Bio

Dr. Julie J. Lee is a board-certified internal medicine physician, epidemiologist, and clinical informaticist at Stanford University. She works at the forefront of responsible technology and artificial intelligence (AI) integration in healthcare—spanning research, operations, and real-world clinical use. With degrees in Psychology from Columbia University and Epidemiology from Yale University, Dr. Lee brings a unique perspective as an end-user clinician, public health researcher, and systems thinker with deep technical fluency.

At the Stanford Division of Primary Care and Population Health, she serves as Clinical Assistant Professor and Health Equity Informaticist, leading data-informed strategies to close care gaps and implement technology that works in real clinical environments—particularly in primary care settings where systemic challenges around access, coordination, and equity are most visible. Her informatics work encompasses implementation research, governance and operations of clinical decision support (CDS), integration of Prescription Drug Monitoring Programs (PDMPs), deployment of continuous glucose monitors (CGMs) in inpatient settings, and human factors research to improve health IT usability and physician-patient communication.

Dr. Lee’s expertise spans interoperability, EHR physician-builder capabilities, and human-centered design—applying design thinking, data science, and implementation science to drive equitable, clinically grounded innovations. Her focus on clinical feasibility ensures AI tools and digital health interventions are scalable, operationally feasible, and aligned with the real needs of patients and frontline care teams. She advises industry and innovators on designing health technologies that bridge the gap between innovation and implementation.

Health equity is Dr. Lee’s north star, guiding her work in both academic and community settings. Her decade of research spans cardiometabolic health, diabetes, applied AI, and patient safety, with a consistent focus on underserved populations. She has led projects on language and acculturation in Latino communities, translated liver disease research into frontline care in East Los Angeles, and contributed to foundational studies on sex-specific cardiovascular risk factors in women and transgender populations. She is currently focused on advancing precision health for Asian and Native Hawaiian and Pacific Islander (NHPI) communities, particularly in the realm of obesity medicine.

Dr. Lee is widely published in journals such as Diabetes Care, JAMA Network Open, NPJ Digital Medicine, Applied Clinical Informatics, Journal of the American Heart Association, and Menopause. Her informatics philosophy centers on translating research into practice—bringing high-quality evidence directly to clinicians in ways that are actionable, equitable, and embedded into the EHR workflow.

Academic Appointments

Honors & Awards

  • Physician Wellness Poster Finalist, American College of Physicians (04/2024)
  • Center of Digital Health Student Grant, Stanford Center of Digital Health (04/2024)
  • Fellow Abstract Award, American Society of Addiction Medicine (02/2024)
  • ODME Conference Scholarship, Stanford School of Medicine Office of Diversity Equity and Inclusion (02/2024)
  • Fellowship Research Scholarship, Stanford Pediatrics Fellowship (11/2023)
  • Quality Improvement Abstract Award, American College of Physicians (10/2023)
  • Overall Innovation Award, Stanford Quality Improvement & Patient Safety Symposium (05/2023)
  • Future Physician Leader, High Value Practice Academic Alliance (02/2021)
  • Research Honors, University at Buffalo (05/2019)
  • Clinical Research First Place Award, Academy of Women's Health (04/2017)
  • Clarence Darrow Leadership Award, Rotaract Club of Columbia University (10/2010)
  • Milken Scholar, Milken Family Foundation - Milken Institute (05/2007)

Professional Education

  • Fellowship, Stanford University, Clinical Informatics
  • Residency, University of California, Riverside, Internal Medicine
  • Internship, University of California, Riverside, Internal Medicine
  • MD, University at Buffalo, Medicine
  • MPH, Yale University, Epidemiology
  • BA, Columbia University, Psychology

Contact

  • Academic
    juliejl@stanford.edu
    University - Academic staff Department: Med/Primary Care and Population Health Position: Clinical Assistant Professor

Additional Info

  • Mail Code: 5411
  • Profile:
    Clinical Informatics fellow (Internal Medicine trained), broadly interested in projects to improve physician wellness.
  • ORCID:
    https://orcid.org/0009-0002-6640-8597

Links

Graduate and Fellowship Programs

All Publications

  • Red teaming ChatGPT in medicine to yield real-world insights on model behavior. NPJ digital medicine Chang, C. T., Farah, H., Gui, H., Rezaei, S. J., Bou-Khalil, C., Park, Y. J., Swaminathan, A., Omiye, J. A., Kolluri, A., Chaurasia, A., Lozano, A., Heiman, A., Jia, A. S., Kaushal, A., Jia, A., Iacovelli, A., Yang, A., Salles, A., Singhal, A., Narasimhan, B., Belai, B., Jacobson, B. H., Li, B., Poe, C. H., Sanghera, C., Zheng, C., Messer, C., Kettud, D. V., Pandya, D., Kaur, D., Hla, D., Dindoust, D., Moehrle, D., Ross, D., Chou, E., Lin, E., Haredasht, F. N., Cheng, G., Gao, I., Chang, J., Silberg, J., Fries, J. A., Xu, J., Jamison, J., Tamaresis, J. S., Chen, J. H., Lazaro, J., Banda, J. M., Lee, J. J., Matthys, K. E., Steffner, K. R., Tian, L., Pegolotti, L., Srinivasan, M., Manimaran, M., Schwede, M., Zhang, M., Nguyen, M., Fathzadeh, M., Zhao, Q., Bajra, R., Khurana, R., Azam, R., Bartlett, R., Truong, S. T., Fleming, S. L., Raj, S., Behr, S., Onyeka, S., Muppidi, S., Bandali, T., Eulalio, T. Y., Chen, W., Zhou, X., Ding, Y., Cui, Y., Tan, Y., Liu, Y., Shah, N., Daneshjou, R. 2025; 8 (1): 149

    Abstract

    Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy of large language models, but non-model creator-affiliated red teaming is scant in healthcare. We convened teams of clinicians, medical and engineering students, and technical professionals (80 participants total) to stress-test models with real-world clinical cases and categorize inappropriate responses along axes of safety, privacy, hallucinations/accuracy, and bias. Six medically-trained reviewers re-analyzed prompt-response pairs and added qualitative annotations. Of 376 unique prompts (1504 responses), 20.1% were inappropriate (GPT-3.5: 25.8%; GPT-4.0: 16%; GPT-4.0 with Internet: 17.8%). Subsequently, we show the utility of our benchmark by testing GPT-4o, a model released after our event (20.4% inappropriate). 21.5% of responses appropriate with GPT-3.5 were inappropriate in updated models. We share insights for constructing red teaming prompts, and present our benchmark for iterative model assessments.

    View details for DOI 10.1038/s41746-025-01542-0

    View details for PubMedID 40055532

    View details for PubMedCentralID 10564921

  • Clinical decision support in the electronic health record: a primer for antimicrobial stewards and infection preventionists: work smarter so end users don't work harder. Antimicrobial stewardship & healthcare epidemiology : ASHE Smith, M. R., Lee, J. J., Holubar, M., Salinas, J. L., Sampson, M. M., Medford, R. J., Chang, A. 2024; 4 (1): e204

    Abstract

    Computerized clinical decision support (CDS) assists healthcare professionals in making decisions to improve patient care. In the realms of antimicrobial stewardship (ASP) and infection prevention (IP) programs, CDS interventions can play a crucial role in optimizing antibiotic prescribing practices, reducing healthcare-associated infections, and promoting diagnostic stewardship when optimally designed. This primer article aims to provide ASP and IP professionals with a practical framework for the development, design, and evaluation of CDS interventions.Large academic medical center design: Established frameworks of CDS evaluation, "Five Rights" of CDS and the "Ten Commandments of Effective Clinical Decision Support", were applied to two real-world examples of CDS tools, a Vancomycin Best Practice Advisory and a Clostridioides Difficile order panel, to demonstrate a structured approach to developing and enhancing the functionality of ASP/IP CDS interventions to promote efficacy and reduce unintended consequences of CDS.By outlining a structured approach for the development and evaluation of CDS interventions, with focus on end user engagement, efficiency and feasibility, ASP and IP professionals can leverage CDS to enhance IP/ASP quality improvement initiatives aimed to improve antibiotic utilization, diagnostic stewardship, and adherence to IP protocols.

    View details for DOI 10.1017/ash.2024.448

    View details for PubMedID 39563933

    View details for PubMedCentralID PMC11574583

  • Perspectives on Artificial Intelligence-Generated Responses to Patient Messages. JAMA network open Kim, J., Chen, M. L., Rezaei, S. J., Liang, A. S., Seav, S. M., Onyeka, S., Lee, J. J., Vedak, S. C., Mui, D., Lal, R. A., Pfeffer, M. A., Sharp, C., Pageler, N. M., Asch, S. M., Linos, E. 2024; 7 (10): e2438535

    View details for DOI 10.1001/jamanetworkopen.2024.38535

    View details for PubMedID 39412810

  • Empowering Hospitalized Patients With Diabetes: Implementation of a Hospital-wide CGM Policy With EHR-Integrated Validation for Dosing Insulin. Diabetes care Lee, M. Y., Seav, S. M., Ongwela, L., Lee, J. J., Aubyrn, R., Cao, F. Y., Kalinsky, A., Aparicio Ramos, O., Gu, Y., Kingston, K., Ivanovic, M., Buckingham, B. A., Desai, D., Lal, R. A., Tan, M., Basina, M., Hughes, M. S. 2024

    Abstract

    We aimed to assess the feasibility, clinical accuracy, and acceptance of a hospital-wide continuous glucose monitoring (CGM) policy with electronic health record (EHR)-integrated validation for insulin dosing.A hospital policy was developed and implemented at Stanford Health Care for using personal CGMs in lieu of fingerstick blood glucose (FSBG) monitoring. It included requirements specific to each CGM, accuracy monitoring protocols, and EHR integration. User experience surveys were conducted among a subset of patients and nurses.From November 2022 to August 2023, 135 patients used the CGM protocol in 185 inpatient encounters. This included 27% with type 1 diabetes and 24% with automated insulin delivery systems. The most-used CGMs were Dexcom G6 (44%) and FreeStyle Libre 2 (43%). Of 1,506 CGM validation attempts, 87.8% met the %20/20 criterion for CGM-based insulin dosing and 99.3% fell within Clarke zones A or B. User experience surveys were completed by 27 nurses and 46 patients. Most nurses found glucose management under the protocol effective (74%), easy to use (67%), and efficient (63%); 80% of nurses preferred inpatient CGM to FSBG. Most patients liked the CGM protocol (63%), reported positive CGM interactions with nursing staff (63%), and felt no significant interruptions to their diabetes management (63%).Implementation of a hospital-wide inpatient CGM policy supporting multiple CGM types with real-time accuracy monitoring and integration into the EHR is feasible. Initial feedback from nurses and patients was favorable, and further investigation toward broader use and sustainability is needed.

    View details for DOI 10.2337/dc24-0626

    View details for PubMedID 39140891

  • Implementation of an EHR-Integrated Hospital-Wide CGM Protocol for Insulin Dosing Seav, S. M., Lee, M., Ongwela, L., Aubyrn, R., Cao, F., Lee, J. H., Kalinsky, A. F., Ramos, O., Gu, Y. M., Ivanovic, M. M., Buckingham, B. A., Desai, D., Lal, R., Tan, M., Basina, M., Hughes, M. S. AMER DIABETES ASSOC. 2024

