Two-Field Photography Can Identify Patients With Vision-Threatening Diabetic Retinopathy: A screening approach in the primary care setting

Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting.Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure.Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI.Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

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Improving Access to Diabetic Retinopathy Screening through Telemedicine (Preprint)

10.2196/preprints.17838 ◽

2020 ◽

Author(s):

Shuja Rayaz ◽

Tiffany Wandy ◽

Jenna Brager ◽

Michael Kiritsy ◽

Daniel Durand

Keyword(s):

Primary Care ◽

Diabetic Retinopathy ◽

Care Setting ◽

Vision Loss ◽

Primary Care Setting ◽

Adult Population ◽

Descriptive Study ◽

Pilot Program ◽

Diabetic Retinopathy Screening ◽

Retinopathy Screening

BACKGROUND Screening for diabetic retinopathy is important for the prevention of blindness among the adult population. Currently, patients with diabetes require a referral from their primary care physician to see an ophthalmologist for their annual eye exam, which can be an added inconvenience. As such, there is a need for alternative screening strategies within an outpatient network. The use of a telemedicine platform in a primary care network serves as a novel strategy to increase diabetic retinopathy screening rates. LifeBridge Health operates two Track 1 Accountable Care Organizations with a combined attribution of approximately 28,000 patients. Many value-based care and pay for performance programs use diabetic retinopathy screening rate as a quality measure. In order to provide better access to diabetic retinopathy screening for our patients, three specialized cameras were placed in three primary care practices in October 2017 as part of a pilot program. The online Intelligent Retinal Imaging Systems (IRIS) platform was utilized as a secure data warehouse of images that could be interpreted remotely by an ophthalmologist within the LifeBridge Health network for the diagnosis of diabetic retinopathy or detecting other types of pathology (e.g. macular edema). OBJECTIVE The aim of this retrospective descriptive study was to examine if a telemedicine platform can be used to increase diabetic retinopathy screening rates in the primary care setting. METHODS Three distinct datasets corresponding with three time periods were examined for this study. Pre-post comparison examined screening rates from all practices from January 2018 – December 2018 to those of January 2017 – December 2017. The pilot program dataset examined screening rates in the practices before and after the implementation of the IRIS cameras in October 2017. Aggregate diagnostic data from the IRIS online dashboard from October 2017- December 2019 was also examined to determine the benefit of the IRIS platform since the initial implementation. RESULTS Pre-post comparison screening rates showed mean screening rates of 38.5% and 47.2%, respectively, indicating an 8.7% improvement in screening. The pilot program showed improved screening rates at each outpatient practice with the implementation of the IRIS cameras. Aggregate data since the implementation of the IRIS cameras showed that, of the 1213 patients who were screened, approximately 17.1% (n=207 patients) were diagnosed with diabetic retinopathy and an additional17.7% (n=215 patients) were suspected of having some form of other pathology. 10.1% (n=123 patients) were also suspected to be at risk for imminent vision loss. CONCLUSIONS This retrospective descriptive study suggests that a telemedicine platform can be used to improve diabetic retinopathy screening rates in the primary care setting within a large healthcare system.

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