Adoption of a laboratory EMR system and inappropriate laboratory testing in Ontario: a cross-sectional observational study
Bibliographic record
Abstract
BACKGROUND: Electronic medical record (EMR) systems have the potential to facilitate appropriate laboratory testing. We examined three common medical tests in primary care-hemoglobin A1c (HbA1c), lipid, and thyroid stimulating hormone (TSH)- to assess whether adoption of a laboratory EMR system in Ontario had an impact on the rate of inappropriate testing among primary care physicians. METHODS: We used FY2016-17 population-level laboratory data to estimate the association between adoption of a laboratory EMR system and the rate of inappropriate testing. Inappropriate testing was assessed based on recommendations for screening, monitoring, and follow-up that take into account risk factors related to patient age and certain clinical conditions. To overcome the problem of potential endogeneity of physician choice to use the EMR, the EMR penetration rate in the physician's geographical area of practice was used as an instrumental variable in an ordinary least squares (OLS) regression. We then simulated the change in the rate of inappropriate testing, by physician payment model, as the EMR penetration rate increased from the baseline percentage. RESULTS: The simulation models showed that an increase in the rate of EMR penetration from a baseline average was associated with a statistically significant decrease in inappropriate hbA1c and lipid testing, but a statistically insignificant increase in inappropriate TSH testing. The impact of EMR penetration also varied by payment model. CONCLUSIONS: This study demonstrated a positive association between availability of an EMR system and appropriate service utilization. Varying impacts of the EMR system availability by primary care payment model may be reflective of different incentives or attributes inherent in payment models. Policies to encourage physicians to increase their use of laboratory EMR systems could improve the quality and continuity of patient care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".