Data quality of electronic medical records in Manitoba: do problem lists accurately reflect chronic disease billing diagnoses?
Bibliographic record
Abstract
OBJECTIVE: To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness. METHODS: A retrospective analysis of primary care EMR data quality related to seven chronic diseases (hypertension, diabetes, asthma, congestive heart failure, coronary artery disease, hypothyroidism, and chronic obstructive pulmonary disorder) in Manitoba, Canada. We included 119 practices in 18 primary care clinics across urban and rural Manitoba. The main outcome measure was EMR problem list completeness. Completeness was measured by comparing the number of EMR-documented diagnoses to the number of billings associated with each disease. We calculated odds ratios for the effect of clinic patient load and salary type on EMR problem list completeness of the 7 chronic diseases. RESULTS: Completeness of EMR problem list for each disease varied widely among clinics. Factors that significantly affected EMR problem list completeness included the primary care provider, the patient load, and the clinic's funding and organization model (ie, salaried, fee-for-service, or residency training clinics). Average rates of completeness were: hypertension, 72%; diabetes, 80%; hypothyroidism, 63%; asthma, 56%; chronic obstructive pulmonary disorder, 43%; congestive heart failure, 54%; and coronary artery disease, 64%. CONCLUSION: This study demonstrates the high variability but generally low quality of problem lists (health condition records) related to 7 common chronic diseases in EMRs. There are systematic physician- and clinic-level factors associated with low data quality completeness. This information may be useful to support improvement in EMR data quality in primary 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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".