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

Objectives: 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. Methods: 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. Results: 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. Conclusion: 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.

Effects of post-discharge telemonitoring on 30-day chronic obstructive pulmonary disease readmissions and mortality.

INTRODUCTION: Much attention has been focused on decreasing chronic obstructive pulmonary disease (COPD) hospital readmissions. The US health system has struggled to meet this goal. The objective of this study was to assess the efficacy of telehealth services on the reduction of hospital readmission and mortality rates for COPD. METHODS: We used a cross-sectional design to examine the association between hospital risk-adjusted readmission and mortality rates for COPD and hospital use of post-discharge telemonitoring (TM). Data for 777 hospitals were sourced from the Centers for Medicare & Medicaid Services and the American Hospital Association annual surveys. Propensity score matching using the kennel weights method was applied to calculate the weighted probability of being a hospital that offers post-discharge TM services. RESULTS: Hospitals with post-discharge TM had about 34% significantly higher odds (adjusted odds ratio (AOR) = 1.34; 95% confidence interval (CI) 1.06-1.70) of 30-day COPD readmission and 33% significantly lower odds (AOR = 0.67; 95% CI 0.50-0.90) of 30-day COPD mortality compared to hospitals without post-discharge TM services. DISCUSSION: Overall, hospitals that offer post-discharge TM services have seen an improvement in 30-day COPD mortality rates. However, those same hospitals have also experienced a significant increase in 30-day COPD readmissions. TM can potentially decrease mortality in patients recently admitted for acute exacerbation of COPD. The results provide further evidence that readmissions present a problematic assessment of health-care quality, as the need for readmission may or may not be directly related to the quality of care received while in hospital.

A Multi-Component Transition of Care Improvement Project to Reduce Hospital Readmissions Following Ischemic Stroke.

Background: Ischemic stroke (IS) is a common cause of hospitalization which carries a significant economic burden and leads to high rates of death and disability. Readmission in the first 30 days after hospitalization is associated with increased healthcare costs and higher risk of death and disability. Efforts to decrease the number of patients returning to the hospital after IS may improve quality and cost of care. Methods: Improving care transitions to reduce readmissions is amenable to quality improvement (QI) initiatives. A multi-component QI intervention directed at IS patients being discharged to home from a stroke unit at an academic comprehensive stroke center using IS diagnosis-driven home care referrals, improved post-discharge telephone calls, and timely completion of discharge summaries was developed. The improvement project was implemented on July 1, 2019, and evaluated for the 6 months following initiation in comparison to the same 6-month period pre-intervention in 2018. Results: Following implementation, a statistically significant decrease in 30-day all-cause same-hospital readmission rates from 7.4% to 2.8% (p = .031, d = 1.61) in the project population and from 6.6% to 3% (p = .010, d = 1.43) in the overall IS population was observed. Improvement was seen in all process measures as well as in patient satisfaction scores. Conclusions: An evidence-based bundled process improvement intervention for IS patients discharged to home was associated with decreased hospital readmission rates following IS.

Evaluation of COPD Chronic Care Management Collaborative to Reduce Emergency Department and Hospital Revisits Across U.S. Hospitals.

