OpenEvidence adds AI coding suggestions

OpenEvidence, a large language model-based search engine for clinicians, has launched an AI feature to support medical coding through targeted suggestions.  

Launched in 2021, OpenEvidence provides clinical decision support, allowing clinicians to ask medical questions and receive evidence-based answers. It recently launched a telehealth feature that enables phone calls and messaging between clinicians and patients, and is now entering the revenue cycle management space.

The new Coding Intelligence feature is live in OpenEvidence Visits, a clinical assistant that transcribes patient encounters with added context from guidelines, research and clinical recommendations. Now, the new coding feature automatically provides ICD-10 diagnoses and evaluation and management (E/M) level recommendations, along with the clinical decision-making rationale, directly in the clinical note.

"Without any extra work, OpenEvidence is able to generate concise rationale for their CPT + E/M suggestions," said Ania Bilski, M.D., vice president of clinical AI at OpenEvidence, in the press release. "It truly captures the complexity of the encounter and saves me hours when I'm at the ER."

The feature also offers CPT code suggestions based on clinical documentation and guidelines. The company stated that the feature can surface uncommon procedure codes for complex cases and display the expected RVU values alongside CPT suggestions, so the codes can be sequenced before the claim is submitted.

OpenEvidence joins several companies looking to automate the revenue cycle with AI tools.

For instance, generative AI startup Abridge announced plans to use part of the $300 million in Series E funding it raised last year to expand its clinical documentation platform to provide revenue cycle intelligence earlier in clinical conversations.

However, recent research shows that ambient AI scribing tools drive up coding intensity. One study estimated approximately $2.3 billion in additional healthcare spending associated with aggressive practices enabled by AI. While these practices net higher reimbursements for the provider, they could result in patients facing higher medical bills, exacerbating the healthcare affordability crisis.

 Anuja Vaidya has covered the healthcare industry since 2012. She currently covers the virtual healthcare landscape, including telehealth, remote patient monitoring and digital therapeutics.