With the government pushing comparative effectiveness research (CER), it also has funded a look at the informatics platforms developed so far, according to research recently published in the journal Medical Care.
The Patient-Centered Outcomes Research Institute (PCORI), a nonprofit established by the Affordable Care Act, this month awarded $30 million in grants for CER projects.
CER uses data from electronic health records and other sources for studies about which tests and treatments work best in everyday practice. It's come under fire, however, as intruding upon physicians' autonomy in care and being a basis for healthcare rationing, according to a commentary published by Philadelphia-based medical freelance writer Christopher Guadagnino, Ph.D. in The Hospitalist.
The informatics platforms studied are being used in myriad ways, including studies on ADHD, obesity, use of counseling for smoking cessation and more. A few outlined included:
- Scalable Partnering Network For Comparative Effectiveness Research: Across Lifespan, Conditions, And Settings (SPAN): This consortium of 19 health plans has formal research capabilities into conditions such as ADHD and obesity. It uses a virtual data warehouse augmented with state and local cancer registry information. SPAN has a new platform in the works called PopMedNet.
- The Partners Research Patient Data Registry (RPDR): This enterprise data warehouse is used to recruit patients for clinical trials and to perform active surveillance for CER across Partners HealthCare in Boston. Using data from billing, decision support and EHRs, it finds matched controls for patients.
- The Surgical Care Outcomes Assessment Program Comparative Effectiveness Research Translation Network (SCOAP-CERTN): In partnership with Microsoft Health Solutions Group, it's working to streamline data extraction from and existing statewide quality assurance and quality improvement registry.
The authors identified six steps for preparing data for any multi-institutional CER project: data identification, extraction, modeling, aggregation, analysis, and dissemination.
Only three projects allowed the combining of patient data from multiple care sources. All used different, and sometimes multiple, data storage and manipulation formats. Only three had natural language processing capability; the others relied on numeric or coded data.
While all were working on "user-friendly" tools to allow nontechnical researchers to make ad-hoc queries, only one had a working version. And all the projects had an oversight committee responsible for data ownership, sharing and publication rights.
The researchers pointed to the need in CER for comprehensive patient data from as many sources as possible; from populations from multiple organizations--which raises the problem of matching patient records across care sites, an issue recently addressed by the Bipartisan Policy Center. They said it requires data extraction, modeling, aggregation and analysis methods and tools--mapping the data for analysis poses another huge challenge.
They concluded that all the systems are "are on their way to creating the sociotechnical infrastructure required to enable researchers from multiple institutions to conduct high-quality, cost-effective CER."