Background: Chronic obstructive pulmonary disease (COPD) is the third-leading cause of early readmissions. The Centers for Medicare and Medicaid instituted a financial penalty for excessive COPD readmissions galvanizing hospitals to implement effective strategies to reduce readmissions. We evaluated a 6-month COPD Chronic Care Management Collaborative to support hospitals to reduce preventable COPD-related revisits. Methods: Sites were recruited among nearly 300 Vizient, Inc., members. The Collaborative used performance improvement initiatives to assist with implementation of effective strategies. Participants submitted performance data for 2 outcome measures: emergency department (ED) and hospital revisits. Results: Forty-seven members enrolled (Part I+II: n=33; Part I: n=3; Part II: n=11) of which 23 submitted data (n=23/47). The majority (n=19/23, 83%) reduced rates of COPD-related ED and/or hospital revisits. Among all 23 sites, the change in ED visits went from 11.05% to 10.87%; among 7 sites with reductions in ED visits, the reduction was 12.7% to 9%. Among all 23 sites, there were not reductions in hospital readmissions (18.53% to 18.64%); among 7 sites with reductions, the readmission rate went from 20.1% to 15.6%. The mean reach across 17 hospitals reporting reach for their most successful measure at baseline was 35.2% (SD=26.7%) and for the other 6, reporting reach at follow-up was 73.8%% (SD=18.3%); of note, only 3 sites submitted both baseline and follow-up data. Conclusions: The Collaborative successfully supported the majority of sites in reducing COPD-related ED and/or hospital revisits using subject matter experts and coaching strategies to support hospitals' implementation of COPD quality improvement interventions.

The Association of Readmission Reduction Activities with Primary Care Practice Readmission Rates.

BACKGROUND: A great deal of research has focused on how hospitals influence readmission rates. While hospitals play a vital role in reducing readmissions, a significant portion of the work also falls to primary care practices. Despite this critical role of primary care, little empirical evidence has shown what primary care characteristics or activities are associated with reductions in hospital admissions. OBJECTIVE: To examine the relationship between practices' readmission reduction activities and their readmission rates. DESIGN, SETTING, AND PARTICIPANTS: A retrospective study of 1,788 practices who responded to the National Survey of Healthcare Organizations and Systems (fielded 2017-2018) and 415,663 hospital admissions for Medicare beneficiaries attributed to those practices from 2016 100% Medicare claims data. We constructed mixed-effects logistic regression models to estimate practice-level readmission rates and a linear regression model to evaluate the association between practices' readmission rates with their number of readmission reduction activities. INTERVENTIONS: Standardized composite score, ranging from 0 to 1, representing the number of a practice's readmission reduction capabilities. The composite score was composed of 12 unique capabilities identified in the literature as being significantly associated with lower readmission rates (e.g., presence of care manager, medication reconciliation, shared-decision making, etc.). MAIN OUTCOMES AND MEASURES: Practices' readmission rates for attributed Medicare beneficiaries. KEY RESULTS: Routinely engaging in more readmission reduction activities was significantly associated (P < .05) with lower readmission rates. On average, practices experienced a 0.05 percentage point decrease in readmission rates for each additional activity. Average risk-standardized readmission rates for practices performing 10 or more of the 12 activities in our composite measure were a full percentage point lower than risk-standardized readmission rates for practices engaging in none of the activities. CONCLUSIONS: Primary care practices that engaged in more readmission reduction activities had lower readmission rates. These findings add to the growing body of evidence suggesting that engaging in multiple activities, rather than any single activity, is associated with decreased readmissions.

Understanding the groups of care transition strategies used by U.S. hospitals: an application of factor analytic and latent class methods.

BACKGROUND: After activation of the Hospital Readmission Reduction Program (HRRP) in 2012, hospitals nationwide experimented broadly with the implementation of Transitional Care (TC) strategies to reduce hospital readmissions. Although numerous evidence-based TC models exist, they are often adapted to local contexts, rendering large-scale evaluation difficult. Little systematic evidence exists about prevailing implementation patterns of TC strategies among hospitals, nor which strategies in which combinations are most effective at improving patient outcomes. We aimed to identify and define combinations of TC strategies, or groups of transitional care activities, implemented among a large and diverse cohort of U.S. hospitals, with the ultimate goal of evaluating their comparative effectiveness. METHODS: We collected implementation data for 13 TC strategies through a nationwide, web-based survey of representatives from short-term acute-care and critical access hospitals (N = 370) and obtained Medicare claims data for patients discharged from participating hospitals. TC strategies were grouped separately through factor analysis and latent class analysis. RESULTS: We observed 348 variations in how hospitals implemented 13 TC strategies, highlighting the diversity of hospitals' TC strategy implementation. Factor analysis resulted in five overlapping groups of TC strategies, including those characterized by 1) medication reconciliation, 2) shared decision making, 3) identifying high risk patients, 4) care plan, and 5) cross-setting information exchange. We determined that the groups suggested by factor analysis results provided a more logical grouping. Further, groups of TC strategies based on factor analysis performed better than the ones based on latent class analysis in detecting differences in 30-day readmission trends. CONCLUSIONS: U.S. hospitals uniquely combine TC strategies in ways that require further evaluation. Factor analysis provides a logical method for grouping such strategies for comparative effectiveness analysis when the groups are dependent. Our findings provide hospitals and health systems 1) information about what groups of TC strategies are commonly being implemented by hospitals, 2) strengths associated with the factor analysis approach for classifying these groups, and ultimately, 3) information upon which comparative effectiveness trials can be designed. Our results further reveal promising targets for comparative effectiveness analyses, including groups incorporating cross-setting information exchange.

