The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians
Background: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to address clinical questions. However, its effectiv...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Journal of Primary Care & Community Health |
| Online Access: | https://doi.org/10.1177/21501319251332215 |
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| author | Ryan T. Hurt Christopher R. Stephenson Elizabeth A. Gilman Christopher A. Aakre Ivana T. Croghan Manpreet S. Mundi Karthik Ghosh Jithinraj Edakkanambeth Varayil |
| author_facet | Ryan T. Hurt Christopher R. Stephenson Elizabeth A. Gilman Christopher A. Aakre Ivana T. Croghan Manpreet S. Mundi Karthik Ghosh Jithinraj Edakkanambeth Varayil |
| author_sort | Ryan T. Hurt |
| collection | DOAJ |
| description | Background: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to address clinical questions. However, its effectiveness in real-world primary care cases remains unknown. Objective: To evaluate the performance of OE in providing EBM recommendations for five common chronic conditions in primary care: hypertension, hyperlipidemia, diabetes mellitus type 2, depression, and obesity. Methods: Five patient cases were retrospectively analyzed. Physicians posed specific clinical questions, and OE responses were evaluated on clarity, relevance, evidence support, impact on CDM, and overall satisfaction. Four independent physicians provided ratings using a 0 to 4 scale. Results: OE provided accurate, evidence-based recommendations in all cases, aligning with physician plans. OE was scored on a scale of zero to four, where zero was very unclear, and four was very clear. Mean scores across cases were clarity (3.55 ± 0.60), relevance (3.75 ± 0.44), support (3.35 ± 0.49), and satisfaction (3.60 ± 0.60). However, the impact on CDM was limited (1.95 ± 1.05), as OE primarily reinforced rather than modified plans. Conclusion: OE was rated high in clarity, relevance, and evidence-based support, reinforcing physician decisions in common chronic conditions. While the impact on CDM was minimal due to the study’s retrospective nature, OE shows promise in augmenting the primary care physician. Prospective trials are needed to evaluate its utility in complex cases and multidisciplinary settings. |
| format | Article |
| id | doaj-art-5e46e33fc2774c1cb6620e5390e4d2b4 |
| institution | OA Journals |
| issn | 2150-1327 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Primary Care & Community Health |
| spelling | doaj-art-5e46e33fc2774c1cb6620e5390e4d2b42025-08-20T02:19:47ZengSAGE PublishingJournal of Primary Care & Community Health2150-13272025-04-011610.1177/21501319251332215The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care PhysiciansRyan T. Hurt0Christopher R. Stephenson1Elizabeth A. Gilman2Christopher A. Aakre3Ivana T. Croghan4Manpreet S. Mundi5Karthik Ghosh6Jithinraj Edakkanambeth Varayil7Mayo Clinic, Rochester, MN, USAMayo Clinic, Rochester, MN, USAMayo Clinic, Rochester, MN, USAMayo Clinic, Rochester, MN, USAMayo Clinic, Rochester, MN, USAMayo Clinic, Rochester, MN, USAMayo Clinic, Rochester, MN, USAMayo Clinic, Rochester, MN, USABackground: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to address clinical questions. However, its effectiveness in real-world primary care cases remains unknown. Objective: To evaluate the performance of OE in providing EBM recommendations for five common chronic conditions in primary care: hypertension, hyperlipidemia, diabetes mellitus type 2, depression, and obesity. Methods: Five patient cases were retrospectively analyzed. Physicians posed specific clinical questions, and OE responses were evaluated on clarity, relevance, evidence support, impact on CDM, and overall satisfaction. Four independent physicians provided ratings using a 0 to 4 scale. Results: OE provided accurate, evidence-based recommendations in all cases, aligning with physician plans. OE was scored on a scale of zero to four, where zero was very unclear, and four was very clear. Mean scores across cases were clarity (3.55 ± 0.60), relevance (3.75 ± 0.44), support (3.35 ± 0.49), and satisfaction (3.60 ± 0.60). However, the impact on CDM was limited (1.95 ± 1.05), as OE primarily reinforced rather than modified plans. Conclusion: OE was rated high in clarity, relevance, and evidence-based support, reinforcing physician decisions in common chronic conditions. While the impact on CDM was minimal due to the study’s retrospective nature, OE shows promise in augmenting the primary care physician. Prospective trials are needed to evaluate its utility in complex cases and multidisciplinary settings.https://doi.org/10.1177/21501319251332215 |
| spellingShingle | Ryan T. Hurt Christopher R. Stephenson Elizabeth A. Gilman Christopher A. Aakre Ivana T. Croghan Manpreet S. Mundi Karthik Ghosh Jithinraj Edakkanambeth Varayil The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians Journal of Primary Care & Community Health |
| title | The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians |
| title_full | The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians |
| title_fullStr | The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians |
| title_full_unstemmed | The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians |
| title_short | The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians |
| title_sort | use of an artificial intelligence platform openevidence to augment clinical decision making for primary care physicians |
| url | https://doi.org/10.1177/21501319251332215 |
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