Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913468172468224 |
|---|---|
| author | Periáñez, África Schmitz, Kathrin Makhupula, Lazola Hassan, Moiz Moleko, Moeti del Río, Ana Fernández Nazarov, Ivan Rastogi, Aditya Tang, Dexian |
| author_facet | Periáñez, África Schmitz, Kathrin Makhupula, Lazola Hassan, Moiz Moleko, Moeti del Río, Ana Fernández Nazarov, Ivan Rastogi, Aditya Tang, Dexian |
| contents | By providing evidence-based clinical decision support, digital tools and electronic health records can revolutionize patient management, especially in resource-poor settings where fewer health workers are available and often need more training. When these tools are integrated with AI, they can offer personalized support and adaptive interventions, effectively connecting community health workers (CHWs) and healthcare facilities. The CHARM (Community Health Access & Resource Management) app is an AI-native mobile app for CHWs. Developed through a joint partnership of Causal Foundry (CF) and mothers2mothers (m2m), CHARM empowers CHWs, mainly local women, by streamlining case management, enhancing learning, and improving communication. This paper details CHARM's development, integration, and upcoming reinforcement learning-based adaptive interventions, all aimed at enhancing health worker engagement, efficiency, and patient outcomes, thereby enhancing CHWs' capabilities and community health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings Periáñez, África Schmitz, Kathrin Makhupula, Lazola Hassan, Moiz Moleko, Moeti del Río, Ana Fernández Nazarov, Ivan Rastogi, Aditya Tang, Dexian Machine Learning Artificial Intelligence Computers and Society By providing evidence-based clinical decision support, digital tools and electronic health records can revolutionize patient management, especially in resource-poor settings where fewer health workers are available and often need more training. When these tools are integrated with AI, they can offer personalized support and adaptive interventions, effectively connecting community health workers (CHWs) and healthcare facilities. The CHARM (Community Health Access & Resource Management) app is an AI-native mobile app for CHWs. Developed through a joint partnership of Causal Foundry (CF) and mothers2mothers (m2m), CHARM empowers CHWs, mainly local women, by streamlining case management, enhancing learning, and improving communication. This paper details CHARM's development, integration, and upcoming reinforcement learning-based adaptive interventions, all aimed at enhancing health worker engagement, efficiency, and patient outcomes, thereby enhancing CHWs' capabilities and community health. |
| title | Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2408.07629 |