Implementing Comparative Effectiveness Research in Primary Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Despite the increasing uptake of electronic medical record (EMR) software in Primary Care, there has been little effort to date to utilize this software to conduct pragmatic comparative effectiveness research (CER) trials in Primary Care. Objectives: The primary objective of the study was to design an implementation framework composed of key self-reflective questions and a prototype patient recruitment interface to aid in CER studies in Primary Care using current-generation EMR products. Research Questions: What is the current state of EMR usage for CER in Primary Care? What are the barriers (technological, methodological, ethical and practical) to implementing CER in Primary Care? Methods: We incorporated selected key stakeholders in discussions to improve on an initial CER framework prototype and “sham” EMR module for patient recruitment. We iterated on both after discussions with each participant. Participants included researchers with an interest in Primary Care research, technical representatives of EMR vendors, and Family Physicians. Results: There was little familiarity and no apparent impetus from the vendor to collaborate in this type of research. There is a common theme of frustration from researchers directed at the difficulty in access EMR databases from a large field of vendors. From the clinician side, physicians are generally reluctant to participate in CER research without effective compensation for time spent. Patient recruitment interfaces should be designed to be as simple and straightforward as possible. Conclusion: There are currently multiple barriers to conducting EMR-enabled research in Primary Care. The largest and most important barrier is the lack of effective IT infrastructure to support this type of research. Although this type of research is overall more cost-effective, there are significant upfront costs in creating the initial study infrastructure that private vendors are unlikely to bear themselves. Ideally, government would step forward and implement the backend infrastructure with which EMR vendors can interface to help enable this type of research. In the future, researchers will need to clearly outline the business case for vendors to participate in Primary Care research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.008 |
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 it