Quoting from McKinsey.

At a convention center in Chicago this April, tens of thousands of attendees watched as a new generative-AI (gen AI) technology, enabled by GPT-4, modeled how a healthcare clinician might use new platforms to turn a patient interaction into clinician notes in seconds.

Here's how it works: a clinician records a patient visit using the AI platform's mobile app. The platform adds the patient's information in real time, identifying any gaps and prompting the clinician to fill them in, effectively turning the dictation into a structured note with conversational language. Once the visit ends, the clinician reviews, on a computer, the AI-generated notes, which they can edit by voice or by typing, and submits them to the patient's electronic health record (EHR). That near-instantaneous process makes the manual and time-consuming note-taking and administrative work that a clinician must complete for every patient interaction look archaic by comparison.

Risk Adjustment is one area in healthcare where the uptake of NLP has been faster than others. This rate of adoption is only going to increase, thanks largely to two changes in the risk adjustment market.

Firstly, perhaps the most pressing trend is the recent final Risk Adjustment Data Validation (RADV) rule issued by the U.S. Centers for Medicare and Medicaid Services (CMS), which has increased regulatory pressure on healthcare organizations to ensure accurate risk adjustment. The rule is intended to make it easier for CMS to claw back overpayments to healthcare organizations that were awarded as a result of faulty risk adjustment. Therefore – the use of accurate NLP to identify clinical conditions and their supporting evidence (as per the Monitor Evaluate Assess Treat – MEAT framework) is vital. Secondly, the Medicare Advantage risk adjustment model is due to change from V24 to V28 over the upcoming three years. These changes will significantly reduce the number of risk adjustable conditions, therefore, technologies which support accurate and complete capture of a member’s health are a necessity for organizations looking to ensure they don’t lose funding needed to provide care for their chronically ill members.

NLP and AI are already being used in risk adjustment in healthcare with great success. For example, one study found that using NLP to analyze clinical notes improved risk adjustment accuracy by 5.3% (WoltersKluwer )

Another study found that using AI to analyze claims data improved risk adjustment accuracy by 7.4% (NIH.gov). These technologies are also being used to identify patients at risk for certain conditions, such as diabetes or heart disease, and to develop personalized treatment plans to prevent these conditions from developing or worsening. As these technologies continue to improve, we can expect to see even more innovative uses for NLP and AI in risk adjustment in healthcare.

AI can eliminate the repetitive tasks that are easier to accomplish, bringing more efficiency to coders and auditors, according to an industry veteran. AI-enabled technology can summarize large data sets stemming from hundreds of pages of medical records, which a human would have to parse to code an encounter accurately.

“The tool can help summarize and give you a snapshot of what happened through the entirety of that admission by quickly capturing the diagnosis and identifying where in the documentation it came from,” he says. “Staff can then quickly go to those areas without reading hundreds of documents. That is a game changer with time and efficiency. That is going to enable your humans to make better decisions quicker.”

That is the key to implementing AI in medical coding and billing; technology must complement the professional’s workload and workflow. Coders and technology need to develop a synergy which is critical.