According to a survey conducted by AKASA, healthcare financial leaders have shown an interest in leveraging generative artificial intelligence (AI) to streamline revenue cycle operations as of November 20, 2023.
The survey, which received responses from over 250 chief financial officers (CFOs) and financial executives from hospitals and health systems nationwide, revealed that more than 70% of the participants are actively considering the use of relationalAI. Additionally, close to 60% of financial leaders are contemplating incorporating this technology into profit cycle operations.
In terms of specific applications, around 25% of respondents are keen on utilizing generative AI for clinical documentation, while 18% are exploring other potential uses, and 13% are evaluating the possibility. Conversely, 30% of respondents are not actively considering the implementation of generativeAI.
Generative AI, a form of artificial intelligence technology, is capable of producing text, images, audio, and synthetic data. In the healthcare sector, generative AI plays a crucial role in analyzing complex medical documents, extracting relevant data, and applying this information across various aspects of the revenue cycle.
The adoption of conceptual AI for revenue cycle tasks could alleviate administrative burdens, enhance efficiency, and assist medical organizations in coping with financial challenges, staff shortages, and increasing patient volumes. Importantly, the use of AI in these processes does not directly jeopardize individual health.
Healthcare institutions have successfully employed generative AI to streamline prior authorization procedures and generate appeal letters following claim denials from payers. Moreover, relational AI can support revenue cycle leaders in optimizing front-end operations, including data verification and scrubbing.
Despite the evident benefits of AI technology in the healthcare industry, organizations face barriers to its effective implementation in revenue cycle management. Challenges such as integration with existing IT systems like Electronic Health Records (EHR) and concerns about job displacement due to AI adoption are among the key obstacles.
In addition to revenue cycle management, healthcare providers have utilized conceptual AI as diagnostic tools. For instance, ChatGPT, a large language model (LLM), has demonstrated the ability to generate diagnoses when presented with complex patient cases and relevant clinical data.
Consumer perspectives on conceptual AI are positive, with Deloitte data indicating that this technology has the potential to reduce medical costs and improve access to care.
