysis indicated that a reduction in AMI readmission rates is related with increased operating revenues as expenses associated with costly treatments related with unnecessary readmissions are avoided. Additionally, reducing readmission rates is related with an increase in operating expenses. As a net effect, increased PN readmission rates may show marginal increase in operating margin because of the higher operating revenues due to readmissions. However, as readmissions continue to happen, a gradual increase in expenses due to greater use of resources may lead to decreased profitability

Medicare Hospital Readmissions Reduction Program

Outlines national health reform provisions to reduce readmissions by publishing readmission data, lowering Medicare payments to hospitals with high readmission rates, and pairing such hospitals with patient safety organizations. Considers implications

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1)explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2)to examine the additive value of patients' hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95%CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets

Risk Factor Analysis for 30-Day Readmission Rates of Newly Tracheostomized Children

Objectives: Pediatric patients undergo tracheostomy for a variety of reasons; however, medical complexity is common among these patients. Although tracheostomy may help to facilitate discharge, these patients may be at increased risk for hospital readmission. The purpose of this study was to evaluate our institutional rate of 30-day readmission for patients discharged with new tracheostomies and to identify risk factors associated with readmission. Study Design: A retrospective cohort study was conducted for all pediatric patients ages 0-18 years with new tracheostomies at our institution over a 36-month period. Methods: A chart review was performed for all newly tracheostomizedchildren from 2013 to 2016. We investigated documented readmissions within 30 days of discharge, reasons for readmission, demographic variables including age and ethnicity, initial discharge disposition, co-morbidities, and socioeconomic status estimated by mean household income by parental zip code. Results: 45 patients were discharged during the study time period. A total of 13 (28.9%) required readmission within 30 days of discharge. Among these 13 patients, the majority (61.5%) were readmitted for lower airway concerns, many (30.8%) were admitted for reasons unrelated to tracheostomy or respiratory concerns, and only one patient (7.7%) was readmitted for a reason related to tracheostomy itself (tracheostomalbreakdown). Age, ethnicity, discharge disposition, co-morbidities, and socioeconomic status were not associated with differences in readmission rates. Patients readmitted within 30 days had a higher number of admissions within the first year. Conclusion: Pediatric patients with new tracheostomies are at high risk for readmission after discharge from initial hospitalization. The readmissions are most likely secondary to underlying medical complexity rather than issues related specifically to the tracheostomy procedure.https://jdc.jefferson.edu/patientsafetyposters/1046/thumbnail.jp

Influence of Sacubitril/Valsartan (LCZ696) on 30-day readmission after heart failure hospitalization

Background: Patients with heart failure (HF) are at high risk for hospital readmission in the first 30 days following HF hospitalization. Objectives: This study sought to determine if treatment with sacubitril/valsartan (LCZ696) reduces rates of hospital readmission at 30-days following HF hospitalization compared with enalapril. Methods: We assessed the risk of 30-day readmission for any cause following investigator-reported hospitalizations for HF in the PARADIGM-HF trial, which randomized 8,399 participants with HF and reduced ejection fraction to treatment with LCZ696 or enalapril. Results: Accounting for multiple hospitalizations per patient, there were 2,383 investigator-reported HF hospitalizations, of which 1,076 (45.2%) occurred in subjects assigned to LCZ696 and 1,307 (54.8%) occurred in subjects assigned to enalapril. Rates of readmission for any cause at 30 days were 17.8% in LCZ696-assigned subjects and 21.0% in enalapril-assigned subjects (odds ratio: 0.74; 95% confidence interval: 0.56 to 0.97; p = 0.031). Rates of readmission for HF at 30-days were also lower in subjects assigned to LCZ696 (9.7% vs. 13.4%; odds ratio: 0.62; 95% confidence interval: 0.45 to 0.87; p = 0.006). The reduction in both all-cause and HF readmissions with LCZ696 was maintained when the time window from discharge was extended to 60 days and in sensitivity analyses restricted to adjudicated HF hospitalizations. Conclusions: Compared with enalapril, treatment with LCZ696 reduces 30-day readmissions for any cause following discharge from HF hospitalization

TLR3 Deficiency Leads to a Dysregulation in the Global Gene-Expression Profile in Murine Oviduct Epithelial Cells Infected with Chlamydia muridarum

OBJECTIVE Describe the implementation and effects of Mobile Acute Care for Elders (MACE) consultation at a Veterans Affairs Medical Center (VAMC). DESIGN Retrospective cohort analysis. INTERVENTION Veterans aged 65 or older who were admitted to the medicine service between October 1, 2012, and September 30, 2014, were screened for geriatric syndromes via review of medical records within 48 hours of admission. If the screen was positive, the MACE team offered the admitting team a same-day consultation involving comprehensive geriatric assessment and ongoing collaboration with the admitting team and supportive services to implement patient-centric recommendations for geriatric syndromes. RESULTS Veterans seen by MACE (n = 421) were compared with those with positive screens but without consultation (n = 372). The two groups did not significantly differ in age, comorbidity, sex, or race. All outcomes (30-day readmission, 30-day mortality, readmission costs) were in the expected direction for patients receiving MACE but did not reach statistical significance. Patients receiving MACE had lower odds of 30-day readmission (11.9% vs 14.8%; odds ratio [OR] = 0.82; 95% confidence interval [CI] = 0.54-1.25; p = .360) and 30-day mortality (5.5% vs 8.6%; OR = 0.64; CI = 0.36-1.12; p = .115), and they had lower 30-day readmission costs (MACE $15,502; CI =$12,242-$19,631; comparison =$18,335; CI = $14,641-$22,962; p = .316) than those who did not receive MACE after adjusting for age and Charlson Comorbidity Index. CONCLUSION Our MACE consultation model for older veterans with geriatric syndromes leverages the limited supply of clinicians with expertise in geriatrics. Although not statistically significant in this study of 793 subjects, MACE patients had lower odds of 30-day readmission and mortality, and lower readmission costs. J Am Geriatr Soc 67:818–824, 2019

Validation of Patient and Nurse Short Forms of the Readiness for Hospital Discharge Scale and Their Relationship to Return to the Hospital

Objective: To validate patient and nurse short forms for discharge readiness assessment and their associations with 30-day readmissions and emergency department (ED) visits. Data Sources/Study Setting: A total of 254 adult medical-surgical patients and their discharging nurses from an Eastern US tertiary hospital between May and November, 2011. Study Design Prospective longitudinal design, multinomial logistic regression analysis. Data Collection/Extraction Methods: Nurses and patients independently completed an eight-item Readiness for Hospital Discharge Scale on the day of discharge. Patient characteristics, readmissions, and ED visits were electronically abstracted. Principal Findings: Nurse assessment of low discharge readiness was associated with a six- to nine-fold increase in readmission risk. Patient self-assessment was not associated with readmission; neither was associated with ED visits. Conclusions: Nurse discharge readiness assessment should be added to existing strategies for identifying readmission risk