Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic.

Autor:	Lee SY; Department of Health Services Administration, The University of Alabama at Birmingham, Birmingham, AL 35233, United States., Eagleson RM; Center for Outcomes and Effectiveness Research and Education, The University of Alabama at Birmingham, Birmingham, AL 35233, United States., Hearld LR; Department of Health Services Administration, The University of Alabama at Birmingham, Birmingham, AL 35233, United States., Gibson MJ; Center for Outcomes and Effectiveness Research and Education, The University of Alabama at Birmingham, Birmingham, AL 35233, United States., Hearld KR; Department of Health Services Administration, The University of Alabama at Birmingham, Birmingham, AL 35233, United States., Hall AG; Department of Health Services Administration, The University of Alabama at Birmingham, Birmingham, AL 35233, United States., Burkholder GA; Department of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States., McMahon J; College of Science and Mathematics, Auburn University, Auburn, AL 36849, United States., Mahmood SY; UAB Medicine, Birmingham, AL 35233, United States., Spraberry CT; UAB Medicine, Birmingham, AL 35233, United States., Baker TJ; UAB Medicine, Birmingham, AL 35233, United States., Garretson AR; UAB Medicine, Birmingham, AL 35233, United States., Bradley HM; Cooper Green Mercy Health Services Authority, Birmingham, AL 35233, United States., Mugavero MJ; Center for Outcomes and Effectiveness Research and Education, The University of Alabama at Birmingham, Birmingham, AL 35233, United States.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2024 Nov 09; Vol. 7 (4), pp. ooae086. Date of Electronic Publication: 2024 Nov 09 (Print Publication: 2024).
DOI:	10.1093/jamiaopen/ooae086
Abstrakt:	Objective: This study applies predictive analytics to identify patients at risk of missing appointments at a novel post-discharge clinic (PDC) in a large academic health system. Recognizing the critical role of appointment adherence in the success of new clinical ventures, this research aims to inform future targeted interventions to increase appointment adherence. Materials and Methods: We analyzed electronic health records (EHRs) capturing a wide array of demographic, socio-economic, and clinical variables from 2168 patients with scheduled appointments at the PDC from September 2022 to August 2023. Logistic regression, decision trees, and eXtreme Gradient Boosting (XGBoost) algorithms were employed to construct predictive models for appointment adherence. Results: The XGBoost machine learning model outperformed logistic regression and decision trees with an area under the curve (AUC) of 72% vs 65% and 67%, respectively, in predicting missed appointments, despite limited availability of historical data. Key predictors included patient age, number of days between appointment scheduling and occurrence, insurance status, marital status, and mental health and cardiac disease conditions. Discussion: Findings underscore the potential of machine learning predictive analytics to significantly enhance patient engagement and operational efficiency in emerging healthcare settings. Optimizing predictive models can help balance the early identification of patients at risk of non-adherence with the efficient allocation of resources. Conclusion: The study highlights the potential value of employing machine learning techniques to inform interventions aimed at improving appointment adherence in a post-discharge transition clinic environment. Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
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