Why is learning from patient safety incidents (still) so hard? A sociocultural perspective on learning from incidents in healthcare organizations
Bibliographic record
Abstract
PURPOSE: Despite robust quality improvement efforts in healthcare, learning from patient safety incidents remains difficult. Our study explores counter-vailing powers shaping learning processes and possibilities in healthcare organizations, with a focus on social, political and organizational dynamics of learning. DESIGN/METHODOLOGY/APPROACH: Deploying concepts of situated curriculum, boundary work and interconnected knowledge practices, we interviewed staff and physicians (n = 15) in a large Academic Health Science Centre in Canada about their experiences of incident investigations and resultant information sharing. Our analytical strategy was abductive, drawing connections to sociology of the professions and learning sciences literature. FINDINGS: Incident investigation and follow-up processes are relatively robust for learning about incidents in the organization. However, learning from incidents remains difficult, complicated by the professional politics of incident classification, counter-vailing policies related to privacy, the organization of improvement work towards reporting, and an organizational focus on incidents with severe outcomes. PRACTICAL IMPLICATIONS: Participants advocated for a broader view of incidents, moving beyond classification and investigation based on severity of outcome to also include incidents that are "learning rich". To that end, we argue for more research on the role of Patient Safety Specialists in organizational learning and more collaborations with learning sciences. ORIGINALITY/VALUE: This paper illuminates ways in which robust information dissemination structures are an important but insufficient condition for learning from incidents. The argument goes beyond a prescriptive approach to learning from incidents to instead explore the competing visions and values implicated with improvement practices.
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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 | 0.038 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.097 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| 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".