Achieving the National Quality Forum's “Never Events”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Review the evidence regarding methods to prevent wrong site operations and present a framework that healthcare organizations can use to evaluate whether they have reduced the probability of wrong site, wrong procedure, and wrong patient operations. SUMMARY BACKGROUND DATA: Operations involving the wrong site, patient, and procedure continue despite national efforts by regulators and professional organizations. Little is known about effective policies to reduce these "never events," and healthcare professional's knowledge or appropriate use of these policies to mitigate events. METHODS: A literature review of the evidence was performed using PubMed and Google; key words used were wrong site surgery, wrong side surgery, wrong patient surgery, and wrong procedure surgery. The framework to evaluate safety includes assessing if a behaviorally specific policy or procedure exists, whether staff knows about the policy, and whether the policy is being used appropriately. RESULTS: Higher-level policies or programs have been implemented by the American Academy of Orthopaedic Surgery, Joint Commission on Accreditation of Healthcare Organizations, Veteran's Health Administration, Canadian Orthopaedic, and the North American Spine Society Associations to reduce wrong site surgery. No scientific evidence is available to guide hospitals in evaluating whether they have an effective policy, and whether staff know of the policy and appropriately use the policy to prevent "never events." CONCLUSIONS: There is limited evidence of behavioral interventions to reduce wrong site, patient, and surgical procedures. We have outlined a framework of measures that healthcare organizations can use to start evaluating whether they have reduced adverse events in operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it