The 'Holy Grail' of Coding Automation: Why Inpatient AI is Around The Corner

Autonomous inpatient coding, once seen as a distant possibility, is now on the brink of reality. It holds immense significance for revenue cycle leaders due to the challenges associated with managing inpatient services. Despite representing a smaller volume of patient visits, inpatient care drives a significant portion of total revenues for provider organizations. However, staffing shortages and the complexity of inpatient coding have hindered efficient revenue cycle management.

Recent technological advancements, including natural language processing (NLP), the transition to ICD-10, deep learning, and the development of large language models (LLMs), have revitalized the prospect of inpatient coding automation. While earlier attempts at automation faced limitations, deep learning and LLMs have paved the way for high levels of automation, particularly in outpatient settings.

Now, with LLMs complementing deep learning, true automation in inpatient coding is within reach. This advancement is expected to arrive by 2024, providing hyper-accurate automation for most patient admissions. To prepare for this monumental shift, health system leaders can start by implementing autonomous coding in outpatient departments, reaping immediate benefits while laying the groundwork for expansion to inpatient care.

The impending arrival of autonomous inpatient coding represents a significant breakthrough for health systems, promising enhanced accuracy, reduced costs, and improved revenue capture. Organizations that proactively embrace this transformative technology will be best positioned to capitalize on its benefits.