Generative AI costs in large healthcare systems, an example in revenue cycle
Michael Burns, Ssu-Ying Chen, Chu-An Tsai, John Vandervest, Balaji Pandian, Paige Nong, David A. Hanauer, Andrew Rosenberg, Jodyn Platt
Abstract
Application of large language models in healthcare continues to expand, specifically for medical free-text classification tasks. While foundation models like those from ChatGPT show potential, alternative models demonstrate superior accuracy and lower costs. This study underscores significant challenges, including computational costs and model reliability. Amidst rising healthcare expenditures and AI's perceived potential to reduce costs, a combination of local and commercial models might offer balanced solutions for healthcare systems.
Topics & Concepts
Health careComputer scienceRevenueGenerative grammarFoundation (evidence)BusinessArtificial intelligenceRisk analysis (engineering)Operations researchHealthcare systemActuarial scienceGenerative modelEconomicsManagement scienceMedical servicesHealth servicesData scienceArtificial Intelligence in Healthcare and EducationMachine Learning in HealthcareTopic Modeling