Health care cost and benefits of artificial intelligence-assisted population-based glaucoma screening for the elderly in remote areas of China: a cost-offset analysis
Open accessAbstract
BACKGROUND: Population-based screening was essential for glaucoma management. Although various studies have investigated the cost-effectiveness of glaucoma screening, policymakers facing with uncontrollably growing total health expenses were deeply concerned about the potential financial consequences of glaucoma screening. This present study was aimed to explore the impact of glaucoma screening with artificial intelligence (AI) automated diagnosis from a budgetary standpoint in Changjiang county, China. METHODS: A Markov model based on health care system's perspective was adapted from previously published studies to predict disease progression and healthcare costs. A cohort of 19,395 individuals aged 65 and above were simulated over a 15-year timeframe. Fur illustrative purpose, we only considered primary angle-closure glaucoma (PACG) in this study. Prevalence, disease progression risks between stages, compliance rates were obtained from publish studies. We did a meta-analysis to estimate diagnostic performance of AI automated diagnosis system from fundus image. Screening costs were provided by the Changjiang screening programme, whereas treatment costs were derived from electronic medical records from two county hospitals. Main outcomes included the number of PACG patients and health care costs. Cost-offset analysis was employed to compare projected health outcomes and medical care costs under the screening with what they would have been without screening. One-way sensitivity analysis was conducted to quantify uncertainties around model results. RESULTS: Among people aged 65 and above in Changjiang county, it was predicted that there were 1940 PACG patients under the AI-assisted screening scenario, compared with 2104 patients without screening in 15 years' time. Specifically, the screening would reduce patients with primary angle closure suspect by 7.7%, primary angle closure by 8.8%, PACG by 16.7%, and visual blindness by 33.3%. Due to early diagnosis and treatment under the screening, healthcare costs surged dramatically to $107,761.4 dollar in the first year and then were constantly declining over time, while without screening costs grew from $14,759.8 in the second year until peaking at $17,900.9 in the 9th year. However, cost-offset analysis revealed that additional healthcare costs resulted from the screening could not be offset by decreased disease progression. The 5-, 10-, and 15-year accumulated incremental costs of screening versus no screening were estimated to be $396,362.8, $424,907.9, and $434,903.2, respectively. As a result, the incremental cost per PACG of any stages prevented was $1464.3. CONCLUSIONS: This study represented the first attempt to address decision-maker's budgetary concerns when adopting glaucoma screening by developing a Markov prediction model to project health outcomes and costs. Population screening combined with AI automated diagnosis for PACG in China were able to reduce disease progression risks. However, the excess costs of screening could never be offset by reduction in disease progression. Further studies examining the cost-effectiveness or cost-utility of AI-assisted glaucoma screening were needed.
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AI in glaucoma screening improved clinical outcomes by reducing blindness by 33.3%, but resulted in additional long-term costs.
“while AI in glaucoma screening improved clinical outcomes by reducing blindness by 33.3%, it resulted in additional long-term costs35.”
From Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare (2025) · cites this paper as [35]
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- Authors
- Xiao, X., Xue, L., Ye, L., Li, H., & He, Y.
- Affiliations
- Renmin Hospital of Wuhan University (CN)
- Jinan University (CN)
- Fudan University (CN)
- Corresponding author
- He, Yunzhen
- Venue
- BMC Public Health
- Published
- 2021-06-04
- Volume
- 21
- Issue
- 1
- Pages
- 1065-1065
- ISSN
- 1471-2458 (ISSN-L)
- PMC
- PMC8178835
- OpenAlex
- W3165428749
- Cited by
- 44
- References
- 38
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 6 · 2023: 8 · 2022: 3
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81100644)
- Natural Science Foundation of Hubei Province
- Topics
- Health Sciences › Medicine › Ophthalmology › Glaucoma and retinal disorders
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- glaucoma screening, artificial intelligence, primary angle-closure glaucoma, mass screening, older adults, remote areas, cost-effectiveness, disease progression, health care costs, China, Markov model, primary angle closure suspect, blindness prevention
- MeSH terms
- Aged, Artificial Intelligence, China, Cost-Benefit Analysis, Glaucoma, Humans, Glaucoma, Angle-Closure, Health Care Costs
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