Intervention design for artificial intelligence-enabled macular service implementation: a primary qualitative study.

ABSTRACT:

Background

Neovascular age-related macular degeneration (nAMD) is one of the largest single-disease contributors to hospital outpatient appointments. Challenges in finding the clinical capacity to meet this demand can lead to sight-threatening delays in the macular services that provide treatment. Clinical artificial intelligence (AI) technologies pose one opportunity to rebalance demand and capacity in macular services. However, there is a lack of evidence to guide early-adopters seeking to use AI as a solution to demand-capacity imbalance. This study aims to provide guidance for these early adopters on how AI-enabled macular services may best be implemented by exploring what will influence the outcome of AI implementation and why.

Methods

Thirty-six semi-structured intervi

SUBMITTER: Hogg HDJ 

PROVIDER: S-EPMC11600873 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature