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    Stanford University builds primary care precision health dataset by developing Humanwide project
  • Stanford University builds primary care precision health dataset by developing Humanwide project

     

    Stanford University's Department of Primary Care has launched the Humanwide Demonstration Program, which aims to provide patients with genetic screening, wearable sensors, health assessments and health coaching.

    The author is Megan Mahoney, PhD, Department of Primary Care and Population Health, Stanford University.

     

    project name

    Humanwide

     

    Project Description

    Humanwide is part of Stanford Medicine's triple vision for precision health -- predicting and preventing disease more effectively, and curing it more precisely.

     

    Implementing agency

    Stanford University Department of Primary Care

     

    Implementation purpose

    Provide patients with genetic screening, wearable sensors, health assessments and health coaching. The researchers synthesized patient-level biometric, genetic, social, environmental and behavioral data into electronic health records that primary care teams can use to develop individual care plans.

     

    Implementation steps

    Investigators began with a 9-month design/thought process to assess patient and health care provider needs for comprehensive data and available paradigms in health care mediated by technical and genetic data.

    From January to July 2018, investigators recruited an initial 50 patients for proof-of-concept, taking care to include representation across age, race/ethnicity, gender, and medical complexity.

    Digital health data from homes and wearables is linked to electronic health records, combined with genetic information, and displayed graphically to medical teams in order to spot whether diseases are poorly controlled and prevent complications.

     

    Implementation of Humanwide requires:

    (1) Health-oriented assessment of personal behavior, social and environmental health;

    (2) Bluetooth-enabled household scales, blood glucose meters, blood pressure cuffs and pedometers;

    (3) Genetic assessment of disease risk and drug interactions.

     

    Health services for patients include:

    (1) in-person and virtual health instruction;

    (2) Community resource referral;

    (3) Coordination of health services among primary care providers, medical assistants, certified health coaches, clinical pharmacists, behavioral health practitioners, nutritionists, registered nurses, and genetic counselors.

     

    Implementation Effect

    In addition to creating an effective referral process for genetic counseling, increased education on genetic screening for primary care patients and providers can be helpful. While the most common reason for enrollment was patient and provider interest in genetic testing, most patients took advantage of the extensive Humanwide resources. Encouraging the use of wearables in healthy populations can help identify multiple individuals with early diabetes and hypertension, facilitating early intervention and self-management.

    The researchers' early experience with Humanwide suggests that creating a more comprehensive patient-centric data environment is feasible and acceptable to both patients and providers. The researchers hope to paint a picture of patient behavior, genetics and physical attributes common in primary care today.

     

    Implementation case

    In one patient, intermittent hypertension was detected by the ambulatory blood pressure cuff, which was later validated with a more formal 24-hour ambulatory blood pressure assessment. Patients are able to initiate lifestyle changes to control blood pressure. Pharmacogenetic testing allows investigators to personalize doses and optimize drug combinations based on a patient's drug-gene interactions.

    The investigators identified a patient who complained of leg cramps as a slow metabolizer of statins and reduced the dose of other statins to address the cramps. Next steps include testing predictive analytics to account for more complex data patterns.

  • Pubdate: 2022-02-28    Viewed: 260