Current Role at Stanford


Software team lead and architect with 20+ years of experience managing and developing enterprise scale custom applications. A seasoned Stanford technical leader that sets vision, provides guidance and drives initiatives for the team. Self-driven and highly motivated, I practice open communication and encourage collaboration across functional teams to achieve objectives.

Responsible for defining project scope, analyzing requirements and designing deliverables for various administrative and research software solutions. Lead and collaborate with cross functional teams including product managers, security/privacy offices, infrastructure teams and project team members, to deliver solution in time. Representing the AAS team to propose SOW (statement of work) and negotiate service agreements with business clients.

Honors & Awards


  • 2021 Integrated Strategic Plan STAR Award for the Research Participation Application, Stanford Health Care and School of Medicine (2021)

Education & Certifications


  • Certificate, Project Management Institute (PMI), PMP®: Project Management Professional (2020)
  • Certificate, MOR Associates & Stanford University, Stanford Technical Leadership Program, STLP (2019)

Projects


  • Participant Engagement Platform (PEP) - 2021 ISP Star Award Winner, Stanford Medicine

    PEP is a research administration software that automates the study recruitment process (known as the Honest Broker workflow) and initiates recruitment engagement thru EPIC integration.

    Location

    Stanford, CA

  • Faculty Leave Accrual (FLA), Stanford University (2020 - 2021)

    FLA is an administrative software that enables OAA admins to manage faculty leave accrual. With the built-in smart algorithm, FLA integrates multiple sources of faculty data and presents the calculated accrual data to users.

    Location

    Stanford, CA

  • Apple Heart Study (AHS) - study published in The New England Journal of Medicine, Stanford University (2018 - 2020)

    AHS is a large scale assessment of a smartwatch to identify Atrial Fibrillation. Over 400,000 participants signed up for AHS study, and over 20TB of data was collected, validated and managed for analysis and publication.

    Location

    Stanford, CA

  • OneDirectory, Stanford University (2017 - 2018)

    One Directory provides a directory lookup that spans Stanford University, Stanford Health Care, and Stanford Childrens. It provides a single place to look up the contact information for a person regardless of their organizational affiliation.

    Location

    Stanford, CA

Service, Volunteer and Community Work


  • Executive Director and Public Speaking Coach, Soundpost Youth Foundation

    https://soundpostyouth.org

    Location

    San Francisco, CA

  • Membership Chair, Stanford Talk of the Farm Speaking Club

    https://cop.stanford.edu/community/talk-farm-speaking-club

    Location

    Stanford, CA

Patents


  • Grace Hung, Christina S. Wu, Christine Cheng, Desyang L. Lyou, Annie Dang. "United States Patent 20090094521 Method and Apparatus to Automate Configuration of Network Entities", Coriant Operations Inc, Oct 9, 2007

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

    Abstract

    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 ClinicalTrials.gov number, NCT03335800.).

    View details for DOI 10.1056/NEJMoa1901183

    View details for PubMedID 31722151

  • Arrhythmias Other Than Atrial Fibrillation in Those With an Irregular Pulse Detected With a Smartwatch: Findings From the Apple Heart Study. Circulation. Arrhythmia and electrophysiology Perino, A. C., Gummidipundi, S. E., Lee, J., Hedlin, H., Garcia, A., Ferris, T., Balasubramanian, V., Gardner, R. M., Cheung, L., Hung, G., Granger, C. B., Kowey, P., Rumsfeld, J. S., Russo, A. M., True Hills, M., Talati, N., Nag, D., Tsay, D., Desai, S., Desai, M., Mahaffey, K. W., Turakhia, M. P., Perez, M. V. 2021: CIRCEP121010063

    Abstract

    The Apple watch irregular pulse detection algorithm was found to have a positive predictive value of 0.84 for identification of atrial fibrillation (AF). We sought to describe the prevalence of arrhythmias other than AF in those with an irregular pulse detected on a smartwatch.The Apple Heart Study investigated a smartwatch-based irregular pulse notification algorithm to identify AF. For this secondary analysis, we analyzed participants who received an ambulatory ECG patch after index irregular pulse notification. We excluded participants with AF identified on ECG patch and described the prevalence of other arrhythmias on the remaining participant ECG patches. We also reported the proportion of participants self-reporting subsequent AF diagnosis.Among 419 297 participants enrolled in the Apple Heart Study, 450 participant ECG patches were analyzed, with no AF on 297 ECG patches (66%). Non-AF arrhythmias (excluding supraventricular tachycardias <30 beats and pauses <3 seconds) were detected in 119 participants (40.1%) with ECG patches without AF. The most common arrhythmias were frequent PACs (burden ≥1% to <5%, 15.8%; ≥5% to <15%, 8.8%), atrial tachycardia (≥30 beats, 5.4%), frequent PVCs (burden ≥1% to <5%, 6.1%; ≥5% to <15%, 2.7%), and nonsustained ventricular tachycardia (4-7 beats, 6.4%; ≥8 beats, 3.7%). Of 249 participants with no AF detected on ECG patch and patient-reported data available, 76 participants (30.5%) reported subsequent AF diagnosis.In participants with an irregular pulse notification on the Apple Watch and no AF observed on ECG patch, atrial and ventricular arrhythmias, mostly PACs and PVCs, were detected in 40% of participants. Defining optimal care for patients with detection of incidental arrhythmias other than AF is important as AF detection is further investigated, implemented, and refined.

    View details for DOI 10.1161/CIRCEP.121.010063

    View details for PubMedID 34565178