Association Between Racial Disparities in Hospital Length of Stay and the Hospital Readmission Reduction Program.

BACKGROUND: On average Black patients have longer LOS than comparable White patients. Longer hospital length of stay (LOS) may be associated with higher readmission risk. However, evidence suggests that the Hospital Readmission Reduction Program (HRRP) reduced overall racial differences in 30-day adjusted readmission risk. Yet, it is unclear whether the HRRP narrowed these LOS racial differences. OBJECTIVE: We examined the relationship between Medicare-insured Black-White differences in average, adjusted LOS (ALOS) and the HRRP's implementation and evaluation periods. METHODS: Using 2009-2017 data from State Inpatient Dataset from New York, New Jersey, and Florida, we employed an interrupted time series analysis with multivariate generalized regression models controlling for patient, disease, and hospital characteristics. Results are reported per 100 admissions. RESULTS: We found that for those discharged home, Black-White ALOS differences significantly widened by 4.15 days per 100 admissions (95% CI: 1.19 to 7.11, P < 0.001) for targeted conditions from before to after the HRRP implementation period, but narrowed in the HRRP evaluation period by 1.84 days per 100 admissions for every year-quarter (95% CI: -2.86 to -0.82, P < 0.001); for those discharged to non-home destinations, there was no significant change between HRRP periods, but ALOS differences widened over the study period. Black-White ALOS differences for non-targeted conditions remained unchanged regardless of HRRP phase and discharge destination. CONCLUSION: Increased LOS for Black patients may have played a role in reducing Black-White disparities in 30-day readmission risks for targeted conditions among patients discharged to home.

Association of Post-discharge Service Types and Timing with 30-Day Readmissions, Length of Stay, and Costs.

BACKGROUND: Although early follow-up after discharge from an index admission (IA) has been postulated to reduce 30-day readmission, some researchers have questioned its efficacy, which may depend upon the likelihood of readmission at a given time and the health conditions contributing to readmissions. OBJECTIVE: To investigate the relationship between post-discharge services utilization of different types and at different timepoints and unplanned 30-day readmission, length of stay (LOS), and inpatient costs. DESIGN, SETTING, AND PARTICIPANTS: The study sample included 583,199 all-cause IAs among 2014 Medicare fee-for-service beneficiaries that met IA inclusion criteria. MAIN MEASURES: The outcomes were probability of 30-day readmission, average readmission LOS per IA discharge, and average readmission inpatient cost per IA discharge. The primary independent variables were 7 post-discharge health services (institutional outpatient, primary care physician, specialist, non-physician provider, emergency department (ED), home health care, skilled nursing facility) utilized within 7 days, 14 days, and 30 days of IA discharge. To examine the association with post-discharge services utilization, we employed multivariable logistic regressions for 30-day readmissions and two-part models for LOS and inpatient costs. KEY RESULTS: Among all IA discharges, the probability of unplanned 30-day readmission was 0.1176, the average readmission LOS per discharge was 0.67 days, and the average inpatient cost per discharge was $5648. Institutional outpatient, home health care, and primary care physician visits at all timepoints were associated with decreased readmission and resource utilization. Conversely, 7-day and 14-day specialist visits were positively associated with all three outcomes, while 30-day visits were negatively associated. ED visits were strongly associated with increases in all three outcomes at all timepoints. CONCLUSION: Post-discharge services of different types and at different timepoints have varying impacts on 30-day readmission, LOS, and costs. These impacts should be considered when coordinating post-discharge follow-up, and their drivers should be further explored to reduce readmission throughout the health care system.

