Patient Safety Incidents in Primary Care Dentistry in England and Wales: A Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: In recent decades, there has been considerable international attention aimed at improving the safety of hospital care, and more recently, this attention has broadened to include primary medical care. In contrast, the safety profile of primary care dentistry remains poorly characterized. OBJECTIVES: We aimed to describe the types of primary care dental patient safety incidents reported within a national incident reporting database and understand their contributory factors and consequences. METHODS: We undertook a cross-sectional mixed-methods study, which involved analysis of a weighted randomized sample of the most severe incident reports from primary care dentistry submitted to England and Wales' National Reporting and Learning System. Drawing on a conceptual literature-derived model of patient safety threats that we previously developed, we developed coding frameworks to describe and conduct thematic analysis of free text incident reports and determine the relationship between incident types, contributory factors, and outcomes. RESULTS: Of 2000 reports sampled, 1456 were eligible for analysis. Sixty types of incidents were identified and organized across preoperative (40.3%, n = 587), intraoperative (56.1%, n = 817), and postoperative (3.6%, n = 52) stages. The main sources of unsafe care were delays in treatment (344/1456, 23.6%), procedural errors (excluding wrong-tooth extraction) (227/1456; 15.6%), medication-related adverse incidents (161/1456, 11.1%), equipment failure (90/1456, 6.2%) and x-ray related errors (87/1456, 6.0%). Of all incidents that resulted in a harmful outcome (n = 77, 5.3%), more than half were due to wrong tooth extractions (37/77, 48.1%) mainly resulting from distraction of the dentist. As a result of this type of incident, 34 of the 37 patients (91.9%) examined required further unnecessary procedures. CONCLUSIONS: Flaws in administrative processes need improvement because they are the main cause for patients experiencing delays in receiving treatment. Checklists and standardization of clinical procedures have the potential to reduce procedural errors and avoid overuse of services. Wrong-tooth extractions should be addressed through focused research initiatives and encouraging policy development to mandate learning from serious dental errors like never events.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".