Jawanna Henry; Brett Andriesen; Kevin Chaney, MGS; Tracy Okubo and Allison Dennis | August 11, 2021
Since 2018, new Leading Edge Acceleration Projects (LEAP) in Health IT award winners have joined an entrepreneurial group dedicated to developing and scaling technological breakthroughs. Together, these award winners create solutions to improve research capacities and improve medical care. This year is no exception. The University of Texas at Austin (UT Austin) and the DARTNet Institute are the latest recipients of the ONC LEAP in Health IT grant.
For 2021, LEAP in Health IT focused on two areas of interest:
- Area 1: Referral Management to Address Social Determinants of Health (SDOH) in accordance with clinical care
- Domain 2: Health IT tools to prepare data research for the electronic health record (EHR) and artificial intelligence (AI)
Referral management to address SDOH in line with clinical care
ONC recently announced the release of United States Core Data for Interoperability Version 2 (USCDI v2)which contained specific data elements that could help advance the use of health information technology to promote equity in health care, including the collection, access, use and reporting of standardized data on social determinants of health (SDOH).
The integration of SDOH data into EHRs provides valuable context on how a person’s living conditions can affect their health. However, merging SDOH data with data in EHRs is just the starting point. To maximize the use of SDOH data, it must also be integrated into a closed loop system to ensure patient handovers are completed seamlessly to prevent gaps in care when patients are referred or transferred to other care providers.
The University of Texas at Austin will be working on just that. UT Austin will build an Application Programming Interface (API) enabled social and health information platform that uses Health Level 7 International’s (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard to deliver a closed social services referral system integrate. The system will be accessible to the EHRs used by the Federally Qualified Health Centers (FQHCs) to consume SDOH data.
The system is used to:
- Dealing with social needs identified in clinical settings;
- Exchange of information between clinical providers and community-based organizations;
- Integrate clinical workflows into EHRs; and
- Provide patient access, consent and navigation through a mobile platform
The system will use the use cases provided by. were developed Gravity project for the collection of SDOH data related to food security, housing stability and transport access.
Health IT tools to make EHR data research and AI ready
Health data come from a wide variety of sources, and that usually means that the data is anything but standardized. Currently, EHR data are mainly collected for administrative and clinical purposes. However, this data would be of immense value to medical and public health researchers – if it were easily exploitable for them.
The DARTNet Institute will work to make clinical data usable for research and, through AI-supported models, for machine learning. DARTNet will work with Cloud Privacy Labs, a data protection technology company, to build and evaluate a data processing framework. The framework will use innovative technologies to enable semantic harmonization of health data collected from several small and large EHR systems of health care providers.
The collection of this data will provide the kind of high quality health datasets needed for AI-based modeling training and research needs. That will ultimately help Further development of the national health IT infrastructure to support research.
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