Accountability sought by patients following adverse events from medical care: the New Zealand experience
Bibliographic record
Abstract
BACKGROUND: Unlike Canada's medical malpractice system, patients in New Zealand who are dissatisfied with the quality of their care may choose between 2 well-established medicolegal paths: one leads to monetary compensation and the other to nonmonetary forms of accountability. We compared the forms of accountability sought by patients and families in New Zealand who took different types of legal action following a medical injury. This study offers insights into the forms of accountability sought by injured patients and may help to inform tort-reform initiatives. METHODS: We reviewed compensation claims submitted to the Accident Compensation Corporation (ACC), New Zealand's national no-fault insurer, following injuries associated with admission to a public hospital in 1998 (n = 582). We also reviewed complaint letters (n = 254) submitted to the national Health and Disability Commissioner (HDC) that same year to determine the forms of accountability sought by injured patients. We used univariable and multivariable analyses to compare sociodemographic and socioeconomic characteristics of patients who sought nonmonetary forms of accountability with those of patients who claimed compensation. RESULTS: Of 154 injured patients whose complaints were sufficiently detailed to allow coding, 50% sought corrective action to prevent similar harm to future patients (45% system change, 6% review of involved clinician's competence) and 40% wanted more satisfying communication (34% explanation, 10% apology). The odds that patients would seek compensation were significantly increased if they were in their prime working years (aged between 30 and 64 years) (odds ratio [OR] 1.66, 95% confidence interval [CI] 1.14-2.41) or had a permanent disability as a result of their injury (OR 1.75, 95% CI 1.14-2.70). When injuries resulted in death, the odds of a compensation claim to the ACC were about one-eighth those of a complaint to the HDC (OR 0.13, 95% CI 0.08-0.23). INTERPRETATION: Injured patients who pursue medicolegal action seek various forms of accountability. Compensation is important to some, especially when economic losses are substantial (e.g., with injury during prime working years or severe nonfatal injuries). However, others have purely nonmonetary goals, and ensuring alternative options for redress would be an efficient and effective response to their needs.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".