A recent survey conducted by AKASA has shed light on the growing interest among financial leaders in the healthcare industry to implement generative artificial intelligence (AI) to streamline revenue cycle operations. The survey collected responses from over 250 chief financial officers (CFOs) and financial leaders from hospitals and health systems across the nation.
Key findings from the survey indicate that more than 70% of respondents are actively considering the utilization of generative AI. Specifically, nearly 60% are interested in employing this technology for revenue cycle operations. Additionally, 23% of respondents expressed interest in deploying generative AI in clinical documentation, 18% for other functional purposes, and 13% for clinical care. However, 30% of participants stated that they are not actively considering the use of generative AI.
Generative AI is a type of artificial intelligence technology that has the ability to generate text, imagery, audio, and synthetic data. In the healthcare context, generative AI can analyze complex clinical documents, extract relevant information, and apply the data to various revenue cycle operations.
With healthcare organizations facing financial challenges, staffing shortages, and increasing patient volumes, the adoption of generative AI for revenue cycle tasks has the potential to alleviate administrative burdens and enhance operational efficiency. Importantly, the use of AI in these processes does not pose any direct risk to patient health.
Some of the applications of generative AI in healthcare revenue cycle management include automatically generating appeal letters in response to claim denials from payers and streamlining the prior authorization process. Revenue cycle leaders can also leverage generative AI to enhance front-end processes like data validation and cleaning.
Despite the promising advantages of AI in healthcare, barriers to its adoption in revenue cycle management persist. Integration with existing IT systems, such as electronic health records (EHRs), can be challenging. Moreover, healthcare providers may harbor skepticism towards these technologies or express concerns about potential job losses due to automation.
In addition to revenue cycle management, generative AI is also being explored in healthcare as a diagnostic tool. A study revealed that ChatGPT, a large language model (LLM), was capable of generating accurate diagnoses when presented with complex patient cases and corresponding clinical data.
According to Deloitte, consumers believe that generative AI has the potential to reduce healthcare costs and improve access to care. As the interest in generative AI continues to grow within the healthcare industry, it is essential for organizations to carefully address implementation challenges and maximize the benefits of this innovative technology.
What is generative AI?
Generative AI refers to artificial intelligence technology that can produce text, imagery, audio, and synthetic data.
In what areas of healthcare can generative AI be utilized?
Generative AI can be applied in various aspects of healthcare, including revenue cycle operations, clinical documentation, clinical care, and other functional purposes.
What are the potential benefits of using generative AI in revenue cycle operations?
Leveraging generative AI in revenue cycle operations can help reduce administrative burden, improve efficiency, and streamline processes without posing any direct risk to patient health.
What are some challenges to the adoption of generative AI in healthcare?
Integrating generative AI with existing IT systems, such as electronic health records (EHRs), can be challenging. Healthcare providers may also have concerns or skepticism about the technology, including potential job losses due to automation.
AKASA (URL: akasa.ai)
Deloitte (URL: deloitte.com)