Data quality in electronic medical records in Manitoba: Do problem lists reflect chronic disease as defined by prescriptions?
Bibliographic record
Abstract
OBJECTIVE: To determine if the problem list (health conditions) in primary care electronic medical records (EMRs) accurately reflects the conditions for which chronic medications are prescribed in the EMR. DESIGN: A retrospective analysis of EMR data. SETTING: Eighteen primary care clinics across rural and urban Manitoba using the Accuro EMR. PARTICIPANTS: Data from the EMRs of active patients seen in an 18-month period (December 18, 2011, to June 18, 2013, or December 3, 2012, to June 3, 2014) were used. MAIN OUTCOME MEASURES: The likelihood of documentation in the EMR problem list of those specific chronic diseases for which drug prescriptions were documented in the EMR. Regression modeling was performed to determine the effect of clinic patient load and remuneration type on the completeness of EMR problem lists. RESULTS: Overall problem-list completeness was low but was highest for diabetes and lowest for insomnia. Fee-for-service clinics generally had lower problem-list completeness than salaried clinics did for all prescription medications examined. Panel size did not affect problem-list completeness rates. CONCLUSION: The low EMR problem-list completeness suggests that this field is not reliable for use in quality improvement initiatives or research until higher reliability has been demonstrated. Further research is recommended to explore the reasons for the poor quality and to support improvement efforts.
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.
How this classification was reachedexpand
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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".