TY - JOUR
T1 - Improving evidence-based grouping of transitional care strategies in hospital implementation using statistical tools and expert review
AU - Li, Jing
AU - Du, Gaixin
AU - Clouser, Jessica Miller
AU - Stromberg, Arnold
AU - Mays, Glen
AU - Sorra, Joann
AU - Brock, Jane
AU - Davis, Terry
AU - Mitchell, Suzanne
AU - Nguyen, Huong Q.
AU - Williams, Mark V.
N1 - Funding Information:
Research reported in this manuscript was funded through a Patient-Centered Outcomes Research Institute (PCORI) Award (TC-1403-14049). The statements in this manuscript are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Hospital readmissions
KW - Patient-centeredness
KW - Transitional care
UR - http://www.scopus.com/inward/record.url?scp=85099112270&partnerID=8YFLogxK
U2 - 10.1186/s12913-020-06020-9
DO - 10.1186/s12913-020-06020-9
M3 - Article
C2 - 33413334
AN - SCOPUS:85099112270
SN - 1472-6963
VL - 21
JO - BMC health services research
JF - BMC health services research
IS - 1
M1 - 35
ER -