Julie J. Lee, MD, MPH, is a board-certified internal medicine physician and clinical informaticist at Stanford University. Dr. Lee's expertise in clinical informatics enables her to effectively implement informatics-driven approaches and clinically integrate AI models to improve patient health outcomes, alleviate physician burnout by streamlining workflows, and champion health equity.

Dr. Lee has been key to several initiatives in improving operational processes within Stanford. Her efforts range from advancing the governance and operations of Clinical Decision Support to the strategic integration of the Prescription Drug Monitoring Program into the electronic health record (EHR), thereby reducing clinician work burden in addressing the opioid crisis. Additionally, she has worked on innovative solutions to improve patient-physician communications--she created a dynamic EHR tool for better triage and processing by medical staff before reaching the doctors.

Health equity is her north star, informing Dr. Lee to dedicated engagement with historically underrepresented populations in medical research and collaborative partnerships between academia and community healthcare practitioners. Her previous role as an EpiScholar with the Los Angeles Department of Public Health involved researching the impact of language and acculturation on the Latino population's dietary habits and health behaviors, with a particular focus on diabetes. She has also worked with community health centers in east Los Angeles to bridge the translational gap between academic research and frontline healthcare workers, facilitating the transfer of cutting-edge liver disease research to those treating patients with substance abuse-related liver conditions. Of major clinical interest is cardiovascular disease—she has published several papers on impact of sex-specific risk factors for cardiovascular disease in women and transgender population.

Currently, as a part of her informatics approaches, Dr. Lee focuses health equity on leveraging patient data and AI/ML models to identify and mitigate health disparities, making certain they function as instruments of equity rather than increasing gaps. She is a member of Healthcare AI Applied Research Team (HEA3RT) with a focus on bringing code to bedside.

In the upcoming academic year, Dr. Lee will lead as health equity informaticist within the Primary Care Population Health division at Stanford.

Clinical Focus

  • Clinical Informatics
  • Health Equity
  • Artificial Intelligence
  • Addiction Medicine
  • Digital Health Equity
  • Fellow
  • Cardiovascular Diseases
  • Women's Health
  • Primary Care

Academic Appointments

Honors & Awards

  • Fellow-In-Training Research Abstract Award, American Society of Addiction Medicine (02/2024)
  • Quality Improvement Abstract Award, American College of Physicians, Northern California (10/2023)
  • Overall Abstract Award, Stanford Quality Improvement & Patient Safety Symposium (05/2023)
  • Future Physician Leader, High Value Physician Alliance (06/2021)
  • Graduation with Research Honors, University at Buffalo (06/2019)
  • Women's Health Clinical Research Abstract 1st Place Award, Academy of Women's Health (04/2017)
  • Greenway Scholarship, Yale University, School of Public Health (08/2011)
  • Clarence Darrow Leadership Award, Rotaract Club of Columbia University (06/2010)
  • Spingarn Named Scholarship, Columbia University (09/2007)
  • Milken Scholar, Milken Family Foundation (03/2007)

Boards, Advisory Committees, Professional Organizations

  • Member, Opioid Task Force (2023 - Present)
  • Member, Medical Informatics Director Weekly Meeting (2022 - Present)
  • Member, Clinical Decision Support Committee (2022 - Present)
  • Member, American Medical Informatics Association (2021 - Present)
  • Member, American College of Physicians (2019 - Present)

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 School of Public Health, Chronic Disease Epidemiology
  • BA, Columbia University, Psychology

All Publications

  • Clinical Decision Support in the Electronic Health Record: A Primer for Antimicrobial/Diagnostic Stewards and Infection Preventionists: Work Smarter so End Users Don’t Work Harder Smith, M. R., Lee, J. J., Chang, A., et al Antibiotic Stewardship & Healthcare Epidemiology. 2024
  • Reimagining Primary Care With AI: A Future Within Reach Shah, S., Lee, J. J., 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



    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


    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


    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 : . 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. W., Lee, J. J., et al 2017; 3 (4)

    View details for DOI 10.1177/2373379917690542

  • 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


    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

  • LA County Diabetes Health Brief. Los Angeles Department of Public Health and American Diabetes Association, Los Angeles County Health Survey 2010-2011 Lee, J., et al Los Angeles Department of Public Health. 2012
  • Artificial Physics, Swarm Engineering, and the Hamiltonian Method Proceedings of the World Congress on Engineering and Computer Science 2007 Sanza, K., Lee, J. J., Lee, J. 2007