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Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial
Open accessAbstract
Autonomous artificial intelligence (AI) promises to increase healthcare productivity, but real-world evidence is lacking. We developed a clinic productivity model to generate testable hypotheses and study design for a preregistered cluster-randomized clinical trial, in which we tested the hypothesis that a previously validated US FDA-authorized AI for diabetic eye exams increases clinic productivity (number of completed care encounters per hour per specialist physician) among patients with diabetes. Here we report that 105 clinic days are cluster randomized to either intervention (using AI diagnosis; 51 days; 494 patients) or control (not using AI diagnosis; 54 days; 499 patients). The prespecified primary endpoint is met: AI leads to 40% higher productivity (1.59 encounters/hour, 95% confidence interval [CI]: 1.37–1.80) than control (1.14 encounters/hour, 95% CI: 1.02–1.25), p < 0.00; the secondary endpoint (productivity in all patients) is also met. Autonomous AI increases healthcare system productivity, which could potentially increase access and reduce health disparities. ClinicalTrials.gov NCT05182580.
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Decreasing the number of patients requiring specialist time has been shown to increase specialist productivity.
“and has been shown to increase specialist productivity,30”
From Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis (2026) · cites this paper as [30]
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References 27 · Cited by 81
Research works printed but not linked: 28
- Lavizzo-Mourey, R. J., Besser, R. E. & Williams, D. R. Understanding and mitigating health inequities—past, current, and future directions. N. Engl. J. Med. 384, 1681–1684 (2021). · DOI
- Guilbert, J. J. The World Health Report 2006: working together for health. Educ. Health 19, 385–387 (2006). · DOI
- Zurn, P., Dal Poz, M. R., Stilwell, B. & Adams, O. Imbalance in the health workforce. Hum. Resour. Health 2, 13 (2004). · DOI
- Monaco, R. M. & Phelps, J. H. Health care prices, the federal budget, and economic growth. Health Aff. 14, 248–259 (1995). · DOI
- Chen, L. et al. Human resources for health: overcoming the crisis. Lancet 364, 1984–1990 (2004). · DOI
- Hussey, P. S. et al. A systematic review of health care efficiency measures. Health Serv. Res. 44, 784–805 (2009). · DOI
- Backman, J. & Gainsbrugh, M. Productivity and living standards. Ind. Labor Relat. Rev. 2, 163–194 (1949).
- Baumol, W. J. The Cost Disease: Why Computers Get Cheaper and Health Care Doesn’t (Yale University Press, 2012).
- US Bureau of Labor Statistics. Major Sector Productivity and Costs, Vol. 2017 (US Department of Labor, Bureau of Labor Statistics, 2017).
- Kamarainen, V. J., Peltokorpi, A., Torkki, P. & Tallbacka, K. Measuring healthcare productivity—from unit to system level. Int. J. Health Care Qual. Assur. 29, 288–299 (2016). · DOI
- Frakt, A. B. Making health care more productive. JAMA 322, 2274–2275 (2019). · DOI
- Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. Nat. Digit. Med. 1, 39 (2018).
- Abramoff, M. D. et al. A reimbursement framework for artificial intelligence in healthcare. NPJ Digit. Med. 5, 72 (2022). · DOI · Paper page
- Centers for Medicare & Medicaid Services. Proposal to Establish Values for Remote Retinal Imaging (CPT code 92229) (Pages 56ff). Medicare Program; CY 2022 Payment Policies under the Physician Fee Schedule (2021).
- Bevan, G. & Brown, L. D. The political economy of rationing health care in England and the US: the ‘accidental logics’ of political settlements. Health Econ. Policy Law 9, 273–294 (2014). · DOI
- Blumenthal, D., Stremikis, K. & Cutler, D. Health care spending—a giant slain or sleeping? N. Engl. J. Med. 369, 2551–2557 (2013). · DOI
- Cutler, D. M. Where are the health care entrepreneurs? The failure of organizational innovation in health care. Innov. Policy Econ. 11, 1–28 (2011). · DOI
- Oliner, S. D., Sichel, D. E. & Stiroh, K. J. Explaining a Productive Decade. FEDS Working Paper No. 2007-63 (2007). · DOI
- Bhargava, H. K. & Mishra, A. Electronic Medical Records and Physician Productivity: Evidence from Panel Data Analysis (Social Science Research Network, 2011). · DOI
- Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. Am. J. Bioeth. 20, 7–17 (2020). · DOI · Paper page
- Abramoff, M. D. et al. Foundational considerations for artificial intelligence using ophthalmic images. Ophthalmology 129, e14–e32 (2022). · DOI · Paper page
- Channa, R., Wolf, R. M., Abramoff, M. D. & Lehmann, H. P. Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model. NPJ Digit. Med. 6, 53 (2023). · DOI
- Sun, H. et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pr. 183, 109119 (2022). · DOI · Paper page
- Concato, J. & Corrigan-Curay, J. Real-world evidence—where are we now? N. Engl. J. Med. 386, 1680–1682 (2022). · DOI
- Hassin, R. Rational Queueing 1st edn (Chapman and Hall/CRC, 2016). · DOI
- Dai, T. & Tayur, S. Healthcare operations management: a snapshot of emerging research. Manuf. Serv. Oper. Manag. 22, 869–887 (2019). · DOI · Paper page
- Fundus photographic risk factors for progression of diabetic retinopathy. ETDRS report number 12. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology 98, 823–833 (1991). · DOI
- Abramoff, M. D. et al. Approach for a clinically useful comprehensive classification of vascular and neural aspects of diabetic retinal disease. Invest. Ophthalmol. Vis. Sci. 59, 519–527 (2018). · DOI
Metadata
- Authors
- Abramoff, M. D., Whitestone, N., Patnaik, J. L., Rich, E., Ahmed, M., Husain, L., Hassan, M. Y., Tanjil, M. S. H., Weitzman, D., Dai, T., Wagner, B. D., Cherwek, D. H., Congdon, N., & Islam, K.
- Affiliations
- University of Colorado Anschutz (US)
- Queen's University Belfast (GB)
- Johns Hopkins University (US)
- Colorado School of Public Health (US)
- Deep Eye Care Foundation (BD)
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2023-10-04
- Volume
- 6
- Issue
- 1
- Pages
- 184-184
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10550906
- OpenAlex
- W4387330128
- Cited by
- 81
- References
- 27
- Citations by year
- 2026: 22 · 2025: 38 · 2024: 21
- FWCI
- 3.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (Queen's University Belfast) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- The project was supported by Orbis International and the Global Vision Foundation.
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- AI autonomy, diabetic retinopathy, AI-assisted diagnosis, health care productivity, diabetes mellitus, health care access, health disparities, real-world evidence
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