Using your electronic medical record for research: a primer for avoiding pitfalls
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
In Canada, use of electronic medical records (EMRs) among primary health care (PHC) providers is relatively low. However, it appears that EMRs will eventually become more ubiquitous in PHC. This represents an important development in the use of health care information technology as well as a potential new source of PHC data for research. However, care in the use of EMR data is required. Four years ago, researchers at the Centre for Studies in Family Medicine, The University of Western Ontario created an EMR-based research project, called Deliver Primary Health Care Information. Implementing this project led us to two conclusions about using PHC EMR data for research: first, additional time is required for providers to undertake EMR training and to standardize the way data are entered into the EMR and second, EMRs are designed for clinical care, not research. Based on these experiences, we offer our thoughts about how EMRs may, nonetheless, be used for research. Family physician researchers who intend to use EMR data to answer timely questions relevant to practice should evaluate the possible impact of the four questions raised by this paper: (i) why are EMR data different?; (ii) how do you extract data from an EMR?; (iii) where are the data stored? and (iv) what is the data quality? In addition, consideration needs to be given to the complexity of the research question since this can have an impact on how easily issues of using EMR data for research can be overcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it