Does Machine Learning Automate Moral Hazard and Error?
Open accessAbstract
Machine learning tools are beginning to be deployed en masse in health care. While the statistical underpinnings of these techniques have been questioned with regard to causality and stability, we highlight a different concern here, relating to measurement issues. A characteristic feature of health data, unlike other applications of machine learning, is that neither y nor x is measured perfectly. Far from a minor nuance, this can undermine the power of machine learning algorithms to drive change in the health care system--and indeed, can cause them to reproduce and even magnify existing errors in human judgment.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
The Effect of Race and Sex on Physicians' Recommendations for Cardiac Catheterization
Schulman, K. A., Berlin, J. A., Harless, W., Kerner, J. F., Sistrunk, S., Gersh, B. J., Dubé, R., Taleghani, C. K., Burke, J. E., Williams, S., et al. (1999) · New England Journal of Medicine · doi:10.1056/nejm199902253400806 · Cited by 1,934 · see citing works
Racial Differences in the Treatment of Early-Stage Lung Cancer
Bach, P. B., Cramer, L. D., Warren, J. L., & Begg, C. B. (1999) · New England Journal of Medicine · doi:10.1056/nejm199910143411606 · Cited by 991 · see citing works
Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584 · see citing works
Self-citationCognitive Biases and Heuristics in Medical Decision Making
Blumenthal-Barby, J. S. S., & Krieger, H. (2014) · Medical Decision Making · doi:10.1177/0272989x14547740 · Cited by 554 · see citing works
Mamede, S., van Gog, T., van den Berge, K., Rikers, R. M. J. P., van Saase, J. L. C. M., van Guldener, C., & Schmidt, H. G. (2010) · JAMA · doi:10.1001/jama.2010.1276 · Cited by 374 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Halevy Alon Norvig Peter Pereira Fernando The unreasonable effectiveness of data IEEE Intelligent Systems 2009 24 2 8 12
Cited by
Sorted by citations
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works
Self-citationZitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works
Lost in thought—the limits of the human mind and the future of medicine
Obermeyer, Z., & Lee, T. H. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1705348 · Cited by 252 · see citing works
Self-citationRegulation of predictive analytics in medicine
Parikh, R. B., Obermeyer, Z., & Navathe, A. S. (2019) · Science · doi:10.1126/science.aaw0029 · Cited by 223 · see citing works
Self-citationMachine learning for environmental monitoring
Hino, M., Benami, E., & Brooks, N. (2018) · Nature Sustainability · doi:10.1038/s41893-018-0142-9 · Cited by 201 · see citing works
A contemporary review of machine learning in otolaryngology–head and neck surgery
Crowson, M. G., Ranisau, J., Eskander, A., Babier, A., Xu, B., Kahmke, R. R., Chen, J. M., & Chan, T. C. Y. (2020) · The Laryngoscope · doi:10.1002/lary.27850 · Cited by 127 · see citing works
Banker, S., & Khetani, S. (2019) · Journal of Public Policy & Marketing · doi:10.1177/0743915619858057 · Cited by 120 · see citing works
Cabitza, F., & Zeitoun, J.-D. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.04.07 · Cited by 72 · see citing works
Wang, H. E., Landers, M., Adams, R., Subbaswamy, A., Kharrazi, H., Gaskin, D. J., & Saria, S. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac065 · Cited by 63 · see citing works
Solving medicine's data bottleneck: Nightingale Open Science
Mullainathan, S., & Obermeyer, Z. (2022) · Nature Medicine · doi:10.1038/s41591-022-01804-4 · Cited by 43 · see citing works
Cui, X., Chang, Y., Yang, C., Cong, Z., Wang, B., & Leng, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010050 · Cited by 19 · see citing works
- Authors
- Mullainathan, S., & Obermeyer, Z.
- Affiliations
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2017-05-01
- Volume
- 107
- Issue
- 5
- Pages
- 476-480
- ISSN
- 0002-8282 (ISSN-L)
- PMC
- PMC5540263
- OpenAlex
- W2615995104
- Cited by
- 156
- References
- 6
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 18 · 2023: 13 · 2022: 22 · 2021: 23 · 2020: 20 · 2019: 22 · 2018: 7 · 2017: 3
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Economic Review (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health (DP5OD012161)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- measurement error, moral hazard, algorithmic bias, health data quality, algorithmic decision-making, healthcare quality improvement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
