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

TL;DR

HOT-FIT-BR is a new evaluation framework for digital health systems in resource-limited settings, incorporating infrastructure, policy, and community factors to improve detection of implementation issues.

Contribution

It extends the HOT-FIT model with three new context-aware dimensions tailored for LMICs, enhancing evaluation sensitivity and adaptability.

Findings

58% more sensitive in detecting problems than HOT-FIT

Effective in rural areas with low infrastructure index

Adaptable to different LMIC contexts like India and Kenya

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.