FHIR could ease data gathering for clinical research
A research project is testing a new way to collect data on chronic diseases from EHRs in an effort to pave the way for better nationwide disease tracking.
A research initiative that uses data from electronic health records systems to support a national sentinel surveillance system to monitor chronic diseases is working on using HL7’s FHIR (Fast Healthcare Interoperability Resource) standard to facilitate data collection.
The initiative, called the Multistate EHR-based Network for Disease Surveillance, or MENDS, is testing an approach that would use a bulk data application programming interface, or API, to make it easier to collect de-identified patient information from a wide range of EHR systems to better support nationwide disease surveillance.
The work is supported by a grant from the Centers for Disease Control and Prevention to the National Association of Chronic Disease Directors. It’s gaining additional support from the University of Colorado Health Data Compass Research Institute, enabled by a grant from Google.
A progress report on the initiative recently was presented by Bob Zambarano, a vice president for healthcare analytics for Commonwealth Informatics, a data consultancy that’s providing technical support for the project. MENDS uses technical elements and design from Massachusetts’ state-level distributed analytics network for chronic disease surveillance.
In a presentation at HL7’s FHIR DevDays International event in June, Zambarano said the goal of the FHIR initiative is to make it easier to automate the collection of core clinical data from any type of EHR system to provide a more well-rounded data pool to support research on chronic diseases.
MENDS is related to two other distributed analytics networks, PCORNet and FDA Sentinel. It uses an open-source product suite called ESPHealth, an acronym for EHR Support for Public Health. MENDS can pull de-identified information from some EHR systems, gathering limited core clinical information into a data mart, against which queries can be written, Zambarano explained.
MENDS has agreements with five data organizations, including the Regenstrief Institute and Health Data Compass, to enable access to data in states or regions.
Until now, MENDS has been using a data collection process that leverages an extract, transform and load, or ETL, approach – a data integration process that combines data from multiple data sources into a single, consistent data store within a data warehouse. In its latest effort, MENDS is testing a shift to an FHIR-based approach.
“ETL is painful when it’s used with other health data systems, and with the current regulatory imperative to use FHIR APIs, it makes sense to develop an FHIR approach,” Zambrano said.
In the ongoing FHIR testing, data requests are sent in batch mode via FHIR APIs, setting off a process that gathers information in records on patient encounters, conditions, observations, claims, medication orders and immunization. The files that eventually flow back into the data mart contain patient medical histories that go back several years and approach 1 terabyte in size, Zambarano said.
Development work for the bulk data FHIR coding and APIs wasn’t as difficult as understanding the structures for the EHR systems that are the source of the data and then making sure that all data gathered is consistent within the MENDS data mart so researchers can query against it, he said. “And the biggest problems are obtaining the appropriate authorizations with the different organizations responsible for data privacy.”
If the initial efforts are successful, they could pave the way for easier access to the data by applying the FHIR standard and using a simpler approach to get that data into a cloud, where it could be easier to analyze, Zambarano said. He expects that the initial work by Health Data Compass could simplify integrations with other data research organizations that now work with MENDS.
More information on the presentation and HL7’s FHIR DevDays event can be found here.