Implementing a safer and more reliable system to monitor test results at a teaching university-affiliated facility in a family medicine group: a quality improvement process report
Bibliographic record
Abstract
INTRODUCTION: Prescribers have the medicolegal responsibility to ensure a sufficiently reliable system is in place to securely monitor the process of efficiently communicating laboratory results. With added complexity of technologies such as electronic medical record systems, few studies address the monitoring, verification and improvement of test results follow-up especially within a teaching facility including resident prescribers. METHOD: The main goal of this quality improvement project was to ensure safety of care through reliable test results follow-up and adapting processes to available technology by (1) implementing an improved, more reliable and efficient system for tracking test results in the setting; and (2) increasing perceived reliability of test results monitoring system of prescribers in the clinical setting. Through three Plan-Do-Study-Act cycles, changes were implemented: (1) family medicine residents recognised as prescribers; (2) connection of prescribers to regional techno-centre; and (3) computer protocol eliminating duplicates. Patients and clinical staff completed surveys (satisfaction, perceived safety and reliability). ANALYSIS: Quantitative and qualitative data were collected, reported incidents, requested prescriptions and received results and time spent communicating normal results. Immediate feedback from prescribers and staff members was considered to improve the process. Microsoft Excel software was used to calculate mean and SD of error rate. Shewhart chart rules were used to determine special cause of change and sustainability. RESULTS: Implemented changes led to decrease in mean error rate (from 6.1% to 1.9%), variation of range (from 2.7-12.1% to 0-4.8%) and SD (from 2.1% to 1.2%). The improvement is sustained over 24 months after the last cycle. 100% of the 30 patients surveyed were satisfied with the changes implemented. Prescribers (75% response rate) including residents (15.8% response rate) perceived the improved system to be safer, more reliable and efficient. CONCLUSION: Implemented changes improved reliability, efficiency and perceived safety of the test results monitoring system while ensuring patient satisfaction.
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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 | 0.054 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".