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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Accesso online: | https://arxiv.org/abs/2305.06426 |
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| _version_ | 1866911721900212224 |
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| author | Adams, Katherine B. Boutilier, Justin J. Deo, Sarang Mintz, Yonatan |
| author_facet | Adams, Katherine B. Boutilier, Justin J. Deo, Sarang Mintz, Yonatan |
| contents | Diabetes is a global health priority, especially in low- and-middle-income countries, where over 50% of premature deaths are attributed to high blood glucose. Community Health Worker (CHW) programs can provide affordable and culturally tailored solutions for early detection and management of diabetes. We introduce an optimization framework to determine personalized CHW visits that maximize glycemic control at a community level. Our framework explicitly models the trade-off between screening new patients and providing management visits to individuals who are enrolled in treatment. We account for patients' motivational states, which affect their decisions to enroll or drop out of treatment and, therefore, the effectiveness of the intervention. By estimating patients' health and motivational states, our model builds visit plans accounting for patients' tradeoffs when deciding to enroll in treatment, leading to reduced dropout rates and improved resource allocation. We apply our approach to generate CHW visit plans using operational data from urban slums in India. We find that our approach can reduce fasting blood glucose by up to 25% with the same capacity as the best baseline method. Our experiments also demonstrate that our approach performs well with imperfect information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_06426 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Planning a Community Approach to Diabetes Care in Low- and Middle-Income Countries Using Optimization Adams, Katherine B. Boutilier, Justin J. Deo, Sarang Mintz, Yonatan Artificial Intelligence Systems and Control Optimization and Control Diabetes is a global health priority, especially in low- and-middle-income countries, where over 50% of premature deaths are attributed to high blood glucose. Community Health Worker (CHW) programs can provide affordable and culturally tailored solutions for early detection and management of diabetes. We introduce an optimization framework to determine personalized CHW visits that maximize glycemic control at a community level. Our framework explicitly models the trade-off between screening new patients and providing management visits to individuals who are enrolled in treatment. We account for patients' motivational states, which affect their decisions to enroll or drop out of treatment and, therefore, the effectiveness of the intervention. By estimating patients' health and motivational states, our model builds visit plans accounting for patients' tradeoffs when deciding to enroll in treatment, leading to reduced dropout rates and improved resource allocation. We apply our approach to generate CHW visit plans using operational data from urban slums in India. We find that our approach can reduce fasting blood glucose by up to 25% with the same capacity as the best baseline method. Our experiments also demonstrate that our approach performs well with imperfect information. |
| title | Planning a Community Approach to Diabetes Care in Low- and Middle-Income Countries Using Optimization |
| topic | Artificial Intelligence Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2305.06426 |