Pathology quality improvement in British Columbia using performance measurement and knowledge mobilization.
Bibliographic record
Abstract
54 Background: Pathology reporting content and formatting varies considerably in British Columbia (BC). The impact of this variability on the quality of care is unknown but it hinders effective data collection and measurement to guide quality improvement. A knowledge mobilization project that digitalized standardized reporting would ensure report completeness and generation of comparative performance data could be used within communities of practice to facilitate peer-to-peer conversations and create action plans to improve patient care. Methods: In 2014 BC adopted the College of American Pathologists cancer checklists as the standard for synoptic reporting using a single standalone electronic synoptic reporting program which interfaced with all five provincial laboratory information systems. Using the tool ensures all mandatory clinical prognostic indicators are reported. Discrete data elements were transmitted to a central data repository (CDR) which was mined to compile key performance indices reports for individual pathologists and generate comparative data reports at the institutional, regional and provincial level. Results: In the first six months of its implementation there was variation from 11-94% across different laboratories in their adoption of the reporting tool. The goal is to increase the average adoption rate from 76% to 90%. Analysis of data showed a highly comparable pattern of pathology practice across the major cancer sites/types and comparable to other Canadian provinces and the literature. Advisory committee reviews with interdisciplinary discussion of comparative data reports revealed potential areas for clinical improvement. For example, significant variation in the number of lymph nodes in colorectal resections was identified with some sites falling short of published recommendations. Conclusions: Population-based standardized digitalized pathology data reporting with centralized data collection can be achieved. Analysis of this data enables monitoring of performance metrics and meaningful discussions via communities of practice identify areas for quality improvement and create action plans.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".