1. Improving evidence-based grouping of transitional care strategies in hospital implementation using statistical tools and expert review
- Author
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Jing Li, Jessica Miller Clouser, Gaixin Du, Terry C. Davis, Jane Brock, Arnold J. Stromberg, Suzanne Mitchell, Huong Q. Nguyen, Mark V. Williams, Glen Mays, and Joann Sorra
- Subjects
Male ,medicine.medical_specialty ,Evidence-based practice ,030204 cardiovascular system & hematology ,Medicare ,Health informatics ,Health administration ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Humans ,Transitional care ,030212 general & internal medicine ,Prospective Studies ,Hospital readmissions ,Aged ,Retrospective Studies ,Family caregivers ,business.industry ,Health Policy ,Nursing research ,lcsh:Public aspects of medicine ,lcsh:RA1-1270 ,Focus group ,Exploratory factor analysis ,Hospitals ,United States ,Outcome and Process Assessment, Health Care ,Family medicine ,Patient-centeredness ,Female ,business ,Research Article - Abstract
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.
- Published
- 2021