Bio

Dr. Tsai is a board-certified family medicine physician, clinical informaticist, and trained in osteopathy. He is a clinical assistant professor in the Stanford University School of Medicine Department of Medicine – Primary Care and Population Health. Prior to joining Stanford Health Care, he obtained a Master of Management in clinical informatics from Duke University.

Dr. Tsai seeks to improve clinician workflows and patient care by applying his knowledge of clinical informatics. His innovations allow providers to quickly access, share, and document information to advance patient care. He has also held many notable leadership, educational, and quality control positions throughout his career.

Dr. Tsai investigates ways to maximize the time clinicians spend with patients. He expedites and standardizes communication between health care providers and patients through the integration of mobile devices and remote patient monitoring programs. He streamlines the documentation process by updating electronic medical record tools and creating more efficient patient questionnaires to optimize the quality of care.

He has presented his research orally or in poster format at the American Medical Informatics Association, Family Medicine Education Consortium, and American Association of Neuromuscular and Electrodiagnostic Medicine. As a medical student, Dr. Tsai developed an open online osteopathic manipulation course, enrolling over 1,200 students. As a clinical fellow at Duke, he co-authored a textbook chapter on the future of health informatics

Clinical Focus

  • Family Medicine
  • Clinical Informatics
  • Artificial Intelligence

Academic Appointments

Professional Education

  • Board Certification: American Board of Family Medicine, Clinical Informatics (2023)
  • Fellowship: Duke University Hospital (2022) NC
  • Board Certification: American Board of Family Medicine, Family Medicine (2020)
  • Residency: Overlook Medical Center - Atlantic Health System (2020) NJ
  • Medical Education: Rowan University School of Osteopathic Medicine Registrar (2017) NJ

Contact

  • Academic
    University - Academic staff Department: Med/Primary Care and Population Health Position: Clinical Assistant Professor
  • Clinical (Primary)  Stanford Primary Care 2518 Mission College Blvd Ste 101 Santa Clara, CA 95054 (408) 688-9730 (fax)

Additional Clinical Info

Additional Info

Graduate and Fellowship Programs

All Publications

  • Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study. JMIR medical informatics Senior, R., Tsai, T., Ratliff, W., Nadler, L., Balu, S., Malcolm, E., McPeek Hinz, E. 2024; 12: e51274

    Abstract

    The problem list (PL) is a repository of diagnoses for patients' medical conditions and health-related issues. Unfortunately, over time, our PLs have become overloaded with duplications, conflicting entries, and no-longer-valid diagnoses. The lack of a standardized structure for review adds to the challenges of clinical use. Previously, our default electronic health record (EHR) organized the PL primarily via alphabetization, with other options available, for example, organization by clinical systems or priority settings. The system's PL was built with limited groupers, resulting in many diagnoses that were inconsistent with the expected clinical systems or not associated with any clinical systems at all. As a consequence of these limited EHR configuration options, our PL organization has poorly supported clinical use over time, particularly as the number of diagnoses on the PL has increased.We aimed to measure the accuracy of sorting PL diagnoses into PL system groupers based on Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concept groupers implemented in our EHR.We transformed and developed 21 system- or condition-based groupers, using 1211 SNOMED CT hierarchal concepts refined with Boolean logic, to reorganize the PL in our EHR. To evaluate the clinical utility of our new groupers, we extracted all diagnoses on the PLs from a convenience sample of 50 patients with 3 or more encounters in the previous year. To provide a spectrum of clinical diagnoses, we included patients from all ages and divided them by sex in a deidentified format. Two physicians independently determined whether each diagnosis was correctly attributed to the expected clinical system grouper. Discrepancies were discussed, and if no consensus was reached, they were adjudicated by a third physician. Descriptive statistics and Cohen κ statistics for interrater reliability were calculated.Our 50-patient sample had a total of 869 diagnoses (range 4-59; median 12, IQR 9-24). The reviewers initially agreed on 821 system attributions. Of the remaining 48 items, 16 required adjudication with the tie-breaking third physician. The calculated κ statistic was 0.7. The PL groupers appropriately associated diagnoses to the expected clinical system with a sensitivity of 97.6%, a specificity of 58.7%, a positive predictive value of 96.8%, and an F1-score of 0.972.We found that PL organization by clinical specialty or condition using SNOMED CT concept groupers accurately reflects clinical systems. Our system groupers were subsequently adopted by our vendor EHR in their foundation system for PL organization.

    View details for DOI 10.2196/51274

    View details for PubMedID 38836556

  • Building Pandemic-Resilient Primary Care Systems: Lessons Learned From COVID-19. Journal of medical Internet research Jeong, Y., Crowell, T., Devon-Sand, A., Sakata, T., Sattler, A., Shah, S., Tsai, T., Lin, S. 2024; 26: e47667

    Abstract

    On January 30, 2023, the Biden Administration announced its intention to end the existing COVID-19 public health emergency declaration. The transition to a "postpandemic" landscape presents a unique opportunity to sustain and strengthen pandemic-era changes in care delivery. With this in mind, we present 3 critical lessons learned from a primary care perspective during the COVID-19 pandemic. First, clinical workflows must support both in-person and internet-based care delivery. Second, the integration of asynchronous care delivery is critical. Third, planning for the future means planning for everyone, including those with potentially limited access to health care due to barriers in technology and communication. While these lessons are neither unique to primary care settings nor all-encompassing, they establish a grounded foundation on which to construct higher-quality, more resilient, and more equitable health systems.

    View details for DOI 10.2196/47667

    View details for PubMedID 38393776

https://orcid.org/0000-0001-8190-675X