ArticleThe quarterly journal of economics2022
DIAGNOSING PHYSICIAN ERROR: A MACHINE LEARNING APPROACH TO LOW-VALUE HEALTH CARE.
Article in The quarterly journal of economics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Language-based game theory in the age of artificial intelligence.Journal of the Royal Society, Interface · 2024Pooled it
- Identifying a Heterogeneous Effect of Atrial Fibrillation Screening in Older Adults: A Secondary Analysis of the VITAL-AF Trial.Circulation. Cardiovascular quality and outcomes · 2025Trial
- Algorithmic decision-making in health care: Evidence from post-acute care in Medicare Advantage.Journal of health economics · 2025Article
- Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.Population health metrics · 2025Article
- Risk stratification of chest pain in the emergency department using artificial intelligence applied to electrocardiograms.Open heart · 2025Article
- Emergency Department Implementation of a Multimodal Electronic Health Record-Integrated Clinical Intervention for High-Sensitivity Troponin Testing Increases Diagnostic Efficiency.Journal of the American College of Emergency Physicians open · 2025Article
- A publicly available benchmark for assessing large language models' ability to predict how humans balance self-interest and the interest of others.Scientific reports · 2025Article
- A machine learning model using clinical notes to identify physician fatigue.Nature communications · 2025Article
- Potential Diagnostic Error for Emergency Conditions, Mortality, and Healthy Days at Home.JAMA network open · 2025Article
- Medicaid claims alone have high sensitivity but low specificity in identifying child abuse and neglect.Frontiers in pediatrics · 2025Article
- Paying for advance care planning in medicare: Impacts on care and spending near end of life.Journal of health economics · 2024Article
- The Health Costs of Cost Sharing.The quarterly journal of economics · 2024Article
- Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions.Proceedings of machine learning research · 2024Article
- Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.Journal of the American College of Cardiology · 2024Review
- Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias.medRxiv : the preprint server for health sciences · 2024Article
- Geographic Variation in Cesarean Sections in the United States: Trends, Correlates, and Other Interesting Facts.Journal of labor economics · 2024Article
- Risk scores, label bias, and everything but the kitchen sink.Science advances · 2024Article
- Deep learning-aided decision support for diagnosis of skin disease across skin tones.Nature medicine · 2024Article
- Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing.Quantitative marketing and economics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
How effective are physicians at diagnosing heart attacks? To answer this question, we contrast physician testing decisions with a machine learning model of risk. When the two deviate, we use actual health outcome data to judge whether the algorithm or the physician was right. We find physicians over-test: tests that are predictably useless are still performed. At the same time, physicians also under-test: many predicted high-risk patients are untested and then suffer adverse health events (including death) at high rates. A natural experiment using shift-to-shift testing variation confirms these findings: increasing testing improves health and reduces mortality, but only for patients flagged as high-risk by the algorithm. The simultaneous existence of over- and under-testing cannot easily be explained by incentives alone, and instead suggests errors. We provide suggestive evidence on the psychology behind these errors:(i) physicians use too simple a model of risk, suggesting bounded rationality; (ii) they over-weight salient information; and (iii) they over-weight symptoms that are representative or stereotypical of heart attack. Together, these results suggest the need for health care models and policies to incorporate not just physician incentives, but also physician mistakes.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.