Data Analytics and Modeling for Appointment No-show in Community Health Centers.
Iman Mohammadi,Huanmei Wu,Ayten Turkcan,Tammy Toscos,Bradley N. Doebbeling +4 more
TLDR
EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs, and the application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care.Abstract:
Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and ...read more
Citations
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Socioeconomic Disparities in Patient Use of Telehealth During the Coronavirus Disease 2019 Surge.
TL;DR: In this article, the authors assess demographic and socioeconomic factors associated with patient participation in telehealth during the coronavirus disease 2019 (COVID-19) pandemic and find that age, sex, median household income, insurance status, and marital status are associated with telehealth.
Journal ArticleDOI
How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection.
Maia Jacobs,Melanie F. Pradier,Thomas H. McCoy,Roy H. Perlis,Finale Doshi-Velez,Krzysztof Z. Gajos +5 more
TL;DR: In this paper, the authors used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation.
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New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows
TL;DR: New wrapper methods based on three variants of the proposed algorithm, Opposition-based Self-Adaptive Cohort Intelligence (OSACI), showed that the proposed algorithms outperformed the other compared algorithms by achieving higher dimensionality reduction and better convergence speed while achieving comparable AUC, sensitivity, and specificity scores.
Journal ArticleDOI
Patient No-Show Prediction: A Systematic Literature Review
TL;DR: A systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art, and an important finding is that only two studies achieved an accuracy higher than the show rate.
Journal ArticleDOI
A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories
TL;DR: The reliable, TAN-based posterior probabilities and conditional relationships among the predictors for such a parsimonious model that has a fairly high sensitivity in detecting the minority samples, can be adopted by primary care facilities to improve the decision-making process in managing the no-show problem.
References
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