Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The delivery of standardized self-report assessments is essential for measurement-based care in mental health. Paper-based methods of measurement-based care data collection may result in transcription errors, missing data, and other data quality issues when entered into patient electronic health records (EHRs). OBJECTIVE This study aims to help address these issues by using a dedicated instance of REDCap (Research Electronic Data Capture; Vanderbilt University)—a free, widely used electronic data capture platform—that was established to enable the deployment of digitized self-assessments in clinical care pathways to inform clinical decision making. METHODS REDCap was integrated with the primary clinical information system to facilitate the real-time transfer of discrete data and PDF reports from REDCap into the EHR. Both technical and administrative components were required for complete implementation. A technology acceptance survey was also administered to capture physicians’ and clinicians’ attitudes toward the new system. RESULTS The integration of REDCap with the EHR transitioned clinical workflows from paper-based methods of data collection to electronic data collection. This resulted in significant time savings, improved data quality, and valuable real-time information delivery. The digitization of self-report assessments at each appointment contributed to the clinic-wide implementation of the major depressive disorder integrated care pathway. This digital transformation facilitated a 4-fold increase in the physician adoption of this integrated care pathway workflow and a 3-fold increase in patient enrollment, resulting in an overall significant increase in major depressive disorder integrated care pathway capacity. Physicians’ and clinicians’ attitudes were overall positive, with almost all respondents agreeing that the system was useful to their work. CONCLUSIONS REDCap provided an intuitive patient interface for collecting self-report measures and accessing results in real time to inform clinical decisions and an extensible backend for system integration. The approach scaled effectively and expanded to high-impact clinics throughout the hospital, allowing for the broad deployment of complex workflows and standardized assessments, which led to the accumulation of harmonized data across clinics and care pathways. REDCap is a flexible tool that can be effectively leveraged to facilitate the automatic transfer of self-report data to the EHR; however, thoughtful governance is required to complement the technical implementation to ensure that data standardization, data quality, patient safety, and privacy are maintained. CLINICALTRIAL
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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 | 0.062 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".