The epidemiology of malpractice claims in primary care: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The aim of this systematic review was to examine the epidemiology of malpractice claims in primary care. DESIGN: A computerised systematic literature search was conducted. Studies were included if they reported original data (≥10 cases) pertinent to malpractice claims, were based in primary care and were published in the English language. Data were synthesised using a narrative approach. SETTING: Primary care. PARTICIPANTS: Malpractice claimants. PRIMARY OUTCOME: Malpractice claim (defined as a written demand for compensation for medical injury). We recorded: medical misadventure cited in claims, missed/delayed diagnoses cited in claims, outcome of claims, prevalence of claims and compensation awarded to claimants. RESULTS: Of the 7152 articles retrieved by electronic search, a total of 34 studies met the inclusion criteria and were included in the narrative analysis. Twenty-eight studies presented data from medical indemnity malpractice claims databases and six studies presented survey data. Fifteen studies were based in the USA, nine in the UK, seven in Australia, one in Canada and two in France. The commonest medical misadventure resulting in claims was failure to or delay in diagnosis, which represented 26-63% of all claims across included studies. Common missed or delayed diagnoses included cancer and myocardial infarction in adults and meningitis in children. Medication error represented the second commonest domain representing 5.6-20% of all claims across included studies. The prevalence of malpractice claims in primary care varied across countries. In the USA and Australia when compared with other clinical disciplines, general practice ranked in the top five specialties accounting for the most claims, representing 7.6-20% of all claims. However, the majority of claims were successfully defended. CONCLUSIONS: This review of malpractice claims in primary care highlights diagnosis and medication error as areas to be prioritised in developing educational strategies and risk management systems.
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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 | 0.016 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".