HOT-FIT-BR: A Context-Aware Evaluation Framework for Digital Health Systems in Resource-Limited Settings

2025/05/26 by Ben Rahman, Rahman, Ben
Business, Management and Accounting · #Big Data and Business Intelligence

paper · pdf · doi:10.48550/arxiv.2505.20585

Abstract

Implementation of digital health systems in low-middle-income countries (LMICs) often fails due to a lack of evaluations that take into account infrastructure limitations, local policies, and community readiness. We introduce HOT-FIT-BR, a contextual evaluation framework that expands the HOT-FIT model with three new dimensions: (1) Infrastructure Index to measure electricity/internet availability, (2) Policy Compliance Layer to ensure regulatory compliance (e.g., Permenkes 24/2022 in Indonesia), and (3) Community Engagement Fit. Simulations at Indonesian Health Centers show that HOT-FIT-BR is 58% more sensitive to detecting problems than HOT-FIT, especially in rural areas with an Infra Index <3. The framework has also proven adaptive to the context of other LMICs such as India and Kenya through local parameter adjustments.

Citations

• Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding with Large Language Models for Advanced Vision Applications
• Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms
• Viability of Mobile Forms for Population Health Surveys in Low Resource Areas

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