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Record W2757627704

The Medical Malpractice Landscape in Ontario: Facts, Trends and Analysis of Trials and Appeals

2017· article· en· W2757627704 on OpenAlexaffabout
Erik S. Knutsen

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsQueen's University
Fundersnot available
KeywordsAppealMalpracticeJuryMedical malpracticeLawCausationTrial courtPolitical scienceMedicineCivil procedure
DOInot available

Abstract

fetched live from OpenAlex

This study presents comprehensive analyzed data about medical malpractice trials and appeals in the Ontario civil court system over a 24-year period, from 1992 to 2016. The study looks at the trends in this population of cases with the hopes that there is, for medical malpractice litigants, some predictive value in at least knowing the facts. The study examined not only success rates for parties in these cases but also other trends such as which fundamental legal issues were pursued, how certain issues fared on appeal, the impact of juries, and legal cost trends. The study concludes by offering some insight into what trends occurred in those cases and how that information might inform future medical malpractice litigation decisions. Highlights of the data include: Medical malpractice cases comprise a tiny proportion of civil matters dealt with by Ontario courts – about 0.06% of all civil proceedings and about 0.6% of matters dealt with by the Court of Appeal for Ontario. Patients in Ontario were successful in about 30% of medical malpractice judge-alone trials during the study period, from 1992 to 2016. The Court of Appeal for Ontario heard roughly 5 medical malpractice appeals a year. These cases were most commonly non-jury cases involving a surgical or obstetrics injury to the patient. Issues about standard of care or causation predominated as the issue about which a party appealed the trial result. Patients were successful in less than one-quarter of appeals about informed consent negligence. Physician appellants were successful in having the Court of Appeal allow an appeal in 37% of physician-launched appeals. Patient appellants were successful in having the Court allow 12% of patient-launched appeals. Only 7 patient-launched appeals in the twenty-four year study period were allowed by the Court, and 5 of those appeals ordered a new trial. Physician respondents were successful in keeping a result at trial at least 88% of the time in the study period, while patient respondents were successful at least 63% of the time. More than half of the appeals were allowed due to a factual error at trial. When an appeal was allowed due to a legal error in patient-launched appeals, the issue typically involved a fundamental legal error like applying doctrine backwards. In healthcare provider-launched appeals, the legal error was more circumscribed and academic. Delay in diagnosis and treatment was a common argument in more than one-third of the physician-launched allowed appeals. The Court of Appeal did not hear many appeals from jury trials during the time period (14%). Patients who were unsuccessful on appeal were directly ordered to pay costs only one-third of the time. In 45% of the appeals brought by patients, the Court ordered costs “if demanded.” The median cost award was $25,000. Leave to Appeal to the Supreme Court of Canada was sought in one-quarter of the total population of medical malpractice appeals. Unsuccessful patients were almost twice as likely (40%) to seek Leave to Appeal than healthcare providers (26%) when they lost on appeal. Yet if a healthcare provider lost on appeal, the healthcare provider sought leave 86% of the time. Over the twenty-four year study period, the Supreme Court of Canada granted Leave to Appeal in only one case.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.019
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.070
GPT teacher head0.457
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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