Enabling Laboratory Medicine in Primary Care Through IT Systems Use
Bibliographic record
Abstract
Important problems remain regarding the efficiency and quality of laboratory testing in primary care. In view of this, a significant function of electronic medical record (EMR) systems is to enable the practice of laboratory medicine by primary care physicians. The present study aims to deepen our understanding of the nature and extent of physicians' use of EMR and other laboratory information exchange systems for patient management and care within the laboratory testing process. We conducted a survey of 684 Canadian family physicians. Results indicate that physicians use 84 percent of the laboratory functionalities available in their EMR system. The two most important impacts are the ability to gain time in the post-analytical phase and to take faster action in this same phase as they follow-up on their patients' test results. Physicians who perceive to benefit most from their EMR use are those who make the most extensive use of their system. Extended use of an EMR system allows primary care physicians to better ascertain and monitor the health status of their patients, verify their diagnosis assumptions, and, if their system includes a clinical decision support module, apply evidence-based practices in laboratory medicine.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".