Data Quality in Primary Care Electronic Medical Records in Manitoba
Bibliographic record
Abstract
Background: Evaluation of primary care EMR data quality is crucial since data must be of high quality in order to maximize patient care and use databases for secondary purposes including improved chronic disease management. Completeness evaluates data for gaps that may limit it's ability to represent what it should. This study aims to evaluate the baseline problem list completeness for Manitoba primary care EMRs. Methods: We conducted a retrospective analysis of the QHR Accuro® EMR database within 9 salaried Winnipeg Regional Health Authority (WRHA} and 3 fee for service primary care clinics in Manitoba. Queries were designed in the Accuro® EMR query builder. Aggregates were used to calculate sensitivity as a measure of completeness. The seven chronic diseases evaluated include, hypertension, diabetes, hypothyroidism, asthma. COPD. CHF, and CAD. Only searchable, structured data with ICD-9 coding was assessed. The 12 clinic types were divided into four categories; teaching, access centres, community, and fee for service and mean completeness was calculated for each. One way AN OVA and post hoc contrast analyses were conducted to identify differences between salaried and fee for service clinics. Results: Fee for service clinics exhibited significantly lower problem list completeness rates than salaried clinics for hypothyroidism, asthma, COPD, and CAD. Sensitivities calculated for each disease were significantly worse than those reported from previous UK research. Conclusion: This study demonstrates the need for better understanding of data quality in Canada and improvements so that primary care data can be reliably used for secondary purposes.
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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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".