ONC aligns with Accenture on two pilot programs to study patient-generated health data in action
The Office of the National Coordinator for Health Information Technology revealed that it is funding two pilots to better understand how patient-generated health data can be put to use.
Accenture Federal Services (AFS), which is contracting with ONC to help the federal government develop standards for collecting and using PGHD, selected the pilots.
Sutter Health is conducting one pilot using Validic’s digital health platform for integrating data from consumer health apps, in-home devices and wearables to focus on patients with Type II diabetes.
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Sutter will give patients its (Motivating Patients Online with Enhanced Resources) smartphone app, which can connect via Validic’s VitalSnap to devices that measure blood glucose, blood pressure, level of activity and weight. That software interprets the information and provides visual feedback and motivational incentives to patients and alerts care teams when attention is required.
In the second pilot, Amita Health in metropolitan Chicago is using TapCloud to collect PGHD and associated clinical results across several medical conditions such as orthopedic surgery, stroke, and behavioral health.
TapCloud’s platform includes a patient app that provides information to and collects information from patients, and a clinical dashboard synthesizing large volumes of individual data points.
Both pilot demonstrations are part of the "Conceptualizing a Data Infrastructure for the Capture, Use, and Sharing of Patient-Generated Health Data in Care Delivery and Research through 2024" program funded by ONC.
Patient generated health data will be among the topics at the Big Data & Analytics Forum in Boston, Oct. 24-25. What to expect:
⇒ Charlotte hospitals analyze social determinants of health to cut ER visits
⇒ Big Data: Healthcare must move beyond the hype
⇒ Tips for reading Big Data results correctly
⇒ Small hospital makes minor investment in analytics and reaps big rewards
⇒ MIT professor's quick primer on two types of machine learning for healthcare
⇒ Must-haves for machine learning to thrive in healthcare
