Bio

Dr. Suffoletto is a primary care doctor at Stanford Internal Medicine Clinic. She is board certified in internal medicine.

For each patient, Dr. Suffoletto prepares a care plan. Her goal is to help every individual achieve the best possible health and quality of life. Her care plans are customized, comprehensive, and compassionate.

Patients praise Dr. Suffoletto‘s clinical skills and warm bedside manner. They value her ability to listen closely and communicate clearly.

Her expertise and empathy are fundamental to her leadership as the medical director of the Stanford Coordinated Care (SCC) program. This program uses an interdisciplinary, team-based approach to enhance care for moderate- and high-risk patients throughout the Stanford Health Care system.

Dr. Suffoletto also helps educate the internal medicine providers of the future. She is a clinical associate professor of primary care and population health in the Stanford Department of Medicine, Division of Primary Care.

To advance the field of internal medicine, she has published virtual patient cases, medical education curricula, and has given regional and national presentations on women’s health topics and medical education with a focus on simulation training.

Prior to joining Stanford, she held positions as associate chief of staff for education and innovative learning and medical director of simulation education in the VA Pittsburgh Healthcare System and chief of staff at Butler VA Healthcare System in Pennsylvania.

Clinical Focus

  • Internal Medicine

Academic Appointments

Professional Education

  • Board Certification: American Board of Internal Medicine, Internal Medicine (2007)
  • Residency: University of Pittsburgh Medical Center Internal Medicine (2008) PA
  • Medical Education: Loyola University Stritch School of Medicine (2004) IL

Contact

  • Academic
    jsuff@stanford.edu
    Department: Med/Primary Care and Population Health Position: Clinical Associate Professor
  • Clinical (Primary)  Stanford Internal Medicine 211 Quarry Rd Ste 305 MC 5987 Palo Alto, CA 94304 (650) 725-7078 (fax)

Additional Clinical Info

Additional Info

  • Mail Code: 5411

All Publications

  • Nephrology Leadership Through Collaboration to Deliver on CKD Care. American journal of kidney diseases : the official journal of the National Kidney Foundation Bahrainwala, J. Z., Suffoletto, J. L., Brady, B. M. 2026; 87 (3): 295-297

    View details for DOI 10.1053/j.ajkd.2026.01.002

    View details for PubMedID 41722932

  • Chronic kidney disease in older adults: challenges and opportunities for the primary care provider. BMC primary care Brady, B. M., Suffoletto, J. A., Sankary, R., Chertow, G. M. 2024; 25 (1): 388

    Abstract

    Kidney disease and its comorbidities disproportionately affect older persons. Kidney disease modifying therapy is underutilized in older adults, as guidelines lack consensus on approaching diagnosis and treatment in older adults. This review aims to highlight the challenges presented by, and opportunities for, identifying and treating CKD in older adults.

    View details for DOI 10.1186/s12875-024-02638-4

    View details for PubMedID 39487419

    View details for PubMedCentralID PMC11529074

  • Improving Emergency Department Visit Risk Prediction: Exploring the Operational Utility of Applied Patient Portal Messages. AMIA ... Annual Symposium proceedings. AMIA Symposium Kiani, H., Hassan, S., Genkins, J. Z., Bilir, J., Kadie, J., Le, T., Suffoletto, J., Chen, J. H. 2024; 2024: 610-619

    Abstract

    Patient portal messages represent a unique source of clinical data due to how they represent the voice of the patient, provide a glimpse into care delivery between episodic synchronous appointments, and capture variations in patient behavior and health literacy. There is little understanding of how to best apply modern natural language processing (NLP) approaches, such as large, pre-trained language models (LLMs), to patient messages. In this study, we aim to explore different approaches in incorporating patient messages into an existing Emergency Departments (ED) visit risk prediction model currently deployed at Stanford Health Care. With the addition of patient message frequencies to the baseline we were able to achieve an improved AUC of .77 and a jump in the F1 score. In future work, we aim to build upon these findings and further test combination models to incorporate features around patient message content, in addition to message frequencies.

    View details for PubMedID 40417468