The Clinical Area Safety Assessment, a Peer Review of Safety across an Acute Trust
Bibliographic record
Abstract
Background Most hospitals have developed processes to manage risk in a reactive manner. Few, however, have instituted proactive systems for the identification of latent risk that has the potential to cause harm. Objective To develop a process for the identification of potential risks to safety across all clinical areas of a hospital. Methods Every clinical department in Cambridge University Hospitals Foundation Trust (CUHFT) underwent peer assessment to confirm that the Trust’s processes for safety were in place and identify possible threats to the safety of patients. This assessment consisted of a number of elements that included review of routinely collected data, observed clinical care and a safety questionnaire. The methodology used to apply this process is described. Results The outcomes of 33 clinical area safety assessments (CASA) are reported. No department was awarded unconditional accreditation nor have any had their service suspended. A number of recurrent issues emerged, the most common being that 31% of departments failed to fully comply with Trust requirements for governance and 12% needed to improve compliance with patient safety standards. Concerns related to documentation were identified in 11% of assessments. To date the programme has cost approximately £111,000 with each review requiring approximately 130 hrs to complete. Conclusions The CASA programme has offered an opportunity to improve standardisation in governance and optimise safety processes across our hospital. It has facilitated the dissemination of good practice amongst teams to resolve common problems. Suggestions as to how we plan to further refine this peer review process are offered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".