How to Improve Attendance for Diabetic Retinopathy Screening: Ideas and Perspectives From People With Type 2 Diabetes and Health-care Professionals
Abstract
Objectives Our aim in this study was to identify how to improve diabetic retinopathy screening from the perspectives of people with type 2 diabetes and health-care professionals and to elicit their thoughts on initiatives to increase attendance. Methods A total of 38 semistructured interviews were conducted with people with type 2 diabetes (n=20), general practitioners (n=10), and ophthalmic staff (n=8). The interviews examined ideas for improving screening and elicited feedback on 3 initiatives: getting a fixed appointment; same-day screening; and outsourcing screening to general practice, including the use of artificial intelligence (AI). Data analysis was guided by content analysis approaches. Results Ideas for improving screening were centred around reducing the inconvenience of attendance, making appointment scheduling easier, and improving health-care professionals' communication. Participants recognized the potential benefits of the initiatives but expressed important reservations to consider. Concerns included the following: that a fixed appointment would cause less active patient involvement and negatively affect attendance; that same-day screening may result in loss of patient-provider communication; that people with type 2 diabetes may be uneasy with having the screening performed outside the eye clinic; and that health-care professionals were concerned about the finances, validity, and examination quality associated with outsourcing screening and using AI. Conclus…
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Time-saving is a facilitator to AI adoption in diabetes management.
“the time sparing effect, both for the patient and for the physician [53,56,57]”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
Forgetfulness to visit the patient is a barrier in the individuals domain.
“or forgetfulness to visit the patient [53].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
Perceived unsatisfactory clinical performance of AI systems is a barrier to AI adoption in diabetes management.
“The relevant barrier under this domain is the unsatisfactory clinical performance of the AI system.”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
High costs of AI software/acquisition are a barrier to AI adoption in diabetes management.
“Another common barrier is related to the financial burden of AI software/high acquisition costs [52,53,56].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
Most of the included studies focused on the use of AI for diabetic retinopathy screening.
“The majority of the included studies were focused on the use of AI for diabetic retinopathy screening [51,52,53,56,57]”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
Liao et al. and Peterson et al. were well designed.
“The studies by Liao et al. [52] and Peterson et al. [53] were well designed.”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
Most of the included studies adopted a qualitative design (n = 4) that used semi-structured interviews.
“Most of the studies adopted a qualitative design (n = 4) that used semi-structured interviews [51,52,53,57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
A total of 7 studies, conducted between 2019 and 2024, were included in the review.
“Finally, a total of n = 7 studies, conducted between 2019 and 2024, were included [51,52,53,54,55,56,57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [53]
Cites
Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045
Teo, Z. L., Tham, Y.-C., Yu, M., Chee, M. L., Rim, T. H., Cheung, N., Bikbov, M. M., Wang, Y. X., Tang, Y., Lu, Y., et al. (2021) · Ophthalmology · doi:10.1016/j.ophtha.2021.04.027 · Cited by 2,420 · see citing works
Liu, J., Gibson, E., Ramchal, S., Shankar, V., Piggott, K., Sychev, Y., Li, A. S., Rao, P. K., Margolis, T. P., Fondahn, E., et al. (2021) · Ophthalmology Retina · doi:10.1016/j.oret.2020.06.016 · Cited by 88 · see citing works
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Cangelosi, G., Conti, A., Caggianelli, G., Panella, M., Petrelli, F., Mancin, S., Ratti, M., & Masini, A. (2025) · Medicina · doi:10.3390/medicina61081403 · Cited by 14 · see citing works
“The identified facilitators are an improved clinical efficiency [52,54,55], easiness of use [52,54], and the time sparing effect, both for the patient and for the physician [53,56,57].”
Influential
- Authors
- Petersen, G. B., Joensen, L. E., Kristensen, J. K., Vorum, H., Byberg, S., Fangel, M. V., & Cleal, B.
- Affiliations
- Steno Diabetes Center Copenhagen (DK)
- Aalborg University (DK)
- Aalborg University Hospital (DK)
- Corresponding author
- Petersen, Gabriela B.
- ORCID
- Venue
- Canadian Journal of Diabetes
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 49
- Issue
- 2
- Pages
- 121-127
- ISSN
- 1499-2671 (ISSN-L)
- OpenAlex
- W4404851835
- Cited by
- 6
- References
- 2
- Citations by year
- 2026: 3 · 2025: 3
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Canadian Journal of Diabetes (publishedVersion)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- Funders
- Novo Nordisk Foundation (NNF18OC0052595)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- diabetic retinopathy screening, type 2 diabetes, screening uptake, artificial intelligence screening, patient-provider communication, patient perspectives, healthcare professional perspectives, barriers to screening
- MeSH terms
- Adult, Aged, Diabetes Mellitus, Type 2, Diabetic Retinopathy, Female, Health Personnel, Humans, Male, Mass Screening, Middle Aged
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