Current Limitations of Electronic Health Record Systems in Supporting Pragmatic Clinical Trials: Insights from the eMERGE Consortium
Bibliographic record
Abstract
Pragmatic clinical trials (PCTs) evaluate interventions in real-world settings, often using electronic health records (EHRs) for efficient data collection. We report on the challenges in performing EHR analysis of healthcare provider orders in a PCT within the eMERGE consortium, which investigates the impact of reporting genome-informed risk assessments (GIRA) to over 25,000 patients across 10 academic medical centers. Clinical informaticians conducted a landscape analysis to identify approaches for evaluating the outcomes of GIRA reporting through the EHR. Of 98 identified outcomes, 54 (55.1%) were determined to be difficult to extract because they involved provider orders, which are typically documented in free text or proprietary formats within the EHR and only mapped to standardized codes after the service is completed. These findings highlight a critical barrier in using EHRs to support PCTs. The authors recommend closer collaboration between clinicians and informaticians, improved EHR systems that support standardized order entry, and future use of machine learning to automate analysis of provider behavior in clinical trials.
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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 | 0.509 | 0.710 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".