High reliability in healthcare: creating the culture and mindset for patient safety
Bibliographic record
Abstract
Occurrences of patient harm in healthcare represent a significant burden, with serious implications for patients and families and for the capacity of health systems to manage patient access, flow, and wait times. Interest in the science of high reliability, developed originally in industries such as commercial airlines that have demonstrated exceptional safety records, is an emerging trend in healthcare with the potential to help organizations and systems achieve the ultimate goal of zero patient harm. This article argues that zero patient harm is a fundamental imperative, and that high-reliability science can help to accelerate and sustain progress toward this vital goal. Although the practices used in other industries are not readily transferable to healthcare, and no single proven model for High Reliability Organizations in healthcare is yet available, leading organizations are beginning to demonstrate effective healthcare-specific strategies. Experience from Studer Group’s international network of partner organizations is used to illustrate and understand these early efforts. Studer Group’s Evidence-Based Leadership SM framework is applied in diverse healthcare settings to provide a foundation of culture transformation and change management to support high reliability. It offers an approach and resources for moving forward toward the goal of zero patient harm, with concurrent benefits related to the efficient use of our valuable healthcare resources.
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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 | 0.055 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.014 |
| 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".