The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912128357629952 |
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| author | Periáñez, África del Río, Ana Fernández Nazarov, Ivan Jané, Enric Hassan, Moiz Rastogi, Aditya Tang, Dexian |
| author_facet | Periáñez, África del Río, Ana Fernández Nazarov, Ivan Jané, Enric Hassan, Moiz Rastogi, Aditya Tang, Dexian |
| contents | Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16098 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems Periáñez, África del Río, Ana Fernández Nazarov, Ivan Jané, Enric Hassan, Moiz Rastogi, Aditya Tang, Dexian Machine Learning Artificial Intelligence Computers and Society Human-Computer Interaction Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue. |
| title | The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems |
| topic | Machine Learning Artificial Intelligence Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.16098 |