Ziyi Wang, Lijia Wei, Lian Xue
arXiv 17 May 2024 · General Economics · 1 citations (OpenAlex)
arXiv:2405.10539 · PDF · DOI · OpenAlex · Extracted main text
This study evaluates the effectiveness of Artificial Intelligence (AI) in mitigating medical overtreatment, a significant issue characterized by unnecessary interventions that inflate healthcare costs and pose risks to patients. We conducted a lab-in-the-field experiment at a medical school, utilizing a novel medical prescription task, manipulating monetary incentives and the availability of AI assistance among medical students using a three-by-two factorial design. We tested three incentive schemes: Flat (constant pay regardless of treatment quantity), Progressive (pay increases with the number of treatments), and Regressive (penalties for overtreatment) to assess their influence on the adoption and effectiveness of AI assistance. Our findings demonstrate that AI significantly reduced overtreatment rates by up to 62% in the Regressive incentive conditions where (prospective) physician and patient interests were most aligned. Diagnostic accuracy improved by 17% to 37%, depending on the incentive scheme. Adoption of AI advice was high, with approximately half of the participants modifying their decisions based on AI input across all settings. For policy implications, we quantified the monetary (57%) and non-monetary (43%) incentives of overtreatment and highlighted AI's potential to mitigate non-monetary incentives and enhance social welfare. Our results provide valuable insights for healthcare administrators considering AI integration into healthcare systems.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | agrawal2022power APACrefauthors Agrawal, A. , Gans, J. \ Goldfarb, A… (2022) 2022 | 0.843 | 3 | 3 | 100% |
| 2 | brownlee2017evidence APACrefauthors Brownlee, S. , Chalkidou, K. , D… (2017) 2017 | 0.585 | 3 | 1 | 100% |
| 3 | korenstein2012overuse APACrefauthors Korenstein, D. , Falk, R. , How… (2012) 2012 | 0.585 | 3 | 1 | 100% |
| 4 | al2023review APACrefauthors Al Kuwaiti, A. , Nazer, K. , Al-Reedy, A… (2023) 2023 | 0.405 | 1 | 1 | 100% |
| 5 | albarqouni2023overuse APACrefauthors Albarqouni, L. , Palagama, S. ,… (2023) 2023 | 0.405 | 1 | 1 | 100% |
| 6 | almog2024ai APACrefauthors Almog, D. , Gauriot, R. , Page, L. \ Mart… (2024) 2024 | 0.405 | 1 | 1 | 100% |
| 7 | bishop2010physicians APACrefauthors Bishop, T F. , Federman, A D. \… (2010) 2010 | 0.405 | 1 | 1 | 100% |
| 8 | blais2006domain APACrefauthors Blais, A R. \ Weber, E U. APACrefauth… (2006) 2006 | 0.405 | 1 | 1 | 100% |
| 9 | burton2020systematic APACrefauthors Burton, J W. , Stein, M K. \ Jen… (2020) 2020 | 0.405 | 1 | 1 | 100% |
| 10 | carpenter1990one APACrefauthors Carpenter, P A. , Just, M A. \ Shell… (1990) 1990 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 64 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Statistical tests for replacing human decision makers with algorithms | 0.405 | 1 | 1 |