Could an artificial intelligence approach to prior authorization be more human?

Abstract

Prior authorization (PA) may be a necessary evil within the healthcare system, contributing to physician burnout and delaying necessary care, but also allowing payers to prevent wasting resources on redundant, expensive, and/or ineffective care. PA has become an “informatics issue” with the rise of automated methods for PA review, championed in the Health Level 7 International’s (HL7’s) DaVinci Project. DaVinci proposes using rule-based methods to automate PA, a time-tested strategy with known limitations. This article proposes an alternative that may be more human-centric, using artificial intelligence (AI) methods for the computation of authorization decisions. We believe that by combining modern approaches for accessing and exchanging existing electronic health data with AI methods tailored to reflect the judgments of expert panels that include patient representatives, and refined with “few shot” learning approaches to prevent bias, we could create a just and efficient process that serves the interests of society as a whole. Efficient simulation of human appropriateness assessments from existing data using AI methods could eliminate burdens and bottlenecks while preserving PA’s benefits as a tool to limit inappropriate care.

Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation

“…Current LLMs might be well suited for administrative tasks that require standardized and predictable forms of writing grounded in accurate information provided by the user. In healthcare, these may include drafting documents (eg, pre-authorization letters) [ 40 ] and synthesizing texts (eg, from the electronic medical record for a discharge summary) [ 41 ]. It should be noted, however, that current publicly available LLMs (eg ChatGPT) may harvest user input for further training, and entering protected patient information into these systems likely breaches confidentiality.…”