Opportunities, barriers, and remedies for implementing REDCap integration with electronic health records via Fast Healthcare Interoperability Resources (FHIR)
Bibliographic record
Abstract
Objective: Accelerate adoption of clinical research technology that obtains electronic health record (EHR) data through HL7 Fast Healthcare Interoperability Resources (FHIR). Materials and Methods: Based on experience helping institutions implement REDCap-EHR integration and surveys of users and potential users, we discuss the technical and organizational barriers to adoption with strategies for remediation. Results: With strong demand from researchers, the 21st Century Cures Act Final Rule in place, and REDCap software already in use at most research organizations, the environment seems ideal for REDCap-EHR integration for automated data exchange. However, concerns from information technology and regulatory leaders often slow progress and restrict how and when data from the EHR can be used. Discussion and Conclusion: While technological controls can help alleviate concerns about FHIR applications used in research, we have found that messaging, education, and extramural funding remain the strongest drivers of adoption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".