Shaping the Role of AI in the Ophthalmologist’s Toolbox

Clinical Scorecard: Shaping the Role of AI in the Ophthalmologist’s Toolbox

At a Glance

Category	Detail
Condition	Physician involvement in AI use and practice analytics in ophthalmology and health care
Key Mechanisms	The author argues that providers should help shape AI tools used in the exam room and practice, and describes how context-free productivity reports can create stress. In a practice example, managers discussed provider goals and support needs, then selected analytics and benchmarks with providers.
Target Population	Ophthalmologists and other providers involved in patient care and practice operations
Care Setting	Ophthalmology exam rooms and practices

Key Highlights

The author argues that providers should participate in designing AI tools rather than only receive tools developed by others.
The author says AI should support the doctor-patient interaction and that the provider makes the final clinical decision and signs the chart.
The author says AI scribes are not the solution and describes documentation and operational demands that can add to provider burden.
In a practice example, managers replaced context-free weekly productivity reports with discussions of provider goals, support needs, useful analytics, reporting frequency, and benchmarks.
The author argues that providers should help determine which metrics matter and how performance data reach them.

Guideline-Based Recommendations

Diagnosis

Management

The author urges providers to take part in designing AI tools used in the exam room and practice.
The author says providers should help shape AI use in ways that support the doctor-patient interaction; the provider makes the final clinical decision and signs the chart.
In the practice example, managers asked providers about productivity and clinic goals, support needs, useful analytics, and preferred reporting frequency.

Monitoring & Follow-up

The author describes adding benchmarks and context to productivity reports rather than sending raw figures alone.
The author urges attention to how and when analytics reach providers and how they follow providers into the exam room.

Risks

The author describes stress and concern about leaving associated with weekly productivity reports sent without context.
The author warns that dashboards may not distinguish a routine break from a productivity problem.

Patient & Prescribing Data

No specific patient population or prescribing data are provided.

The article does not discuss medications or treatment regimens; it focuses on provider involvement in AI use, the doctor-patient interaction, and practice analytics.

Clinical Best Practices

The author urges providers who work directly with patients to participate in AI design and deployment.
The author states that the provider makes the final clinical decision and signs the chart.
In the practice example, managers discussed provider goals and support needs before selecting analytics.
The author describes sharing productivity data with benchmarks and context and asking providers how often they want to see analytics.