Optimizing AI solutions for population health in primary care
Artificial intelligence (AI) has primarily enhanced individual primary care visits, yet its potential for population health management remains untapped. Effective AI should integrate longitudinal patient data, automate proactive outreach, and mitigate disparities by addressing barriers such as trans...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01864-z |
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| _version_ | 1849332470791536640 |
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| author | Sanjay Basu Pablo Bermudez-Canete Tannen Christopher Hall Pranav Rajpurkar |
| author_facet | Sanjay Basu Pablo Bermudez-Canete Tannen Christopher Hall Pranav Rajpurkar |
| author_sort | Sanjay Basu |
| collection | DOAJ |
| description | Artificial intelligence (AI) has primarily enhanced individual primary care visits, yet its potential for population health management remains untapped. Effective AI should integrate longitudinal patient data, automate proactive outreach, and mitigate disparities by addressing barriers such as transportation and language. Properly deployed, AI can significantly reduce administrative burden, facilitate early intervention, and improve equity in primary care, necessitating rigorous evaluation and adaptive design to realize sustained population-level benefits. |
| format | Article |
| id | doaj-art-4acd18aa286546d698bcb21fb0a9e863 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-4acd18aa286546d698bcb21fb0a9e8632025-08-20T03:46:12ZengNature Portfolionpj Digital Medicine2398-63522025-07-01811310.1038/s41746-025-01864-zOptimizing AI solutions for population health in primary careSanjay Basu0Pablo Bermudez-Canete1Tannen Christopher Hall2Pranav Rajpurkar3WaymarkStanford UniversityStanford UniversityHarvard Medical SchoolArtificial intelligence (AI) has primarily enhanced individual primary care visits, yet its potential for population health management remains untapped. Effective AI should integrate longitudinal patient data, automate proactive outreach, and mitigate disparities by addressing barriers such as transportation and language. Properly deployed, AI can significantly reduce administrative burden, facilitate early intervention, and improve equity in primary care, necessitating rigorous evaluation and adaptive design to realize sustained population-level benefits.https://doi.org/10.1038/s41746-025-01864-z |
| spellingShingle | Sanjay Basu Pablo Bermudez-Canete Tannen Christopher Hall Pranav Rajpurkar Optimizing AI solutions for population health in primary care npj Digital Medicine |
| title | Optimizing AI solutions for population health in primary care |
| title_full | Optimizing AI solutions for population health in primary care |
| title_fullStr | Optimizing AI solutions for population health in primary care |
| title_full_unstemmed | Optimizing AI solutions for population health in primary care |
| title_short | Optimizing AI solutions for population health in primary care |
| title_sort | optimizing ai solutions for population health in primary care |
| url | https://doi.org/10.1038/s41746-025-01864-z |
| work_keys_str_mv | AT sanjaybasu optimizingaisolutionsforpopulationhealthinprimarycare AT pablobermudezcanete optimizingaisolutionsforpopulationhealthinprimarycare AT tannenchristopherhall optimizingaisolutionsforpopulationhealthinprimarycare AT pranavrajpurkar optimizingaisolutionsforpopulationhealthinprimarycare |