Estimating deaths due to medical error: the ongoing controversy and why it matters
Bibliographic record
Abstract
One important reason for the widespread attention given to the 1999 US Institute of Medicine (IOM) report To Err Is Human 1 lie in its estimate that medical error was to blame for 44 000–98 000 deaths each year in the US hospitals. This striking claim established patient safety as a public concern, strengthened the case for improving the science underlying safety and motivated providers, policymakers, payers and regulators to take safety seriously. Some did express disquiet about the validity of the figures cited,2 including one of the principal investigators of the two studies that provided the data for these estimates.3 A decade and a half later, Makary and Daniel4 attribute an even higher toll to medical error: 251 454 deaths in US hospitals per year, making, they say, medical error the third-leading cause of death in the USA. Unsurprisingly, this claim generated widespread coverage in multiple media channels. It also ignited scientific controversy about the basis of the estimate and the role of mortality as a patient safety indicator (PSI). In this paper, we address this controversy and why it matters. We propose that the new estimate is very likely to be wrong. Not only is it wrong, it risks undermining rather than strengthening the cause of patient safety. Though the paper by Makary and Daniel was widely cited as ‘a study’, it presented no new data nor did it use formal methods to synthesise the data it used from previous studies. The authors simply took the arithmetic average of four estimates since the publication of the IOM report, including one from HealthGrades,5 a for-profit company that markets quality and safety ratings, a report from the US Office of the Inspector General (OIG)6 and two peer-reviewed articles (table 1).7 ,8 The paper did not apply …
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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 | 0.175 | 0.472 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".