Cost-effectiveness and cost-utility of traditional and telemedicine combined population-based age-related macular degeneration and diabetic retinopathy screening in rural and urban China

Li, Ruyue; Yang, Ziwei; Zhang, Yue; Bai, Weiling; Du, Yifan; Sun, Runzhou; Tang, Jianjun; Wang, Ke; Liu, Hanruo

doi:10.1016/j.lanwpc.2022.100435

Cited by 14 publications

(32 citation statements)

References 45 publications

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“…The adoption of DL model for community-based eye disease screening is necessary. Before the development of DL model, the screening relied on ophthalmologists heavily, regardless of conducting traditional face-to-face screening or a telemedicine system [ 25 ]. At this stage, continuous eye disease screening was not affordable in most of the countries [ 6 ] for two reasons.…”

Section: Discussionmentioning

confidence: 99%

“…On the one hand, the limited human resources of the ophthalmologists resulted in extremely high screening costs [ 5 ]. On the other hand, the organization of the screening was challenging, requiring the coordination of ophthalmologists, community health centers, and residents at the same time [ 25 ]. As a result, in Shanghai, before the adoption of the DL model, each community only could provide screening service to approximately 300 residents per year.…”

Section: Discussionmentioning

confidence: 99%

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Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Lin¹,

Li²,

Zou³

et al. 2022

J Med Internet Res

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Background Deep learning–assisted eye disease diagnosis technology is increasingly applied in eye disease screening. However, no research has suggested the prerequisites for health care service providers and residents willing to use it. Objective The aim of this paper is to reveal the preferences of health care service providers and residents for using artificial intelligence (AI) in community-based eye disease screening, particularly their preference for accuracy. Methods Discrete choice experiments for health care providers and residents were conducted in Shanghai, China. In total, 34 medical institutions with adequate AI-assisted screening experience participated. A total of 39 medical staff and 318 residents were asked to answer the questionnaire and make a trade-off among alternative screening strategies with different attributes, including missed diagnosis rate, overdiagnosis rate, screening result feedback efficiency, level of ophthalmologist involvement, organizational form, cost, and screening result feedback form. Conditional logit models with the stepwise selection method were used to estimate the preferences. Results Medical staff preferred high accuracy: The specificity of deep learning models should be more than 90% (odds ratio [OR]=0.61 for 10% overdiagnosis; P<.001), which was much higher than the Food and Drug Administration standards. However, accuracy was not the residents’ preference. Rather, they preferred to have the doctors involved in the screening process. In addition, when compared with a fully manual diagnosis, AI technology was more favored by the medical staff (OR=2.08 for semiautomated AI model and OR=2.39 for fully automated AI model; P<.001), while the residents were in disfavor of the AI technology without doctors’ supervision (OR=0.24; P<.001). Conclusions Deep learning model under doctors’ supervision is strongly recommended, and the specificity of the model should be more than 90%. In addition, digital transformation should help medical staff move away from heavy and repetitive work and spend more time on communicating with residents.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Lin¹,

Li²,

Zou³

et al. 2022

J Med Internet Res

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“…Consequently, the optimal allocation of resources in healthcare could ensure that those with a high risk for AMD undergo regular eye cares appointments and receive timely interventions; this may be an effective public health strategy. Annual screening for AMD based on a teleophthalmology platform has been proven to be an optimal screening approach ( 142 ).…”

Section: Perspectives On Preventionmentioning

confidence: 99%

Epidemiology, health policy and public health implications of visual impairment and age-related eye diseases in mainland China

