Utility of Electronic Medical Records in Community Rheumatology Practice for Assessing Quality of Care Indicators for Gout
Bibliographic record
Abstract
OBJECTIVES: With comprehensive capture of information on patient encounters, electronic medical records (EMRs) may have utility for assessing adherence to quality indicators (QIs) in gout. Our objectives were to translate 10 previously established gout QIs into relevant EMR data and evaluate and describe the feasibility of using EMRs to assess gout QIs. METHODS: Using EMRs from 3 community rheumatology practices in Vancouver, British Columbia, Canada, we identified gout patients seen between January 1, 2012, and December 31, 2013. We translated each gout QI into potential EMR variables that would allow identification of patients the QI pertains to and whether the QI could be assessed. We extracted deidentified EMR data on gout diagnosis, medications, laboratory tests, radiological tests, and clinical notes and calculated the percent availability of data for each QI. RESULTS: We included 125 patients with gout, with mean age of 64 ± 17 years and with males comprising 78%. Overall, there were sufficient EMR data to allow translation of 7 QIs and assessment of 6 QIs including therapy-related gout QIs (69%-83% data availability) and one counseling-related QI (8% data availability). The highest percent data availability was observed in the single QI translated into EMR data and assessed based on diagnostic codes and prescription medications and not laboratory tests. CONCLUSIONS: Electronic medical records are promising tools for assessing QIs for gout. It was feasible to translate seven gout QIs into relevant EMR variables and there was sufficient EMR data to feasibly assess six of these QIs -Our findings lend evidence to support the utility of EMRs for ut QI assessment, with implications for helping improve management of this disease.
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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 | 0.023 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".