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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20585 |
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| _version_ | 1866910997786132480 |
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| author | Rahman, Ben |
| author_facet | Rahman, Ben |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20585 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HOT-FIT-BR: A Context-Aware Evaluation Framework for Digital Health Systems in Resource-Limited Settings Rahman, Ben Human-Computer Interaction Computers and Society 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. |
| title | HOT-FIT-BR: A Context-Aware Evaluation Framework for Digital Health Systems in Resource-Limited Settings |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2505.20585 |