Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909258999660544 |
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| author | Dasgupta, Arpan Boehmer, Niclas Madhiwalla, Neha Hedge, Aparna Wilder, Bryan Tambe, Milind Taneja, Aparna |
| author_facet | Dasgupta, Arpan Boehmer, Niclas Madhiwalla, Neha Hedge, Aparna Wilder, Bryan Tambe, Milind Taneja, Aparna |
| contents | Automated voice calls are an effective method of delivering maternal and child health information to mothers in underserved communities. One method to fight dwindling listenership is through an intervention in which health workers make live service calls. Previous work has shown that we can use AI to identify beneficiaries whose listenership gets the greatest boost from an intervention. It has also been demonstrated that listening to the automated voice calls consistently leads to improved health outcomes for the beneficiaries of the program. These two observations combined suggest the positive effect of AI-based intervention scheduling on behavioral and health outcomes. This study analyzes the relationship between the two. Specifically, we are interested in mothers' health knowledge in the post-natal period, measured through survey questions. We present evidence that improved listenership through AI-scheduled interventions leads to a better understanding of key health issues during pregnancy and infancy. This improved understanding has the potential to benefit the health outcomes of mothers and their babies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11973 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs Dasgupta, Arpan Boehmer, Niclas Madhiwalla, Neha Hedge, Aparna Wilder, Bryan Tambe, Milind Taneja, Aparna Human-Computer Interaction Artificial Intelligence Computers and Society Automated voice calls are an effective method of delivering maternal and child health information to mothers in underserved communities. One method to fight dwindling listenership is through an intervention in which health workers make live service calls. Previous work has shown that we can use AI to identify beneficiaries whose listenership gets the greatest boost from an intervention. It has also been demonstrated that listening to the automated voice calls consistently leads to improved health outcomes for the beneficiaries of the program. These two observations combined suggest the positive effect of AI-based intervention scheduling on behavioral and health outcomes. This study analyzes the relationship between the two. Specifically, we are interested in mothers' health knowledge in the post-natal period, measured through survey questions. We present evidence that improved listenership through AI-scheduled interventions leads to a better understanding of key health issues during pregnancy and infancy. This improved understanding has the potential to benefit the health outcomes of mothers and their babies. |
| title | Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2407.11973 |