AI-Powered Revolution in Telehealth Billing: Balancing Expertise and Time

AI-Driven Solution for Telehealth Billing Inequities

Researcher Dong-Gil Ko, Ph.D., an associate professor at the University of Cincinnati's Carl H. Lindner College of Business, is spearheading an innovative approach to address the growing challenges in telehealth billing. Ko's research, recently published in the Journal of the American Medical Informatics Association, leverages artificial intelligence (AI) and electronic health records to create a more equitable and effective billing model for telehealth services 1

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Current Telehealth Billing Challenges

The current time-based billing model for telehealth services, implemented in Ohio in 2023, has revealed significant shortcomings:

1. Undervaluation of expertise: Experienced doctors who provide quick, accurate responses may be compensated less than less knowledgeable colleagues who take longer to answer 1

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2. Inefficiency rewards: The system inadvertently rewards inefficiency, failing to recognize the value of cognitive judgment and expertise 2

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3. Trust erosion: The model may strain doctor-patient relationships by forcing billing decisions without reliable measurement methods 1

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4. Patient hesitation: Uncertainty about billing may discourage patients from seeking medical advice, potentially leading to delayed treatment and worse health outcomes 2

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AI-Powered Solution

Ko, in collaboration with Umberto Tachinardi, MD, and Eric J. Warm, MD, is developing an AI-driven billing model that aims to:

1. Incorporate doctors' clinical judgment and expertise alongside time spent on patient inquiries 1

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2. Use machine learning to analyze doctors' behaviors and measure their expertise more accurately 2

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3. Create a balanced billing model that considers both time and medical expertise 1

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Anticipating Future Challenges

As generative AI becomes more integrated into medical practice, Ko foresees new challenges:

1. Validation of AI-assisted responses will be crucial in the early stages 2

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2. Doctors will need compensation for time spent maintaining and operating AI systems to prevent burnout 1

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Broader Applications and Future Plans

Ko's research extends beyond the current billing model:

1. Developing a system to predict whether a patient will be billed before submitting a question 2

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2. Uncovering insights from patient data to improve care outcomes 1

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3. Piloting the program with health systems in 2025 2

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By addressing the current inequities in telehealth billing and preparing for the integration of AI in healthcare, Ko's work has the potential to transform the telehealth landscape, ensuring fair compensation for medical professionals while improving patient care and outcomes.