Clinical Focus

  • Internal Medicine
  • ClickWell Care

Academic Appointments

Administrative Appointments

  • Physician Lead, ClickWell Care, Stanford University School of Medicine (2016 - Present)

Honors & Awards

  • 2016 Tilles Award for Junior-Emerging ACP Servant Leader, California Northern Chapter, American College of Physicians (05/2016)
  • Provider Innovation in Informatics Award, iHealth 2015, American Medical Informatics Association (04/2015)
  • Best Generalist Faculty Teacher of the Year 2011-2012, Southern Illinois University School of Medicine, Internal Medicine Residency Program (07/08/2012)
  • Joseph E. Johnson Leadership Grant Award for ACP Leadership Day 2012, American College of Physicians (07/05/2012)
  • Tufts Health Care Institute Quality and Patient Safety Change Agent Awards, Honorable Mention, Tufts Health Care Institute (05/02/2012)
  • Jay F. Blunck Memorial Award (Resident Physician), Northwestern University, Internal Medicine Residency Program (06/2011)
  • VA Professionalism Award (Resident Physician), Northwestern University, Internal Medicine Residency Program (07/2010)
  • Resident Excellence in Teaching Awards, Northwestern University, Internal Medicine Residency Program (07/2008, 12/2009, 07/2010)
  • Delta Omega Honorary Society Poster Recognition, Delta Omega Honorary Society in Public Health (10/27/2008)

Boards, Advisory Committees, Professional Organizations

  • Clinician Panelist, Patient-Centered Outcomes Research Institute's Advisory Panel on Improving Healthcare Systems (2013 - 2015)
  • Member, Society of General Internal Medicine (2012 - Present)
  • Member, American Medical Informatics Association (2012 - Present)
  • Fellow, American College of Physicians (2010 - Present)

Professional Education

  • Postdoctoral Scholar, Department of Veterans Affairs - VA Palo Alto Health Care System, Medical Informatics (2014)
  • Residency:McGaw Medical Center of Northwestern University (2011) IL
  • Medical Education:Northwestern Univ - McGaw Medical Center (2008) IL
  • Board Certification: Internal Medicine, American Board of Internal Medicine (2011)
  • Master of Public Health, Northwestern University Graduate School, Public Health (2008)
  • Bachelor of Science, Northwestern University McCormick School of Engineering, Biomedical Engineering (2004)


All Publications

  • Overlap in drug-disease associations between clinical practice guidelines and drug structured product label indications. Journal of biomedical semantics Leung, T. I., Dumontier, M. 2016; 7: 37


    Clinical practice guidelines (CPGs) recommend pharmacologic treatments for clinical conditions, and drug structured product labels (SPLs) summarize approved treatment indications. Both resources are intended to promote evidence-based medical practices and guide clinicians' prescribing decisions. However, it is unclear how well CPG recommendations about pharmacologic therapies match SPL indications for recommended drugs. In this study, we perform text mining of CPG summaries to examine drug-disease associations in CPG recommendations and in SPL treatment indications for 15 common chronic conditions.We constructed an initial text corpus of guideline summaries from the National Guideline Clearinghouse (NGC) from a set of manually selected ICD-9 codes for each of the 15 conditions. We obtained 377 relevant guideline summaries and their Major Recommendations section, which excludes guidelines for pediatric patients, pregnant or breastfeeding women, or for medical diagnoses not meeting inclusion criteria. A vocabulary of drug terms was derived from five medical taxonomies. We used named entity recognition, in combination with dictionary-based and ontology-based methods, to identify drug term occurrences in the text corpus and construct drug-disease associations. The ATC (Anatomical Therapeutic Chemical Classification) was utilized to perform drug name and drug class matching to construct the drug-disease associations from CPGs. We then obtained drug-disease associations from SPLs using conditions mentioned in their Indications section in SIDER. The primary outcomes were the frequency of drug-disease associations in CPGs and SPLs, and the frequency of overlap between the two sets of drug-disease associations, with and without using taxonomic information from ATC.Without taxonomic information, we identified 1444 drug-disease associations across CPGs and SPLs for 15 common chronic conditions. Of these, 195 drug-disease associations overlapped between CPGs and SPLs, 917 associations occurred in CPGs only and 332 associations occurred in SPLs only. With taxonomic information, 859 unique drug-disease associations were identified, of which 152 of these drug-disease associations overlapped between CPGs and SPLs, 541 associations occurred in CPGs only, and 166 associations occurred in SPLs only.Our results suggest that CPG-recommended pharmacologic therapies and SPL indications do not overlap frequently when identifying drug-disease associations using named entity recognition, although incorporating taxonomic relationships between drug names and drug classes into the approach improves the overlap. This has important implications in practice because conflicting or inconsistent evidence may complicate clinical decision making and implementation or measurement of best practices.

    View details for DOI 10.1186/s13326-016-0081-1

    View details for PubMedID 27277160

  • Comparing Drug-Disease Associations in Clinical Practice Guideline Recommendations and Drug Product Label Indications. Studies in health technology and informatics Leung, T. I., Dumontier, M. 2015; 216: 1039-?


    Clinical practice guidelines (CPGs) and structured product labels (SPLs) are both intended to promote evidence-based medical practices and guide clinicians' prescribing decisions. However, it is unclear how well CPG recommendations about pharmacologic therapies for certain diseases match SPL indications for recommended drugs. In this study, we use publicly available data and text mining methods to examine drug-disease associations in CPG recommendations and SPL treatment indications for 15 common chronic conditions. Preliminary results suggest that there is a mismatch between guideline-recommended pharmacologic therapies and SPL indications. Conflicting or inconsistent recommendations and indications may complicate clinical decision making and implementation or measurement of best practices.

    View details for PubMedID 26262338

  • Automating Identification of Multiple Chronic Conditions in Clinical Practice Guidelines. AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science Leung, T. I., Jalal, H., Zulman, D. M., Dumontier, M., Owens, D. K., Musen, M. A., Goldstein, M. K. 2015; 2015: 456-460


    Many clinical practice guidelines (CPGs) are intended to provide evidence-based guidance to clinicians on a single disease, and are frequently considered inadequate when caring for patients with multiple chronic conditions (MCC), or two or more chronic conditions. It is unclear to what degree disease-specific CPGs provide guidance about MCC. In this study, we develop a method for extracting knowledge from single-disease chronic condition CPGs to determine how frequently they mention commonly co-occurring chronic diseases. We focus on 15 highly prevalent chronic conditions. We use publicly available resources, including a repository of guideline summaries from the National Guideline Clearinghouse to build a text corpus, a data dictionary of ICD-9 codes from the Medicare Chronic Conditions Data Warehouse (CCW) to construct an initial list of disease terms, and disease synonyms from the National Center for Biomedical Ontology to enhance the list of disease terms. First, for each disease guideline, we determined the frequency of comorbid condition mentions (a disease-comorbidity pair) by exactly matching disease synonyms in the text corpus. Then, we developed an annotated reference standard using a sample subset of guidelines. We used this reference standard to evaluate our approach. Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs. Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others. Sixty-one (29.0%) of 210 possible disease-comorbidity pairs occurred zero times; for example, no guideline on chronic kidney disease mentioned depression, while heart failure guidelines mentioned ischemic heart disease the most frequently. Our method adequately identifies comorbid chronic conditions in CPG recommendations with precision 0.82, recall 0.75, and F-measure 0.78. Our work identifies knowledge currently embedded in the free text of clinical practice guideline recommendations and provides an initial view of the extent to which CPGs mention common comorbid conditions. Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

    View details for PubMedID 26306285

  • Chapters on “Sepsis;" “Depression;” “Transitions of Care: Patients with Pending Issues;" “Transitions of Care: Transitions of Care: Patients with Multiple Medication Changes" Resident Readiness Internal Medicine (1st ed). New York, NY: McGraw-Hill Companies, Inc. Hingle, S. T., Klamen, D. L. 2013
  • Literacy and Retention of Information After a Multimedia Diabetes Education Program and Teach-Back JOURNAL OF HEALTH COMMUNICATION Kandula, N. R., Malli, T., Zei, C. P., Larsen, E., Baker, D. W. 2011; 16: 89-102


    Few studies have examined the effectiveness of teaching strategies to improve patients' recall and retention of information. As a next step in implementing a literacy-appropriate, multimedia diabetes education program (MDEP), the present study reports the results of two experiments designed to answer (a) how much knowledge is retained 2 weeks after viewing the MDEP, (b) does knowledge retention differ across literacy levels, and (c) does adding a teach-back protocol after the MDEP improve knowledge retention at 2-weeks' follow-up? In Experiment 1, adult primary care patients (n = 113) watched the MDEP and answered knowledge-based questions about diabetes before and after viewing the MDEP. Two weeks later, participants completed the knowledge assessment a third time. Methods and procedures for Experiment 2 (n = 58) were exactly the same, except that if participants answered a question incorrectly after watching the MDEP, they received teach-back, wherein the information was reviewed and the question was asked again, up to two times. Two weeks later, Experiment 2 participants completed the knowledge assessment again. Literacy was measured using the S-TOFHLA. After 2 weeks, all participants, regardless of their literacy levels, forgot approximately half the new information they had learned from the MDEP. In regression models, adding a teach-back protocol did not improve knowledge retention among participants and literacy was not associated with knowledge retention at 2 weeks. Health education interventions must incorporate strategies that can improve retention of health information and actively engage patients in long-term learning.

    View details for DOI 10.1080/10810730.2011.604382

    View details for Web of Science ID 000299952500010

    View details for PubMedID 21951245