Information Technology in PBRNs: The Indiana University Medical Group Research Network (IUMG ResNet) Experience
Information Technology in PBRNs: The Indiana University Medical Group Research Network (IUMG ResNet) Experience, Abel Kho, MD MS, Atif Zafar, MD and William Teirney, MD, J AM Board Fam Med, 2007;20:196-203.
OBJECTIVE: To integrate clinical information system applications in order to facilitate the process of identifying and enrolling potential research subjects in practice-based research networks (PBRNs).
METHODS: ResNet is a research network of 110 primary care physicians at 17 sites, clinically networked via the Regenstrief Medical Record System (RMRS). All research studies are organized centrally by a research data base that tracks inclusion and exclusion criteria using the same standards for data coding (LOINC, CPT, and ICD) used by the RMRS. On a daily basis, data managers query the RMRS for a list of patients who fit the eligibility criteria, and subsequently query the registration records (IDX) to determine when patients are due for an appointment. The system then uses the Medical Gopher (computer physician order entry) system to notify physicians on the day of the appointment which patients may be eligible for a research study. For studies of acute conditions, potential subjects may be identified by a query of the Regenstrief Study Monitoring Database that is initiated when the physician types in the reason for the visit. If the physician agrees to have the patient screened for the study and keys in “yes,” an email is automatically generated and sent to notify the research assistants of the willingness of and the location of the patient. The system relies heavily upon informatics—use of standard terminology, a central data repository, and HL7 capabilities for messaging between various applications.
RESULTS: The PBRN has screened more than 18,000 patients and enrolled more than 6000 study subjects in 5 years. Less than 2% of potentially eligible patients are missed by research assistants.
CONCLUSIONS: Electronic data collection can greatly facilitate clinical research. Key principles for successful implementation include use of standards for data storage, definition, and transmission.