Capturing the clinical decision-making processes of expert and novice diabetic retinal graders using a 'think-aloud' approach

Eye (Lond). 2022 May;36(5):1019-1026. doi: 10.1038/s41433-021-01554-6. Epub 2021 May 10.

Abstract

Background: Diabetic eye screening programmes have been developed worldwide based on evidence that early detection and treatment of diabetic retinopathy are crucial to preventing sight loss. However, little is known about the decision-making processes and training needs of diabetic retinal graders, particularly in low- and middle-income countries.

Objectives: To provide data for improving evidence-based diabetic retinopathy training to help novice graders process fundus images more like experts.

Subjects/methods: This is a mixed-methods qualitative study conducted in southern Vietnam and Northern Ireland. Novice diabetic retinal graders in Vietnam (n = 18) and expert graders in Northern Ireland (n = 5) were selected through a purposive sampling technique. Data were collected from 21st February to 3rd September 2019. The interviewer used neutral prompts during think-aloud sessions to encourage participants to verbalise their thought processes while grading fundus images from anonymised patients, followed by semi-structured interviews. Thematic framework analysis was used to identify themes, supported by illustrative quotes from interviews. Mann-Whitney U tests were used to compare graders' performance.

Results: Expert graders used a more systematic approach when grading images, considered all four images per patient and used available software tools such as red-free filters prior to making a decision on management. The most challenging features for novice graders were intra-retinal microvascular abnormalities and new vessels, which were more accurately identified by experts.

Conclusion: Taking more time to grade fundus images and adopting a protocol-driven "checklist" approach may help novice graders to function more like experts.

MeSH terms

  • Clinical Decision-Making
  • Diabetes Mellitus*
  • Diabetic Retinopathy* / diagnosis
  • Fundus Oculi
  • Humans
  • Retina
  • Software