Specialty-Specific Readmission Risk Models Outperform General Models in Estimating Hepatopancreatobiliary Surgery Readmission Risk.

BACKGROUND: Readmissions are costly and inconvenient for patients, and occur frequently in hepatopancreatobiliary (HPB) surgery practice. Readmission prediction tools exist, but most have not been designed or tested in the HPB patient population. METHODS: Pancreatectomy and hepatectomy operation-specific readmission models defined as subspecialty readmission risk assessments (SRRA) were developed using clinically relevant data from merged 2014-15 ACS NSQIP Participant Use Data Files and Procedure Targeted datasets. The two derived procedure-specific models were tested along with 6 other readmission models in institutional validation cohorts in patients who had pancreatectomy or hepatectomy, respectively, between 2013 and 2017. Models were compared using area under the receiver operating characteristic curves (AUC). RESULTS: A total of 16,884 patients (9169 pancreatectomy and 7715 hepatectomy) were included in the derivation models. A total of 665 patients (383 pancreatectomy and 282 hepatectomy) were included in the validation models. Specialty-specific readmission models outperformed general models. AUC characteristics of the derived pancreatectomy and hepatectomy SRRA (pancreatectomy AUC=0.66, hepatectomy AUC=0.74), modified Readmission After Pancreatectomy (AUC=0.76), and modified Readmission Risk Score for hepatectomy (AUC=0.78) outperformed general models for readmission risk: LOS/2 + ASA integer-based score (pancreatectomy AUC=0.58, hepatectomy AUC=0.66), LACE Index (pancreatectomy AUC=0.54, hepatectomy AUC=0.62), Unplanned Readmission Nomogram (pancreatectomy AUC=0.52, hepatectomy AUC=0.55), and institutional ARIA (pancreatectomy AUC=0.46, hepatectomy AUC=0.58). CONCLUSION: HPB readmission risk models using 30-day subspecialty-specific data outperform general readmission risk tools. Hospitals and practices aiming to decrease readmissions in HPB surgery patient populations should use specialty-specific readmission reduction strategies.

Triple Aim and the Hospital Readmission Reduction Program.

OBJECTIVES: Despite substantial attention on hospital readmission rates, the impact of the Hospital Readmission Reduction Program (HRRP) on a comprehensive set of Triple Aim goals has not been studied: improve hospital quality, reduce cost, and improve patient experience. METHODS: We analyze inpatient claims data from 2006 to 2015 from the Dallas Fort Worth Hospital Council Foundation with a panel of 27,397 patients with chronic obstructive pulmonary disease and congestive heart failure. We deploy a quasi-natural experiment using a difference-in-difference specification to estimate the effect of HRRP effect on readmission rates, length of stay (LOS), and hospital satisfaction. RESULTS: We find that the likelihood of 30-day readmissions declined by 2.6%, average LOS decreased by 7.9%, and overall hospital rating increased by 2.1% among hospitals that fell under the scope of the HRRP, compared to non-HRRP hospitals. Our results provide evidence of a spillover effect of the HRRP in terms of its impact not only on Medicare patients, but across all insurance types, and other performance measures such as cost and patient experience. CONCLUSION: Our findings indicate that HRRP hospitals do not trade-off reductions in readmission rates with lower quality across other patient health outcomes. Rather, we find evidence that the HRRP has affected all 3 dimensions of the Triple Aim with respect to patient and hospital outcomes.

