Community Data Sharing

Melinda Buntin PhD, Professor & Chair for the Department of Health Policy and the Community Health Peer (CHP) Learning Program at Vanderbilt University, spoke at Academy Health’s 2016 Consortium https://medschool.vanderbilt.edu/health-policy. She discussed the success of the Community Health Peer (CHP Learning Program www.academyhealth.org/CHPHealthIT  especially when dealing with infant mortality in Tennessee communities.

The CHP Learning Program was initiated by AcademyHealth www.academyhealth.org along with partners from the National Partnership for Women & Families and NORC at the University of Chicago. Their goal is to collaboratively work to enable communities to link and share critical data affecting population health improvement with other communities but also among individual departments.

In July 2015, the HHS Office of the National Coordinator for HIT www.healthit.gov awarded AcademyHealth $2.2 million to lead the CHP Learning Program. Throughout the two year program AcademyHealth has been working in partnership with ONC to establish a national peer learning collaborative for 15 competitively awarded communities to address specified population health management challenges.

In January 2016, AcademyHealth announced that the 15 communities, including 10 Participant Communities and 5 Subject Matter Expert Communities were selected to identify data solutions, accelerate local progress, disseminate the best practices and lessons learned, and align with other delivery system reform efforts.

According to Dr. Buntin, “Before the CHP Learning Program was undertaken and despite the fact that many programs and resources were available, no coordinated effort had been undertaken to address the total problem that Nashville was facing due to the full scale of infant mortality in the community.”

Dr. Buntin reports that the CHP Learning Program at Vanderbilt is developing a data sharing network called the ‘Nashville Infant Mortality Project”. The objective is to share data and then aggregate and analyze the disparate data. This will enable identifying of at-risk pregnant women and infants so that they can be referred to community specific and culturally appropriate interventions.

Plans for the first six months include sharing data electronically and then create a model to identify high risk mothers and infants. The months from seven to twelve will test predictive models using previous infant mortality data and work to find new partners to fill data gaps as needed. Lastly, the Develop Community Action Plan will be developed.

In months thirteen through seventeen, algorithms will be used to target possible interventions, and then feedback will be collected so that data can be effectively shared. University faculty and staff will then have experience in collecting, exchanging, and using health data as well as expertise in translating their research into possible policy actions.