Artificial intelligence tools in supporting healthcare professionals for tailored patient care
Abstract Artificial intelligence (AI) tools to support clinicians in providing patient-centered care can contribute to patient empowerment and care efficiency. We aimed to draft potential AI tools for tailored patient support corresponding to patients’ needs and assess clinicians’ perceptions about...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01604-3 |
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| author | Jiyeong Kim Michael L. Chen Shawheen J. Rezaei Tina Hernandez-Boussard Jonathan H. Chen Fatima Rodriguez Summer S. Han Rayhan A. Lal Sun H. Kim Chrysoula Dosiou Susan M. Seav Tugce Akcan Carolyn I. Rodriguez Steven M. Asch Eleni Linos |
| author_facet | Jiyeong Kim Michael L. Chen Shawheen J. Rezaei Tina Hernandez-Boussard Jonathan H. Chen Fatima Rodriguez Summer S. Han Rayhan A. Lal Sun H. Kim Chrysoula Dosiou Susan M. Seav Tugce Akcan Carolyn I. Rodriguez Steven M. Asch Eleni Linos |
| author_sort | Jiyeong Kim |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI) tools to support clinicians in providing patient-centered care can contribute to patient empowerment and care efficiency. We aimed to draft potential AI tools for tailored patient support corresponding to patients’ needs and assess clinicians’ perceptions about the usefulness of those AI tools. To define patients’ issues, we analyzed 528,199 patient messages of 11,123 patients with diabetes by harnessing natural language processing and AI. Applying multiple prompt-engineering techniques, we drafted a series of AI tools, and five endocrinologists evaluated them for perceived usefulness and risk. Patient education and administrative support for timely and streamlined interaction were perceived as highly useful, yet deeper integration of AI tools into patient data was perceived as risky. This study proposes assorted AI applications as clinical assistance tailored to patients’ needs substantiated by clinicians’ evaluations. Findings could offer essential ramifications for developing potential AI tools for precision patient care for diabetes and beyond. |
| format | Article |
| id | doaj-art-90b0aa2314ef42d8aeefecb19ba01068 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-90b0aa2314ef42d8aeefecb19ba010682025-08-20T03:18:28ZengNature Portfolionpj Digital Medicine2398-63522025-04-018111010.1038/s41746-025-01604-3Artificial intelligence tools in supporting healthcare professionals for tailored patient careJiyeong Kim0Michael L. Chen1Shawheen J. Rezaei2Tina Hernandez-Boussard3Jonathan H. Chen4Fatima Rodriguez5Summer S. Han6Rayhan A. Lal7Sun H. Kim8Chrysoula Dosiou9Susan M. Seav10Tugce Akcan11Carolyn I. Rodriguez12Steven M. Asch13Eleni Linos14Stanford Center for Digital Health, Department of Medicine, Stanford UniversityStanford Center for Digital Health, Department of Medicine, Stanford UniversityStanford Center for Digital Health, Department of Medicine, Stanford UniversityDepartment of Biomedical Data Science, School of Medicine, Stanford UniversityStanford Center for Biomedical Informatics Research, School of Medicine, Stanford UniversityDivision of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford UniversityStanford Center for Biomedical Informatics Research, School of Medicine, Stanford UniversityDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford UniversityDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford UniversityDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford UniversityDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford UniversityDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford UniversityDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Stanford UniversityDivision of Primary Care and Population Health, Department of Medicine, Stanford UniversityStanford Center for Digital Health, Department of Medicine, Stanford UniversityAbstract Artificial intelligence (AI) tools to support clinicians in providing patient-centered care can contribute to patient empowerment and care efficiency. We aimed to draft potential AI tools for tailored patient support corresponding to patients’ needs and assess clinicians’ perceptions about the usefulness of those AI tools. To define patients’ issues, we analyzed 528,199 patient messages of 11,123 patients with diabetes by harnessing natural language processing and AI. Applying multiple prompt-engineering techniques, we drafted a series of AI tools, and five endocrinologists evaluated them for perceived usefulness and risk. Patient education and administrative support for timely and streamlined interaction were perceived as highly useful, yet deeper integration of AI tools into patient data was perceived as risky. This study proposes assorted AI applications as clinical assistance tailored to patients’ needs substantiated by clinicians’ evaluations. Findings could offer essential ramifications for developing potential AI tools for precision patient care for diabetes and beyond.https://doi.org/10.1038/s41746-025-01604-3 |
| spellingShingle | Jiyeong Kim Michael L. Chen Shawheen J. Rezaei Tina Hernandez-Boussard Jonathan H. Chen Fatima Rodriguez Summer S. Han Rayhan A. Lal Sun H. Kim Chrysoula Dosiou Susan M. Seav Tugce Akcan Carolyn I. Rodriguez Steven M. Asch Eleni Linos Artificial intelligence tools in supporting healthcare professionals for tailored patient care npj Digital Medicine |
| title | Artificial intelligence tools in supporting healthcare professionals for tailored patient care |
| title_full | Artificial intelligence tools in supporting healthcare professionals for tailored patient care |
| title_fullStr | Artificial intelligence tools in supporting healthcare professionals for tailored patient care |
| title_full_unstemmed | Artificial intelligence tools in supporting healthcare professionals for tailored patient care |
| title_short | Artificial intelligence tools in supporting healthcare professionals for tailored patient care |
| title_sort | artificial intelligence tools in supporting healthcare professionals for tailored patient care |
| url | https://doi.org/10.1038/s41746-025-01604-3 |
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