Healthcare Providers Spend Billions On Claims Adjudication
Healthcare providers are spending nearly $26 billion managing claims adjudication, a 23% increase year over year, due to the complexity of payer policies and guidelines. This complexity may be addressed with AI-driven payer intelligence.
Original reporting · healthcaredive.com
Original: “Why AI-driven payer intelligence is a revenue cycle imperative”
Read “Why AI-driven payer intelligence is a revenue cycle imperative” at Healthcare Dive →
How this was written. Diagnosis Forward wrote this article with a language model from Healthcare Dive’s reporting (model: @cf/meta/llama-3.3-70b-instruct-fp8-fast); it describes that reporting rather than adding to it. The original, linked above, is the full story.
Key points from Healthcare Dive’s report
- Healthcare providers are spending nearly $26 billion on claims adjudication, a 23% increase year over year.
- AI-driven payer intelligence may help providers anticipate payer behavior and reduce complexity.
- Reducing payer-provider friction may improve the patient experience and reduce administrative costs.
Healthcare providers are facing significant challenges in managing claims adjudication, with spending reaching nearly $26 billion, according to Healthcare Dive. This represents a 23% increase year over year, driven by the growing complexity of payer policies, guidelines, and contracts.
Revenue cycle management (RCM) teams are struggling to keep up with the constant evolution of payer policies, which may change without warning. This complexity may be addressed by leveraging data to anticipate payer behavior, rather than operating in catch-up mode.
The current state of revenue cycle management is highly fragmented, with separate workflows for eligibility, authorizations, coding, and denials. This fragmentation may make it challenging to piece together the complete narrative of how payers behave, and may lead to hidden requirements being discovered only after a claim is submitted.
AI-driven payer intelligence may help providers anticipate payer behavior, by identifying, analyzing, and acting on patterns in how payers reimburse or deny claims. This may enable organizations to shift from reactive recovery to proactive prevention and revenue protection.
Healthcare organizations may already have enormous amounts of revenue data, but much of it remains disconnected and difficult to operationalize. With AI and connected workflow automation, organizations may be able to synthesize payer-provider interaction data at scale and drive more consistent decisions.
Reducing payer-provider friction may also improve the patient experience, by enabling providers to submit cleaner claims, anticipate requirements, and shift from correction to prevention. Patients may benefit from fewer pending claims and denials, less frustrating post-service outreach, and fewer barriers to timely care.
Questions and answers
What is driving the increase in claims adjudication spending?
The growing complexity of payer policies, guidelines, and contracts is driving the increase in claims adjudication spending.
How may AI-driven payer intelligence help providers?
AI-driven payer intelligence may help providers anticipate payer behavior, identify patterns, and reduce complexity.
What may be the benefits of reducing payer-provider friction?
Reducing payer-provider friction may improve the patient experience, reduce administrative costs, and enable providers to submit cleaner claims.
