Effectiveness and cost of recruiting healthy volunteers for clinical research studies using an electronic patient portal: A randomized study.

INTRODUCTION: It is not clear how to effectively recruit healthy research volunteers. METHODS: We developed an electronic health record (EHR)-based algorithm to identify healthy subjects, who were randomly assigned to receive an invitation to join a research registry via the EHR's patient portal, letters, or phone calls. A follow-up survey assessed contact preferences. RESULTS: The EHR algorithm accurately identified 858 healthy subjects. Recruitment rates were low, but occurred more quickly via the EHR patient portal than letters or phone calls (2.7 vs. 19.3 or 10.4 d). Effort and costs per enrolled subject were lower for the EHR patient portal (3.0 vs. 17.3 or 13.6 h, $113 vs. $559 or $435). Most healthy subjects indicated a preference for contact via electronic methods. CONCLUSIONS: Healthy subjects can be accurately identified from EHR data, and it is faster and more cost-effective to recruit healthy research volunteers using an EHR patient portal.

Electronic medical record integration with a database for adult congenital heart disease: Early experience and progress in automating multicenter data collection.

BACKGROUND: The adoption of electronic health records (EHR) has created an opportunity for multicenter data collection, yet the feasibility and reliability of this methodology is unknown. The aim of this study was to integrate EHR data into a homogeneous central repository specifically addressing the field of adult congenital heart disease (ACHD). METHODS: Target data variables were proposed and prioritized by consensus of investigators at five target ACHD programs. Database analysts determined which variables were available within their institutions' EHR and stratified their accessibility, and results were compared between centers. Data for patients seen in a single calendar year were extracted to a uniform database and subsequently consolidated. RESULTS: From 415 proposed target variables, only 28 were available in discrete formats at all centers. For variables of highest priority, 16/28 (57%) were available at all four sites, but only 11% for those of high priority. Integration was neither simple nor straightforward. Coding schemes in use for congenital heart diagnoses varied and would require additional user input for accurate mapping. There was considerable variability in procedure reporting formats and medication schemes, often with center-specific modifications. Despite the challenges, the final acquisition included limited data on 2161 patients, and allowed for population analysis of race/ethnicity, defect complexity, and body morphometrics. CONCLUSION: Large-scale multicenter automated data acquisition from EHRs is feasible yet challenging. Obstacles stem from variability in data formats, coding schemes, and adoption of non-standard lists within each EHR. The success of large-scale multicenter ACHD research will require institution-specific data integration efforts.