Mortality as an indicator of patient safety in orthopaedics: lessons from qualitative analysis of a database of medical errors
Bibliographic record
Abstract
BACKGROUND: Orthopaedic surgery is a high-risk specialty in which errors will undoubtedly occur. Patient safety incidents can yield valuable information to generate solutions and prevent future cases of avoidable harm. The aim of this study was to understand the causative factors leading to all unnecessary deaths in orthopaedics and trauma surgery reported to the National Patient Safety Agency (NPSA) over a four-year period (2005-2009), using a qualitative approach. METHODS: Reports made to the NPSA are categorised and stored in the database as free-text data. A search was undertaken to identify the cases of all-cause mortality in orthopaedic and trauma surgery, and the free-text elements were used for thematic analysis. Descriptive statistics were calculated based on the incidents reported. This included presenting the number of times categories of incidents had the same or similar response. Superordinate and subordinate categories were created. RESULTS: A total of 257 incident reports were analysed. Four main thematic categories emerged. These were: (1) stages of the surgical journey - 118/191 (62%) of deaths occurred in the post-operative phase; (2) causes of patient deaths - 32% were related to severe infections; (3) reported quality of medical interventions - 65% of patients experienced minimal or delayed treatment; (4) skills of healthcare professionals - 44% of deaths had a failure in non-technical skills. CONCLUSIONS: Most complications in orthopaedic surgery can be dealt with adequately, provided they are anticipated and that risk-reduction strategies are instituted. Surgeons take pride in the precision of operative techniques; perhaps it is time to enshrine the multimodal tools available to ensure safer patient care.
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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 | 0.048 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| 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".