Liu

Zhu

Zhang

et al. 2022

Front. Public Health

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The prevalence of visual impairment (VI) and age-related eye diseases has increased dramatically with the growing aging population in mainland China. However, there is limited comprehensive evidence on the progress of ophthalmic epidemiological research in mainland China to enhance our awareness of the prevention of eye diseases to inform public health policy. Here, we conducted a literature review of the population-based epidemiology of VI and age-related eye diseases in mainland China from the 1st of January 1946 to the 20th of October 2021. No language restrictions were applied. There was significant demographic and geographic variation in the epidemic of VI and age-related eye diseases. There are several factors known to be correlated to VI and age-related eye diseases, including age, gender, family history, lifestyle, biological factors, and environmental exposures; however, evidence relating to genetic predisposition remains unclear. In addition, posterior segment eye diseases, including age-related macular degeneration and diabetic retinopathy, are amongst the major causes of irreversible visual impairments in the senile Chinese population. There remains a significant prevention gap, with only a few individuals showing awareness and achieving optimal medical care with regards to age-related eye diseases. Multiple challenges and obstacles need to be overcome, including the accelerated aging of the Chinese population, the lack of structured care delivery in many underdeveloped regions, and unequal access to care. Despite the progress to date, there are few well-conducted multi-center population-based studies following a single protocol in mainland China, which findings can hopefully provide valuable cues for governmental decision-making and assist in addressing and halting the incidence of VI and age-related eye diseases in China.

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“…From 1990 to 2010, visual impairment due to DR increased by 64% and blindness by 27% [1], both of which were due to the rising DM prevalence in low-and middle-income countries (LMICs). Several studies have confirmed the benefits of telemedicine screening for DR [3][4][5][6]. Compared to no screening and traditional face-to-face screening, telemedicine screening is highly cost-effective in the long term.…”

Section: Introductionmentioning

confidence: 97%

“…Compared to no screening and traditional face-to-face screening, telemedicine screening is highly cost-effective in the long term. As a result, telemedicine screening will become the main form of community-based eye disease screening [5]. Some recent studies have suggested that using artificial intelligence (AI) can further reduce the costs of telemedicine screening [3,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data

Lin¹,

Ma²,

Xu³

et al. 2023

JMIR Public Health Surveill

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Background Community-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)–based and manual grading–based telemedicine screening is inadequate for policy making. Objective The aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China. Methods We conducted cost-effectiveness and cost-utility analyses by using decision-analytic Markov models with 30 one-year cycles from a societal perspective to compare the cost, effectiveness, and utility of 2 scenarios in telemedicine screening for DR: manual grading and an AI model. Sensitivity analyses were performed. Real-world data were obtained mainly from the Shanghai Digital Eye Disease Screening Program. The main outcomes were the incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR). The ICUR thresholds were set as 1 and 3 times the local gross domestic product per capita. Results The total expected costs for a 65-year-old resident were US $3182.50 and US $3265.40, while the total expected years without blindness were 9.80 years and 9.83 years, and the utilities were 6.748 quality-adjusted life years (QALYs) and 6.753 QALYs in the AI model and manual grading, respectively. The ICER for the AI-assisted model was US $2553.39 per year without blindness, and the ICUR was US $15,216.96 per QALY, which indicated that AI-assisted model was not cost-effective. The sensitivity analysis suggested that if there is an increase in compliance with referrals after the adoption of AI by 7.5%, an increase in on-site screening costs in manual grading by 50%, or a decrease in on-site screening costs in the AI model by 50%, then the AI model could be the dominant strategy. Conclusions Our study may provide a reference for policy making in planning community-based telemedicine screening for DR in LMICs. Our findings indicate that unless the referral compliance of patients with suspected DR increases, the adoption of the AI model may not improve the value of telemedicine screening compared to that of manual grading in LMICs. The main reason is that in the context of the low labor costs in LMICs, the direct health care costs saved by replacing manual grading with AI are less, and the screening effectiveness (QALYs and years without blindness) decreases. Our study suggests that the magnitude of the value generated by this technology replacement depends primarily on 2 aspects. The first is the extent of direct health care costs reduced by AI, and the second is the change in health care service utilization caused by AI. Therefore, our research can also provide analytical ideas for other health care sectors in their decision to use AI.

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Cost-effectiveness and cost-utility of traditional and telemedicine combined population-based age-related macular degeneration and diabetic retinopathy screening in rural and urban China

Cited by 14 publications

References 45 publications

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment

Epidemiology, health policy and public health implications of visual impairment and age-related eye diseases in mainland China

Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data

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