    View details for DOI 10.2337/db24-38-OR

    View details for Web of Science ID 001301361100073

  • Reimagining Primary Care With AI: A Future Within Reach Shah, S., et al Practice UPdate. 2024
  • Paging the Clinical Informatics Community: Respond STAT to Dobbs v Jackson's Women's Health Organization. Applied clinical informatics Arvisais-Anhalt, S., Ravi, A., Weia, B., Aarts, J., Ahmad, H. B., Araj, E., Bauml, J. A., Benham-Hutchins, M., Boyd, A. D., Brecht-Doscher, A., Butler-Henderson, K., Butte, A., Cardillo, A. B., Chilukuri, N., Cho, M. K., Cohen, J. K., Craven, C. K., Crusco, S. J., Dadabhoy, F., Dash, D., DeBolt, C., Elkin, P. L., Fayanju, O. A., Fochtmann, L., Graham, J. V., Hanna, J., Hersh, W., Hoffard, M. R., Hron, J., Huang, S. S., Jackson, B. R., Kaplan, B., Kelly, W., Ko, K., Koppel, R., Kurapati, N., Labbad, G., Lee, J., Lehmann, C. U., Leitner, S., Liao, Z. C., Medford, R. J., Melnick, E. R., Muniyappa, A. N., Murray, S., Neinstein, A., Nichols-Johnson, V., Novak, L., Ogan, W. S., Ozeran, L., Pageler, N., Pandita, D., Perumbeti, A., Petersen, C., Pierce, L., Puttagunta, R., Ramaswamy, P., Rogers, K. M., Rosenbloom, T., Ryan, A., Saleh, S., Sarabu, C., Schreiber, R., Shaw, K. A., Sim, I., Sirintrapun, S. J., Solomonides, A., Spector, J. D., Starren, J. B., Stoffel, M., Subbian, V., Swanson, K., Tomes, A., Trang, K., Unertl, K. M., Weon, J. L., Whooley, M., Wiley, K., Williamson, D. F., Winkelstein, P., Wong, J., Xie, J., Yarahuan, J. K., Yung, N., Zera, C., Ratanawongsa, N., Sadasivaiah, S. 2022

    Abstract

    n/a.

    View details for DOI 10.1055/a-2000-7590

    View details for PubMedID 36535703

  • After menopause, is an enlarging middle, an enlarging cardiovascular risk factor? Menopause (New York, N.Y.) Lee, J. J., Shufelt, C. L. 2020; 27 (9): 974-975

    View details for DOI 10.1097/GME.0000000000001620

    View details for PubMedID 32852448

  • Cardiovascular implications of gender-affirming hormone treatment in the transgender population. Maturitas Dutra, E., Lee, J., Torbati, T., Garcia, M., Merz, C. N., Shufelt, C. 2019; 129: 45-49

    Abstract

    Transgender men and women represent a growing population in the United States and Europe, with 0.5% of adults and 3% of youth identifying as transgender. Globally, an estimated 0.3-0.5% of the population identify as transgender. Despite the increasing percentage of individuals whose gender identity, gender expression and behavior differ from their assigned sex at birth, health outcomes in transgenders have been understudied. Many transgender people seek treatment with cross-sex hormone therapy starting from a young age and frequently at high doses in order to obtain the secondary sex characteristics of the desired gender. There is a need to understand the potential long-term health consequences of cross-sex hormone therapy, given that cardiovascular disease is the leading disease-specific cause of death in this population. This review discusses the cardiovascular risks of gender-affirming hormone treatments with respect to transgender women and transgender men.

    View details for DOI 10.1016/j.maturitas.2019.08.010

    View details for PubMedID 31547912

    View details for PubMedCentralID PMC6761990

  • Age at Menarche and Risk of Cardiovascular Disease Outcomes: Findings From the National Heart Lung and Blood Institute-Sponsored Women's Ischemia Syndrome Evaluation. Journal of the American Heart Association Lee, J. J., Cook-Wiens, G., Johnson, B. D., Braunstein, G. D., Berga, S. L., Stanczyk, F. Z., Pepine, C. J., Bairey Merz, C. N., Shufelt, C. L. 2019; 8 (12): e012406

