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Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow
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Intervention design for artificial intelligence-enabled macular service implementation: a primary qualitative study.

Henry David Jeffry Hogg1,2,3, Katie Brittain4, James Talks5

  • 1Research, Development and Innovation, University Hospitals Birmingham NHS Foundation Trust, Level 2 ITM, Queen Elizabeth HospitalMindelsohn Way, Birmingham, B15 2GW, UK. J.Hogg.1@bham.ac.uk.

Implementation Science Communications
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can optimize neovascular age-related macular degeneration (nAMD) treatment scheduling. This study guides early adopters on implementing AI to improve macular services and patient outcomes.

Keywords:
Artificial intelligenceClinical decision supportImplementationMachine learningNeovascular age-related macular degenerationOphthalmologyQualitative researchRetinaTheoretical approachTheory

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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.