Prioritizing Threats to Patient Safety in Rural Primary Care
Bibliographic record
Abstract
CONTEXT: Rural primary care is a complex environment in which multiple patient safety challenges can arise. To make progress in improving safety with limited resources, each practice needs to identify those safety problems that pose the greatest threat to patients and focus efforts on these. PURPOSE: To describe and field-test a novel approach to prioritizing safety problems in rural primary care based on the method of Failure Modes and Effects Analysis. METHODS: A survey instrument designed to assess perceptions of medical error frequency, severity, and cause was administered anonymously to staff of 2 rural primary care practices in New York State. Responses were converted to quantitative hazard scores, which were used to make priority rankings of safety problems. Concordance analysis was conducted. RESULTS: Response rate was 94% at each site. Analysis yielded a list of priorities for each site. Comparison between staff groups (provider vs nursing vs administration), based on the top 10 priorities perceived by staff, showed 53% concordance at one site and 30% at the other. Concordance between sites was lower, at 20%. CONCLUSIONS: Initial field-testing of a Failure Modes and Effects Analysis approach in rural primary care suggests that it is feasible and can be used to estimate, based on staff perceptions, the greatest threats to patient safety in an individual practice so that limited resources can be focused appropriately. Higher concordance between staff within a practice than between practices lends preliminary support to the validity of the approach.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".