Machine Learning-Based Prediction of No-Show Telemedicine Encounters
ML models can moderately predict telemedicine no-shows in Peru, with cost-sensitive boosting techniques enhancing the identification of high-risk patients. Key predictors reflect both individual behavior and system-level contexts, suggesting the need for tailored, context-specific interventions. These findings can inform targeted strategies to optimize telemedicine, improve appointment adherence, and promote equitable health care access.
Authors
- C. Mahony Reátegui-Rivera (ORCID: https://orcid.org/0000-0002-4030-8777)
- Leonardo Rojas-Mezarina (ORCID: https://orcid.org/0000-0003-0293-7107)
- Wanting Cui (ORCID: https://orcid.org/0000-0001-7341-363X)
- Stefan Escobar-Agreda (ORCID: https://orcid.org/0000-0002-8355-4310)
- Joseph Finkelstein (ORCID: https://orcid.org/0000-0002-8084-7441)
Institutions
- National University of San Marcos (PE)
- University of Utah (US)
Publication Details
- Journal
- Telemedicine Reports
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1089/tmr.2025.0009
- Primary Topic
- Telemedicine and Telehealth Implementation
- Type
- article
- Field-Weighted Citation Impact
- 0.00