Accuracy and completeness of electronic medical records obtained from referring physicians in a Hamilton, Ontario, plastic surgery practice: A prospective feasibility study
Bibliographic record
Abstract
Objective To assess the feasibility of auditing electronic medical records (EMRs) in plastic surgery for future large-scale research studies. The secondary objective was to ascertain the accuracy and completeness of EMRs accompanying referral requests by physicians for plastic surgery consultation between July and December 2013. Methods EMRs of 30 patients were reviewed and crosschecked independently by two reviewers and subsequently verified by a third reviewer using predefined criteria to determine whether they were accurate and/or complete. Descriptive analysis was performed to calculate the frequency of inaccuracies and incompleteness for each EMR information field. Information fields were compared to assess whether the frequency of inaccuracies and incompleteness varied. Results Of the 270 information fields reviewed, four (1.48%) were inaccurate and 66 (24.4%) were incomplete. The most common field of inaccuracy was current medications, followed by medical history and medical allergies. The most common field of incompleteness was history of presenting illness followed by surgical history. Conclusion Despite their purported benefits, inaccuracies and incompleteness are a frequently occurring problem in EMRs. A large-scale study may be beneficial in determining the efficacy of EMRs in the future.
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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 | 0.019 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".