. 2025 Apr 16;8(1):210.

doi: 10.1038/s41746-025-01604-3.

Artificial intelligence tools in supporting healthcare professionals for tailored patient care

Jiyeong Kim¹, Michael L Chen², Shawheen J Rezaei², Tina Hernandez-Boussard^{3

4}, Jonathan H Chen⁴, Fatima Rodriguez⁵, Summer S Han⁴, Rayhan A Lal⁶, Sun H Kim⁶, Chrysoula Dosiou⁶, Susan M Seav⁶, Tugce Akcan⁶, Carolyn I Rodriguez^{7

8}, Steven M Asch⁹, Eleni Linos²

Affiliations

¹ Stanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA. jykim3@stanford.edu.
² Stanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA.
³ Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
⁴ Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Palo Alto, CA, USA.
⁵ Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Stanford, CA, USA.
⁶ Division of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University, Stanford, CA, USA.
⁷ Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Palo Alto, CA, USA.
⁸ Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
⁹ Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA.

PMID: 40240489
PMCID: PMC12003912
DOI: 10.1038/s41746-025-01604-3

Artificial intelligence tools in supporting healthcare professionals for tailored patient care

Jiyeong Kim et al. NPJ Digit Med. 2025.

. 2025 Apr 16;8(1):210.

doi: 10.1038/s41746-025-01604-3.

Authors

Affiliations

¹ Stanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA. jykim3@stanford.edu.
² Stanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA.
³ Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
⁴ Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Palo Alto, CA, USA.
⁵ Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Stanford, CA, USA.
⁶ Division of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University, Stanford, CA, USA.
⁷ Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Palo Alto, CA, USA.
⁸ Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
⁹ Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA.

PMID: 40240489
PMCID: PMC12003912
DOI: 10.1038/s41746-025-01604-3

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.

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Conflict of interest statement

Competing interests: In the last 3 years, CIR has served as a consultant for Biohaven Pharmaceuticals, Osmind, and Biogen; and receives research grant support from Biohaven Pharmaceuticals, a stipend from American Psychiatric Association Publishing for her role as Deputy Editor at The American Journal of Psychiatry, and book royalties from American Psychiatric Association Publishing. The other authors declare no competing interests.

Figures

**Fig. 1. Data source and study design.**
a Individuals with diabetes mellitus: Determined by ICD-10 codes: E08 (underlying DM), E09 (induced DM), E10 (Type 1 DM), E11 (Type 2 DM), E13 (Secondary DM); b Number of unique message threads.

**Fig. 2. Low-dimensional representation of patient messages from individuals with diabetes mellitus, 2013–2024.**
12,000 messages were randomly selected for each diagnosis (type II and type I DM) for visualization. Message topics: Scheduling/canceling, Pharmacy/refill, Medication, Symptom (Pain/fell), Thyroid/hormone, Lab/test order, Treatment/surgery, Mental Health/sleep, Insurance/coverage, Device/Sensor/supplies, Meal/diet.

formula image — **Fig. 2. Low-dimensional representation of patient messages from individuals with diabetes mellitus, 2013–2024.**
12,000 messages were randomly selected for each diagnosis (type II and type I DM) for visualization. Message topics: Scheduling/canceling, Pharmacy/refill, Medication, Symptom (Pain/fell), Thyroid/hormone, Lab/test order, Treatment/surgery, Mental Health/sleep, Insurance/coverage, Device/Sensor/supplies, Meal/diet.

**Fig. 3. Rate^a comparisons of sending messages on specific topics by demographic characteristics.**
a Rate_A = The number of patients in subgroup A who had messages on the topic X /Total number of patients in subgroup A; a Race: non-White includes Asians, Blacks, Native Americans, Other, and Pacific Islanders; b Ethnicity (Hispanic vs. non-Hispanic); c Sex (Female vs. Male); d Marital status: Unmarried includes divorced, life partner, other, separated, single, and widowed; * When the rate difference is significant (p < 0.05).

See this image and copyright information in PMC

References

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Artificial intelligence tools in supporting healthcare professionals for tailored patient care

Affiliations

Artificial intelligence tools in supporting healthcare professionals for tailored patient care

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

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