Timothy Tsai

All Publications

• Transforming the Primary Care Journey with Generative AI: A Foundation Model to Boost Efficiency, Quality, and Engagement. Journal of general internal medicine Yang, B., Lee, J. J., Tsai, T., Sattler, A. L., Shah, S., Smith, M. A., Lin, S. 2025

  Abstract

  Primary care is the cornerstone of every patient's health journey. Given its central role in the delivery of medical care, primary care is crucial in the integration of artificial intelligence (AI) into healthcare. Generative AI has the potential to augment primary care workflows to achieve the quintuple aim. In this paper, we use a hypothetical case to outline a medical care experience for both a patient and provider. We highlight five common problems faced by primary care providers (PCPs): data overload, rapidly changing evidence, barriers to adequate patient education, clinical documentation burden, and increase in patient electronic messages. We propose a generative AI foundation model that can transform the primary care journey through chart summarization, clinical decision support, personalized patient instructions, clinical documentation, and asynchronous care delivery.

  View details for DOI 10.1007/s11606-025-09716-y

  View details for PubMedID 40802036

  View details for PubMedCentralID 11384335

• Data Transformation to Advance AI/ML Research and Implementation in Primary Care. Annals of family medicine Tsai, T., Lee, J. J., Phillips, R., Lin, S. 2025; 23 (4): 363-367

  Abstract

  Artificial intelligence and machine learning (AI/ML) in health care is accelerating at a breathtaking pace. As the largest health care delivery platform, primary care is where the power, opportunity, and future of AI/ML are most likely to be realized in the broadest and most ambitious scale. However, there is a relative lack of organized, open, large-scale primary care datasets to attract industry and academia in primary care-focused research and development. This article proposes a set of high-level considerations around the data transformation that is needed to enable the growth of AI/ML applications in primary care. These considerations call for automation of data collection, organization of fragmented data, identification of primary care-specific use cases, integration of AI/ML into human workflows, and surveillance for unintended consequences. By unlocking the power of its data, primary care can play a leading role in advancing health care AI/ML to support patients, clinicians, and the health of the nation.

  View details for DOI 10.1370/afm.240459

  View details for PubMedID 40721338

• 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