Preventing COPD Readmissions Under the Hospital Readmissions Reduction Program: How Far Have We Come?

The Hospital Readmissions Reduction Program (HRRP) was developed and implemented by the Centers for Medicare & Medicaid Services to curb the rate of 30-day hospital readmissions for certain common, high-impact conditions. In October 2014, COPD became a target condition for which hospitals were penalized for excess readmissions. The appropriateness, utility, and potential unintended consequences of the metric have been a topic of debate since it was first enacted. Nevertheless, there is evidence that hospital policies broadly implemented in response to the HRRP may have been responsible for reducing the rate of readmissions following COPD hospitalizations even before it was added as a target condition. Since the addition of the COPD condition to the HRRP, several predictive models have been developed to predict COPD survival and readmissions, with the intention of identifying modifiable risk factors. A number of interventions have also been studied, with mixed results. Bundled care interventions using the electronic health record and patient education interventions for inhaler education have been shown to reduce readmissions, whereas pulmonary rehabilitation, follow-up visits, and self-management programs have not been consistently shown to do the same. Through this program, COPD has become recognized as a public health priority. However, 5 years after COPD became a target condition for HRRP, there continues to be no single intervention that reliably prevents readmissions in this patient population. Further research is needed to understand the long-term effects of the policy, the role of competing risks in measuring quality, the optimal postdischarge care for patients with COPD, and the integrated use of predictive modeling and advanced technologies to prevent COPD readmissions.

Financial Performance of Hospitals in the Appalachian Region Under the Hospital Readmissions Reduction Program and Hospital Value-Based Purchasing Program.

PURPOSE: The Hospital Readmission and Reduction Program (HRRP) and Hospital Value-Based Purchasing Program (HVBP) propose to improve quality of patient care by either rewarding or penalizing hospitals through inpatient reimbursement. This study analyzes the effect of both programs on profitability of hospitals located in the Appalachian Region (AR) compared to hospitals in Appalachian states and the rest of the United States. METHODS: This study used a retrospective research design with a longitudinal unbalanced panel dataset from 2008 to 2015. Hospitals participating in both HRRP and HVBP during this time frame were included in the study. A difference-in-difference model with hospital-level fixed effects, controlling for hospital and market characteristics, was used to determine effects of both programs on profitability of hospitals serving the AR, Appalachian states, and the rest of the United States. FINDINGS: After implementation of HRRP and HVBP, only hospitals located in Appalachian states experienced a significant decrease in operating margin (-1.14 percentage points). Unexpectedly, during the same time period, total margin increased significantly for hospitals located in the AR (1.05 percentage points), Appalachian states (1.71 percentage points), and the rest of the United States (2.38 percentage points). CONCLUSIONS: HRRP and HVBP financially incentivize hospitals to focus efforts on improving patient care. The programs may not have the anticipated results. Increases in total margin for all hospitals during the study period indicate access to nonpatient revenues, offsetting the financial penalties from both programs. This revenue source may undermine the program's objectives of delivering value and achieving quality outcomes.

Prognostic Implications of Early and Midrange Readmissions After Acute Heart Failure Hospitalizations: A Report From a Japanese Multicenter Registry.

