Improving evidence-based grouping of transitional care strategies in hospital implementation using statistical tools and expert review

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.

Perceived Self-Efficacy, Confidence, and Skill Among Factors of Adult Patient Participation in Transitional Care: A Systematic Review of Quantitative Studies

“…Although progress has been made on testing a number of transitional care interventions to improve care-transitions, a downward trend of readmission rates has been unevenly observed ( Angraal et al, 2018 ; Gerhardt et al, 2013 ; Zuckerman et al, 2016 ). Among U.S. hospitals, wide variation in readmission rates is noted, as is persistently elevated rates among low-income patients and other vulnerable subgroups ( Li et al, 2021 ). In a 2018 retrospective cohort study examining readmission to a large, rural acute tertiary and quintenary care facility, it was identified patients with ambulation difficulty, ADL deficits, difficulties completing errands, and those with a history of substance abuse treatment, experienced higher 30-day readmission rates than those without one or more of these characteristics ( Mallow et al, 2020 ).…”

“…The discipline of social work is known for the Bridge Model of Transitional Care ( Alvarez et al, 2016 ), while the discipline of pharmacy has long focused on mitigating medication risks during times of care-transitions ( Conklin et al, 2014 ). Recently, multidisciplinary efforts to improve care-transitions and develop recommendations has begun ( Li et al, 2021 ) and nursing education accreditors are recognizing both interprofessional partnerships and care that spans across settings throughout the lifespan as areas of emphasis ( American Association of Colleges of Nursing, 2021 ). Hence, future interventions aimed at patient participation in care-transitions should be multidisciplinary in nature.…”

Development and Psychometric Properties of Surveys to Assess Patient and Family Caregiver Experience With Care Transitions

“…Instead, the type or number of services or components of care can be examined to identify which groups or clusters result in more positive patient and caregiver experiences and outcomes. Another study by the Project ACHIEVE team [12] conducted analyses of survey data from hospitals and selected data from the patient survey and identi ed ve groups of transitional care components or strategies that were most likely to co-occur and be delivered by hospitals. The strategies patients reported receiving were more important in predicting 30-day hospital readmissions than strategies that hospitals reported delivering.…”

“…This study was undertaken as one of the speci c aims of a much larger project funded by the Patient-Centered Outcomes Research Institute (PCORI) called Project ACHIEVE (Achieving Patient-Centered Care and Optimized Health In Care Transitions by Evaluating the Value of Evidence). The overall aims of Project ACHIEVE were to identify the transitional care services and outcomes that matter most to patients and family caregivers, and to identify which combinations of transitional care strategies, or groups of services, yield desired outcomes among a large and diverse cohort of United States (US) hospitals [11,12].…”

Finite mixtures of normal distributions in the study of the error in altimetry

“…In this way, the probability of an observed value comes from the mixture of the probabilities that it comes from each of the distributions that make up the mixture. The first works date back to 1894 when Pearson worked with the mixture of two normal distributions with the same variance and has been developed by multiple researchers (a detailed review can be seen in McLachlan-Peel, 2000;McLachlan et al, 2019, or Huang et al, 2017 and some examples of recent applications of mixtures in different fields can be seen, for instance, in Zhao et al, 2021or Li et al, 2021. To the best of our knowledge, this approach has never been previously applied to the case of errors in DEM.…”