New Meaningful Use 3 Requirement: Inclusion of Patient Generated Health Data

In December, Centers for Medicare & Medicaid Services (CMS) announced final rules of Meaningful Use 3 (MU3)—the third and final iteration of the Meaningful Use Program. The principal goal of this incentive program is to ensure that electronic health records are being used by providers in a way that improves quality of care (e.g., used for e-prescribing, or for submission of clinical quality measures). MU requires providers to meet these criteria in order to receive incentive payments and avoid downward reimbursement adjustments.

As part of MU3, eligible providers will be required to integrate Patient Generated Health Data (PGHD) with clinical data in the EHR for at least 5 percent of the patient population. PGHD includes any data that is generated outside of the clinical setting. Examples include data captured by a device such as a smart phone, or self-reported data (e.g., diet, functional status, emotion well-being) that is manually recorded by the patient. The patient both captures and transfers that data to the provider.

Inclusion of PGHD in this third and final phase of Meaningful Use is exciting for several reasons  

First, this new rule has potential to incentivize providers to invest in the technology and infrastructure (e.g., data storage and security) that will support the integration and use of this data, which to date has not been systematically incorporated into routine patient care.

Second, this new rule coincides with the rapidly growing wearable device market and consumer use of these devices that allows patients to capture their own health data outside of the clinic or hospital setting. Integrating these data points with clinical data and allowing providers to use these data at the point of care will contribute to patient engagement, patient activation, and self-management.

Third, at the policy level, this is likely to drive interoperability and data security standards, which could have broader and positive implications for other types of healthcare data and analytics.

How should providers prepare?

This new ruling will go into effect in 2018, thus giving providers time to make changes to current EMRs and technology that will support the use of the transfer, use, and storage of this data.

At Intel, we are working to advance these goals through data security efforts, big data analytics, data storage capabilities, and wearable devices that promote and support PGHD.

One such initiative within Intel Health & Life Sciences involves Big Cloud Analytics and its COVALENCE Health Analytics Platform. The COVALENCE Health Analytics Platform is powered by Intel Xeon processor-based servers in the cloud, which ensures a secure, reliable, and scalable infrastructure. Big Cloud Analytics utilizes the Basis Peak watch, which provides 24x7 real-time heart rate monitoring, and supplies metrics for sleep patterns, steps taken, skin temperature, and perspiration. It collects readings on 50 biometric data points every 60 seconds and syncs the data security with the Basis Cloud.  This allows insurance providers, healthcare institutions, and employers to securely use wearable device data to engage patients with event-triggered personalized messaging.

Biometric sensor data gathered from the device is also transmitted to the cloud or on premise data storage and aggregated in the COVALENCE Health Analytics Platform. This platform transforms data into business intelligence and predictive analytics. It then generates wellness scores, bio-identity scores, and many others. Insights based on analysis of the data points and trends provide an early indication of potential health issues or lack of progress toward health goals.

PGHD as part of routine care: opportunities and challenges

While PGHD will substantially increase the number of data points that can inform healthcare and lead to new insights, we recognize that operationalizing the transmission and use of PGHD will not happen instantly, nor effortlessly. Many questions remain as to how this data will be most effectively used by providers and patients. For example, what is relevant data?  How should providers communicate this to patients so that the appropriate data can be collected and transferred? How much data will providers want and need to obtain in order to make this data useful for patient care? How often will providers want to see this data? How might this influx of data affect staff or clinic workflows? From a user experience perspective, how will this data be best displayed so that providers and patients alike can act upon it? Perhaps further research, particularly ethnographic research that takes into account both the clinician and patient perspective, is needed if we are to use this data in way that translates to better patient outcomes.