Evaluation of the Type and Frequency of Errors Discovered During Routine Secondary Patient Chart Review
Bibliographic record
Abstract
Purpose: Desire to improve efficiency and throughput inspired a review of the frequency and scope of our physics chart check procedures. Departmental policy mandates review of a patient’s treatment plan prior to port-filming, after first treatment and “weekly” every 3-5 fractions. This study examined the effectiveness of the “after-first” physics check with respect to improving patient safety and clinical efficiency.Methods and Materials: A shared spreadsheet was created to record errors discovered during patient-specific chart review following the first fraction of treatment and before the second fraction. First, entries were recorded and categorized from August 2014 through February 2015. Frequencies were assessed month-to-month. Next, utilizing thes e results, a continuous quality improvement (CQI) process following Deming’s Plan-Do-Study-Act (PDSA) methodology was generated. The first iteration of this PDSA was adding a dose tracking checklist item in the pre-treatment plan check assessment. A two-sided Fisher’s exact test was used to determine if there was a nonrandom association between the checklist implementation and incidence of dose tracking errors.Results: Analysis of recorded errors indicated an overall error rate of 3.4% over the 13 month period. The majority of errors related to discrepancies in documentation, followed by prescription, plan deficiency, and dose tracking-related errors. A two-sided Fisher’s exact test revealed a statistically significant decrease in dose tracking-related errors after implementing the checklist item (p = 0.0322, significance level = 0.05). Conclusions: This work indicates that this redundant secondary check is an effective QA process in our department. The first month spike in rates could be due to the Hawthorne/observer effect, but the consistent 3% error rate suggests the need for continuous quality improvement and periodical re-training on errors noted as frequent to improve awareness and quality of the initial chart review process, which may lead to improved treatment quality, patient safety and increased clinical efficiency.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".