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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., (© 2021. The Author(s).)

Importance: Despite substantial geographic variation in Medicare per beneficiary spending in the US, little is known about the extent to which social determinants of health (SDoH) are associated with this variation., Objective: To determine the associations between SDoH and county-level price-adjusted Medicare per beneficiary spending., Design, Setting, and Participants: This cross-sectional study used county-level data on 2017 Medicare fee-for-service (FFS) spending, patient demographic characteristics (eg, age and gender) and clinical risk score, supply of health care resources (eg, number of hospital beds), and SDoH measures (eg, median income and unemployment rate) from multiple sources. Multivariable regressions were used to estimate the association of the variation in spending across quintiles with SDoH., Main Outcomes and Measures: 2017 county-level price-adjusted Medicare Parts A and B spending per beneficiary. SDoH measures included socioeconomic position, race/ethnicity, social relationships, and residential and community context., Results: Among 3038 counties with 33 495 776 Medicare FFS beneficiaries (18 352 336 [54.8%] women; mean [SD] age, 72 [1.5] years), mean Medicare price-adjusted per beneficiary spending for counties in the highest spending quintile was $3785 (95% CI, $3706-$3862) higher, or 49% higher, than spending for bottom-quintile counties (mean [SD] spending per beneficiary, $11 464 [735] vs $7679 [522]; P < .001). The total contribution (including through both direct and indirect pathways) of SDoH was 37.7% ($1428 of $3785) of this variation, compared with 59.8% ($2265 of $3785) by patient clinical risk, 14.5% ($549 of $3785) by supply of health care resources, and 19.8% ($751 of $3785) by patient demographic characteristics. When all factors were included within the same model, the direct contribution of SDoH was associated with 5.8% of the variation, compared with 4.6% by supply, 4.7% by patient demographic characteristics, and 62.0% by patient clinical risk., Conclusions and Relevance: These findings suggest social determinants of health are associated with considerable proportions of geographic variation in Medicare spending. Policies addressing SDoH for disadvantaged patients in certain regions have the potential to contain health care spending and improve the value of health care; patient SDoH may need to be accounted for in publicly reported physician performance, and in value-based purchasing incentive programs for health care professionals.

Objective: To evaluate the impact of the Affordable Care Act's Physician Payments Sunshine Act (PPSA), which mandates disclosure of industry payments to physicians, on physician prescribing of branded statins.Data Sources: Administrative claims data from 2011 to 2015 from three large national commercial insurers were provided by the Health Care Cost Institute.Study Design: We adopted a difference-in-differences and event study design, leveraging the control group of physicians in two states, MA and VT, which implemented state laws on disclosure of industry payments prior to the national PPSA. To further address potential confounding caused by differences in prescribing patterns across states, our analytical sample includes physicians practicing in border counties between the treatment (NH, NY, and RI) and control (MA and VT) states.Data Collection: We restricted our sample to physicians who had at least 50 new-fill prescription claims for statins during the five-year study period, with at least one new-fill prescription claim each year.Principal Findings: We found that the PPSA led to a 7% (p < 0.001) reduction in monthly new prescriptions of brand-name statin over the study period, with little change in generic prescribing. The reduction in branded prescriptions was concentrated among physicians with the highest tercile of drug spending pre-PPSA, with a decrease of 15% (p < 0.001) in new branded statin prescriptions. The decline was most prominent after mandated reporting of industry payments began before the payment data was published.Conclusions: The PPSA may have achieved its intended effect of reducing branded prescriptions at least in the short run, particularly among physicians most likely to have engaged in excessive or low-value prescribing of branded drugs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Poorly managed hospital discharges and care transitions between health care facilities can cause poor outcomes for both patients and their caregivers. Unfortunately, the usual approach to health care delivery does not support continuity and coordination across the settings of hospital, doctors' offices, home or nursing homes. Though complex efforts with multiple components can improve patient outcomes and reduce 30-day readmissions, research has not identified which components are necessary. Also we do not know how delivery of core components may need to be adjusted based on patient, caregiver, setting or characteristics of the community, or how system redesign can be accelerated.Methods/design: Project ACHIEVE focuses on diverse Medicare populations such as individuals with multiple chronic diseases, patients with low health literacy/numeracy and limited English proficiency, racial and ethnic minority groups, low-income groups, residents of rural areas, and individuals with disabilities. During the first phase, we will use focus groups to identify the transitional care outcomes and components that matter most to patients and caregivers to inform development and validation of assessment instruments. During the second phase, we will evaluate the comparative effectiveness of multi-component care transitions programs occurring across the U.S. Using a mixed-methods approach for this evaluation, we will study historical (retrospective) and current and future (prospective) groups of patients, caregivers and providers using site visits, surveys, and clinical and claims data. In this natural experiment observational study, we use a fractional factorial study design to specify comparators and estimate the individual and combined effects of key transitional care components.Discussion: Our study will determine which evidence-based transitional care components and/or clusters most effectively produce patient and caregiver desired outcomes overall and among diverse patient and caregiver populations in different healthcare settings. Using the results, we will develop concrete, actionable recommendations regarding how best to implement these strategies. Finally, this work will provide tools for hospitals, community-based organizations, patients, caregivers, clinicians and other stakeholders to help them make informed decisions about which strategies are most effective and how best to implement them in their communities.Trial Registration: Registered as NCT02354482 on clinicaltrials.gov on 1/29/2015. [ABSTRACT FROM AUTHOR]