Background Although 30-day readmission is thought to be an important quality indicator in patients with hospitalized heart failure, its prognostic impact and comparison of patients who were readmitted beyond 30 days has not been investigated. We assessed early (0-30 days) versus midrange (31-90 days) readmission in terms of incidence and distribution, and elucidated whether the timing of readmission could have a different prognostic significance. Methods and Results We examined patients with hospitalized heart failure registered in the WET-HF (West Tokyo Heart Failure) registry. The primary outcomes analyzed were all-cause death and HF readmission. Data of 3592 consecutive patients with hospitalized heart failure (median follow-up, 2.0 years [interquartile range, 0.8-3.1 years]; 39.6% women, mean age 73.9±13.3 years) were analyzed. Within 90 days after discharge, HF readmissions occurred in 11.1% patients. Of them, patients readmitted within 30 and 31 to 90 days after discharge accounted for 43.1% and 56.9%, respectively. Independent predictors of 30- and 90-day readmission were almost identical, and after adjustment, readmission for HF within 90 days (including both early and midrange readmission) was an independent predictor of subsequent all-cause death (hazard ratio, 2.36; P<0.001). Among 90-day readmitted patients, the time interval from discharge to readmission was not significantly associated with subsequent all-cause death. Conclusions Among patients readmitted within 90 days after index hospitalization discharge, ≈60% of readmission events occurred beyond 30 days. Patients readmitted within 90 days had a higher risk of long-term mortality, regardless of the temporal proximity of readmission to the index hospitalization.

The Association Between Structural, Performance, and Community Factors and the Likelihood of Receiving a Penalty Under the Hospital Readmissions Reduction Program (Fiscal Year 2013-2019).

Purpose: Little is known about the role of structural, performance, and community factors that impact the likelihood of receiving a penalty under the Hospital Readmission Reduction Program. This study examined the association between structural, performance, and community factors and the likelihood of receiving a penalty as well as investigated the likelihood of hospitals serving vulnerable populations of receiving a penalty. Methods: Centers for Medicare and Medicaid Services and United States Census Bureau data were used in this analysis. Ordered logistic regressions in a cross-sectional analysis were employed to estimate the probability of receiving a high or low penalty in the fiscal year 2013 through 2019. Results: On average, medium-sized, major teaching, and safety-net hospitals had the highest proportion of hospitals with a high penalty. After controlling for performance and community factors, structural factor variables such as safety-net status, rural status, and teaching status either were no longer significant or the likelihood magnitude changed. However, after controlling for performance and community factors, the statistical significance of hospital size variables and geographic location persisted across the years. Length of stay and occupancy rate variables were also statistically significant across the 7 years under review. Conclusion: Taken together, structural, performance, and community factors are important in explaining variation in the likelihood of receiving a penalty. There is no evidence that safety-net, rural, and public hospitals are more likely to receive a penalty. The results also suggest that there is room for providers to reduce avoidable readmissions and policymakers to mitigate unintended consequences.

Use of Observation Status Versus Readmission in Elective Total Knee and Hip Arthroplasty Returns to Hospital: A Single-Institution Perspective.

BACKGROUND: The Affordable Care Act's Readmission Reduction Program (RRP) and ongoing transparency efforts to promote consumer-driven competition place significant institutional focus on improving 30-day readmission rates. It remains unclear whether the reduction in readmission rates subsequent to the RRP occurred due to improved quality and/or partly due to increased use of observation status in conditions that may have been classified as readmissions prior to the RRP. We hypothesize that a significant percentage of our institution's 30-day readmissions after elective total knee and hip arthroplasty (TKA/THA) overestimate the needs, duration, and complexity of the hospital-based intervention and inaccurately reflect the quality of service provided. METHODS: We performed a retrospective review of prospectively collected quality control data for 30-day returns to hospital after elective TKA/THA at our institution over a 2-year period. After stratification of the readmissions to under 48-hour and over 48-hour length of stay, we calculated the financial implications to our institution if the under 48-hour length of stay admissions were reclassified as an observation by applying discharge-weighted and payment-weighted analyses to the 2017 RRP report. We then calculated the out-of-pocket expenses for the under 48-hour Medicare subpopulation. RESULTS: We found that 16.7% of the 30-day readmissions after elective TKA/THA required a length of stay under 48 hours. If the short length of stay TKA/THA readmissions were reclassified as observations, our institution's 2018 RRP penalty would have been reduced to 39% or $334,512.28. However, this reclassification would result in an increase in out-of-pocket expenses by $540.25 (range $291.56-$1105.08) per patient. CONCLUSION: A subpopulation of 30-day readmissions does not require a level of care consistent with inpatient admission services. Classification of this short length of stay subpopulation as an observation vs an admission per Centers for Medicare and Medicaid Services guidelines would have removed our institution from the TKA/THA-specific RRP penalty. However, this would result in the unintended consequence of shifting costs, particularly self-administered drug costs, to patients.

