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Section: Business
As telehealth continues to gain traction, the challenges associated with billing practices have come to the forefront. The traditional billing approach does not adequately account for the varying levels of medical expertise when processing claims, leading to a flawed system that affects both healthcare providers and patients.
Dong-Gil Ko, an associate professor at the University of Cincinnati's Carl H. Lindner College of Business, has identified these issues as part of his research into telehealth billing practices. His recent study, published in a reputable medical informatics journal, explores the potential of artificial intelligence (AI) and electronic health records to develop a more equitable billing framework.
The conventional time-based billing system has been criticized for creating disparities in compensation, particularly disadvantaging experienced medical professionals. Ko points out that seasoned doctors who provide accurate diagnoses quickly may receive less compensation than less experienced colleagues who take longer to arrive at the same conclusions. This model inadvertently rewards inefficiency while failing to adequately recognize the value of expertise and clinical judgment.
In collaboration with UC Health's chief health digital officer and an internal medicine researcher, Ko is leveraging AI to enhance billing methodologies. The goal is to create a system that acknowledges the complexities of medical expertise while still considering the time invested in patient interactions.
Ko emphasizes the need to recognize the rigorous training that medical professionals undergo to develop their specialized knowledge. By implementing a balanced billing model that values both the time spent and the expertise of doctors, the healthcare system can ensure fair compensation.
Ohio's current medical billing code, established in 2023, compensates healthcare professionals based primarily on response time to patient inquiries through secure messaging systems. However, this model presents challenges: if a physician takes fewer than five minutes to respond, the service is free, while responses exceeding that time incur charges that increase with duration.
This creates a systemic issue where experienced doctors earn less due to the emphasis on time rather than skill. In contrast, less experienced practitioners may benefit financially despite providing potentially less accurate or timely care.
Moreover, the existing billing framework can undermine the trust between physicians and patients, as it lacks reliable metrics for evaluating the quality of care provided. Doctors often do not track their response times meticulously, and complex inquiries may require multiple sessions to resolve, complicating billing further.
Ko highlights the possibility that uncertainty regarding billing may deter patients from seeking medical advice, disrupting continuity of care and potentially worsening health outcomes. He argues for a balanced approach that incorporates both time and expertise into billing practices.
As generative AI becomes more integrated into healthcare, Ko anticipates that billing challenges will escalate. While AI can offer quicker solutions, healthcare providers will still need to validate AI-generated responses and manage these systems, which necessitates adequate remuneration to prevent burnout among healthcare workers.
Ko's AI framework aims to assess physicians' expertise and time spent on patient inquiries, presenting a more accurate evaluation of their contributions. Early tests of machine learning models have shown promise in developing a sustainable and fair billing model.
Looking forward, Ko plans to expand his research to predict billing outcomes before patients submit inquiries and to derive insights from patient data to enhance care quality. His innovative approach may reshape telehealth billing practices, ensuring equitable compensation for healthcare providers while fostering improved patient outcomes. A pilot program for this new billing model is slated for introduction in health systems in 2025.
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