    Abstract

    Background Previous studies have reported an association between the timing of menarche and cardiovascular disease ( CVD ). However, emerging studies have not examined the timing of menarche in relation to role of estrogen over a lifetime and major adverse cardiac events ( MACE ). Methods and Results A total of 648 women without surgical menopause undergoing coronary angiography for suspected ischemia in the WISE (Women's Ischemia Syndrome Evaluation) study were evaluated at baseline and followed for 6 years (median) to assess major adverse CVD outcomes. MACE was defined as the first occurrence of all-cause death, nonfatal myocardial infarction, nonfatal stroke, or heart failure hospitalization. Age at menarche was self-reported and categorized (≤10, 11, 12, 13, 14, ≥15 years) with age 12 as reference. Total estrogen time and supra-total estrogen time were calculated. Cox regression analysis was performed adjusting for CVD risk factors. Baseline age was 57.9 ± 12 years (mean ± SD ), body mass index was 29.5 ± 6.5 kg/m2, total estrogen time was 32.2 ± 8.9 years, and supra-total estrogen time was 41.4 ± 8.8 years. MACE occurred in 172 (27%), and its adjusted regression model was J-shaped. Compared with women with menarche at age 12 years, the adjusted MACE hazard ratio for menarche at ≤10 years was 4.53 (95% CI 2.13-9.63); and at ≥15 years risk for MACE was 2.58 (95% CI , 1.28-5.21). Conclusions History of early or late menarche was associated with a higher risk for adverse CVD outcomes. These findings highlight age at menarche as a potential screening tool for women at risk of adverse CVD events. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT00000554.

    View details for DOI 10.1161/JAHA.119.012406

    View details for PubMedID 31165670

    View details for PubMedCentralID PMC6645646

  • Lagged Versus Difference Score Regression: An Example From a Community-Based Educational Seminar Evaluation Pedagogy in Health Promotion Valente, T., Lee, J., et al 2017
  • Clinical characteristics associated with Spitz nevi and Spitzoid malignant melanomas: the Yale University Spitzoid Neoplasm Repository experience, 1991 to 2008. Journal of the American Academy of Dermatology Lott, J. P., Wititsuwannakul, J., Lee, J. J., Ariyan, S., Narayan, D., Kluger, H. H., Lazova, R. 2014; 71 (6): 1077-82

    Abstract

    Spitz nevi and Spitzoid malignant melanomas are uncommon and may be difficult to distinguish histopathologically. Identification of clinical features associated with these lesions may aid in diagnosis.We sought to identify clinical characteristics associated with Spitz nevi and Spitzoid malignant melanomas.We conducted a retrospective cohort study of Spitz nevi and Spitzoid malignant melanomas from the Yale University Spitzoid Neoplasm Repository diagnosed from years 1991 through 2008. Descriptive statistics and multivariate logistic regression were used to compare select patient- and tumor-level factors associated with each lesion.Our cohort included 484 Spitz nevi and 54 Spitzoid malignant melanomas. Spitz nevi were more common (P = .03) in females (65%; n = 316) compared with Spitzoid malignant melanomas (50%; n = 27), occurred more frequently in younger patients (mean age at diagnosis 22 vs 55 years; P < .001), and more likely presented as smaller lesions (diameter 7.6 vs 10.5 mm; P < .001). Increasing age (odds ratio 1.16, 95% CI [1.09, 1.14]; P< .001) and male gender (odds ratio 2.77, 95% CI [1.17, 6.55]; P< .02) predicted Spitzoid malignant melanoma diagnosis.Small sample size, unmeasured confounding, and restriction to a single institution may limit the accuracy and generalizability of our findings.Age and gender help predict diagnosis of Spitz nevi and Spitzoid malignant melanomas.

    View details for DOI 10.1016/j.jaad.2014.08.026

    View details for PubMedID 25308882

    View details for PubMedCentralID PMC6133655

  • The Association Of Acculturation And Poor Nutritional Behavior Among Latinos In The Los Angeles County Health Survey 2007 Lee, J. J. Public Theses. 2013
  • Artificial Physics, Swarm Engineering, and the Hamiltonian Method World Congress on Engineering and Computer Science Kazadi, S., Lee, J. J., Lee, J. 2007
https://orcid.org/0009-0002-6640-8597