Post-Discharge Services for Different Diagnoses Than Index Hospitalization Predict Decreased 30-Day Readmissions Among Medicare Beneficiaries.

BACKGROUND: Efforts to reduce hospital readmissions include post-discharge interventions related to the illness treated during the index hospitalization (IH). These efforts may be inadequate because readmissions are precipitated by a wide range of health conditions unrelated to the primary diagnosis of the IH. OBJECTIVE: To investigate the relationship between post-discharge health services utilization for the same or a different diagnosis than the IH and unplanned 30-day readmission. DESIGN AND PARTICIPANTS: The study sample included 583,199 all-cause IHs among 2014 Medicare fee-for-service beneficiaries. For all-cause IH, as well as individually for heart failure, myocardial infarction, and pneumonia IH, we used multivariable logistic regressions to investigate the association between post-discharge services utilization and readmission. MAIN MEASURES: The outcome was unplanned 30-day readmission. Primary independent variables were post-discharge services utilization, including institutional outpatient, office-based primary care, office-based specialist, office-based non-physician practitioner, emergency department, home health care, and skilled nursing facility providers. KEY RESULTS: Among all-cause IH, 11.7% resulted in unplanned 30-day readmissions, and only 18.1% of readmissions occurred for the same primary diagnosis as IH. A substantial majority of post-discharge health services were utilized for a primary diagnosis differing from IH. Compared with no visit, institutional outpatient visits for the same primary diagnosis as IH (odds ratio [OR], 0.33; 95% confidence interval [CI], 0.31-0.34) and for a different primary diagnosis than IH (OR, 0.36; 95% CI, 0.35-0.37) were similarly strongly associated with decreased unplanned 30-day readmission. Primary care physician, specialist, non-physician practitioner, and home health care showed similar patterns. IH for heart failure, myocardial infarction, and pneumonia manifested similar patterns to all-cause IH both in terms of post-discharge services utilization and in terms of its impact on readmission. CONCLUSIONS: To reduce unplanned 30-day readmission more effectively, discharge planning should include post-discharge services to address health conditions beyond the primary cause of the IH.

Updates in heart failure 30-day readmission prevention.

Heart failure (HF) and HF 30-day readmission rates have been a major focus of efforts to reduce health care cost in the recent era. Since the implementation of the Affordable Care Act (ACA) in 2012 and the Hospital Readmission Reduction Program (HRRP), concerted efforts have focused on reduction of 30-day HF readmissions and other admission diagnoses targeted by the HRRP. Hospitals and organizations have instituted wide-ranging programs to reduce short-term readmissions, but the data supporting these programs is often mixed. In this review, we will discuss the challenges associated with reducing HF readmissions and summarize the rationale and effect of specific programs on HF 30-day readmission rates, ranging from medical therapy and adherence to remote hemodynamic monitoring. Finally, we will review the effect that the focus on reducing 30-day HF readmissions has had on the care of the HF patient.

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. METHODS: We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. RESULTS: Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. CONCLUSIONS: Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.

Is Mental Illness a Risk Factor for Hospital Readmission?

People with mental illnesses (MI) receive suboptimal care for medical comorbidities and their high risk for readmission may be addressed by adequate medication management and follow-up care. We examined the association between MI, medication changes, and post-discharge outpatient visits with 30-day readmission in 40,048 Medicare beneficiaries hospitalized for acute myocardial infarction, heart failure or pneumonia. Beneficiaries with MI were more likely to be readmitted than those without MI (14 vs. 11%). Probability of readmission was 13 and 12% when medications were dropped or added, respectively, versus 11% when no change was made. Probability of readmission also increased with outpatient visits.