AI Reimagines Telehealth Billing System

The rise of telehealth has introduced a pressing concern in the medical billing landscape, where the current time-based compensation model is struggling to reflect the value of medical expertise and experience accurately. As telehealth continues to grow in popularity, the limitations of the existing billing system have become increasingly apparent, with experienced doctors potentially being under-compensated for their services despite providing higher-quality care.

To address this issue, researchers at the University of Cincinnati are leveraging artificial intelligence and electronic health records to develop a novel billing model that considers the time spent responding to patient inquiries and the doctor’s clinical judgment and expertise.

By harnessing the power of AI to quantify medical expertise, this innovative approach aims to create a more balanced and sustainable billing system that recognizes the value of cognitive judgment and specialized knowledge, ultimately leading to fairer compensation for doctors and improved patient outcomes.

Introduction to Telehealth Billing Challenges

The increasing popularity of telehealth has created a conundrum in the medical billing system. The current approach fails to quantify the varying levels of medical expertise and experience, making it unsustainable. This issue is being tackled by Dong-Gil Ko, an associate professor at the University of Cincinnati’s Carl H. Lindner College of Business, who is using artificial intelligence (AI) and electronic health records to create a fairer and more effective billing model.

The current time-based billing model creates inequities in compensation by undervaluing experienced doctors. Those with greater expertise can provide accurate answers quickly, but may be compensated less than less knowledgeable doctors who take more time to respond. This system unfairly rewards inefficiency and fails to recognize the value of cognitive judgment and expertise, leading to skilled doctors being undercompensated despite offering higher-quality care.

To address these shortcomings, Ko is collaborating with Umberto Tachinardi, MD, UC Health’s chief health digital officer, and Eric J. Warm, MD, an internal medicine physician and researcher at the UC College of Medicine. Together, they are leveraging AI and electronic health records from UC Health to develop a new billing model that incorporates doctors’ clinical judgment and expertise, alongside the time spent responding to patient inquiries.

The Current Telehealth Billing Model

Ohio’s current medical billing code pays medical professionals based on how much time they spent answering a question via a secure messaging system. If they spend less than five minutes responding, the service is free. If they take more than five minutes to answer a question, they receive compensation, with fees increasing as their time increases. However, this model prioritizes time over skill, which can lead to experienced doctors being undercompensated.

The current model also creates uncertainty for patients, who may be discouraged from contacting a medical professional due to the uncertainty of whether they will get billed. This can break down continuity of care, delaying treatment and potentially leading to worse health outcomes. Furthermore, the current model forces doctors to make billing decisions without reliable methods to measure their work, which can erode trust between doctors and their patients.

The integration of generative AI into medical practice is expected to exacerbate these challenges. While AI can deliver faster solutions, doctors will still need to validate its responses and invest time in maintaining and operating these systems — efforts that must be compensated to avoid increasing burnout among medical professionals.

Developing a New Billing Model with AI

Ko’s research aims to create a fairer and more effective billing model by incorporating doctors’ clinical judgment and expertise into the billing process. His AI system can use doctors’ behaviors to better understand and measure their expertise, offering a framework for fairer compensation. The tests of machine learning models have delivered consistent results, demonstrating the potential to more accurately evaluate doctors’ expertise and time spent on patient inquiries.

The new billing model will consider both time and medical expertise in billing decisions, providing a more balanced approach. Ko anticipates that this model will be particularly important as generative AI becomes more integrated into medical practice, requiring doctors to validate AI-assisted responses and invest time in maintaining and operating these systems.

Future Applications and Implications

Ko envisions broader applications for his research, including creating a system that predicts whether a patient will be billed before submitting a question and uncovering insights from patient data to improve care outcomes. By combining AI and innovative research, Ko’s work could transform telehealth billing, ensuring fair compensation for doctors while improving patient outcomes.

The University of Cincinnati plans to pilot Ko’s program with health systems in 2025, which could have significant implications for the future of telehealth billing. The integration of AI into medical practice is expected to continue growing, and Ko’s research provides a critical framework for ensuring that doctors are fairly compensated for their work while improving patient outcomes.