Current Role at Stanford

Infrastructure and Architecture Lead for the Research IT team. We design, build, and operate a variety of software applications and infrastructure to support research and improve patient health outcomes here at Stanford and at other hospitals around the world. I enjoy partnering with our researchers and clinicians to help them apply information technology to solve meaningful problems. I also manage a team of software developers working on tracking health status and outcomes, mobile health, and cloud computing infrastructure.

Education & Certifications

  • BS, Willamette University, Mathematics and Computer Science


  • Stanford Medicine Research Data Repository (STARR) f.k.a. STRIDE (2007 - Present)

    Software platform to support research involving patient data.

    ? Clinical data warehouse aggregating patient data from SHC and SCH
    ? Search tools for cohort identification
    ? Tools for reviewing patient cohorts for study inclusion/exclusion, and abstracting text notes
    ? Data lakes, data extraction, analysis pipelines
    ? Research data management - data entry tools, reporting, tissue tracking, imaging integration
    ? De-identification services and APIs
    ? Real-time research alerting based on HL7 events

    These systems were previously known by the name STRIDE.


    Stanford, CA


    • Susan Weber, Ms, SoM - IRT Research Technology
    • David Tom, Senior Internet Application Developer, SoM - IRT Research Technology
    • Tina Seto, SoM - IRT Research Technology

    For More Information:

  • Collaborative Health Outcomes Information Registry (CHOIR) (2011 - Present)

    A software system to monitor patient health status over time, providing better insight for doctors and better health outcomes for patients. We use sophisticated computer-adaptive testing and other techniques to minimize patient burden, and use standardized, validated instruments to quantitatively assess patient status.

    CHOIR was designed and built here at Stanford, and is shared without fee with a large consortium of hospitals and academic medical centers around the world. Consortium members actively collaborate as a community to further develop CHOIR and add new features.

    Stanford launched CHOIR in 2012, and it has been used at both hospitals and in many clinical areas. The system is also actively used for various research projects.


    Stanford, CA and other institutions around the world


    • Sean Mackey, Dr, Stanford University Medical Center
    • Ming-Chih Kao, Clinical Assistant Professor, Stanford University Medical Center
    • Beth Darnall, Associate Professor, Stanford University Medical Center
    • Susan Weber, Ms, SoM - IRT Research Technology
    • Randy Strauss, Software Developer, SoM - IRT Research Technology

    For More Information:

  • Stanford mHealth Platform (2015 - Present)

    Infrastructure to support mobile health applications such as phone apps, wearables, and Internet of Things (IoT) devices.

    This work started as part of the MyHeart Counts population health study, and has expanded to a variety of other projects.


    Stanford, CA

    For More Information:

  • Open Source Projects (2014 - Present)

    Create open source libraries, application templates, build/test/deployment infrastructure, and generally support our development of various software applications.


    Stanford, CA

    For More Information:


All Publications

  • Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. The New England journal of medicine Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P., Talati, N., Nag, D., Gummidipundi, S. E., Beatty, A., Hills, M. T., Desai, S., Granger, C. B., Desai, M., Turakhia, M. P., Apple Heart Study Investigators, Perez, M. V., Turakhia, M. P., Lhamo, K., Smith, S., Berdichesky, M., Sharma, B., Mahaffey, K., Parizo, J., Olivier, C., Nguyen, M., Tallapalli, S., Kaur, R., Gardner, R., Hung, G., Mitchell, D., Olson, G., Datta, S., Gerenrot, D., Wang, X., McCoy, P., Satpathy, B., Jacobsen, H., Makovey, D., Martin, A., Perino, A., O'Brien, C., Gupta, A., Toruno, C., Waydo, S., Brouse, C., Dorfman, D., Stein, J., Huang, J., Patel, M., Fleischer, S., Doll, E., O'Reilly, M., Dedoshka, K., Chou, M., Daniel, H., Crowley, M., Martin, C., Kirby, T., Brumand, M., McCrystale, K., Haggerty, M., Newberger, J., Keen, D., Antall, P., Holbrook, K., Braly, A., Noone, G., Leathers, B., Montrose, A., Kosowsky, J., Lewis, D., Finkelmeier, J. R., Bemis, K., Mahaffey, K. W., Desai, M., Talati, N., Nag, D., Rajmane, A., Desai, S., Caldbeck, D., Cheung, L., Granger, C., Rumsfeld, J., Kowey, P. R., Hills, M. T., Russo, A., Rockhold, F., Albert, C., Alonso, A., Wruck, L., Friday, K., Wheeler, M., Brodt, C., Park, S., Rogers, A., Jones, R., Ouyang, D., Chang, L., Yen, A., Dong, J., Mamic, P., Cheng, P., Shah, R., Lorvidhaya, P. 2019; 381 (20): 1909?17


    BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study number, NCT03335800.).

    View details for DOI 10.1056/NEJMoa1901183

    View details for PubMedID 31722151

Footer Links:

Stanford Medicine Resources: