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

Area of Science:

• Ophthalmology
• Medical Informatics
• Health Services Research

Background:

• Neovascular age-related macular degeneration (nAMD) significantly contributes to hospital outpatient appointments.
• Clinical demand for macular services often exceeds capacity, leading to treatment delays.
• Artificial intelligence (AI) offers a potential solution to rebalance demand and capacity in macular services.

Purpose of the Study:

• To provide guidance for early adopters of AI in macular services.
• To explore factors influencing the successful implementation of AI-enabled macular services.
• To offer insights into optimizing AI implementation for demand-capacity imbalance.

Main Methods:

• Thirty-six semi-structured interviews were conducted.
• Data were analyzed using the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework.
• A secondary analysis using the Fit between Individuals, Technology and Task (FITT) framework was performed to propose an intervention.

Main Results:

• AI-enabled scheduling can maintain or enhance patient communication while reducing consultation frequency.
• Trained photographers should manage AI data input and communication, with ophthalmologists providing clinical oversight.
• Interoperability requires secure cloud image transfer for AI analysis and PACS integration with EMRs.

Conclusions:

• Implementation enablers are numerous, with few barriers directly related to AI technology.
• The proposed intervention needs local tailoring and prospective evaluation.
• AI implementation can be optimized for success in macular services.