– Change Healthcare unveils a new AI solution, Charge Capture Advisor that will help providers capture more revenue—automatically.
– Charge Capture Advisor identifies potentially missing charges for services that providers performed before claims are submitted.
– Change Healthcare’s AI for charge capture is trained on more than 500 million service lines and 180 million unique, de-identified claims that touch $245 billion in charges.
Change Healthcare today introduced Charge Capture Advisor, a new cloud-based addition to the company’s portfolio of Revenue Integrity Solutions. The solution uses Change Healthcare Artificial Intelligence to identify potentially missing charges for services that providers actually performed before claims are submitted.
The result: more complete capture of services rendered without additional time and effort by hospital revenue integrity teams. Working alongside providers’ existing health information system (HIS), coding, billing, and manual processes as part of a comprehensive charge-capture strategy, Charge Capture Advisor brings the power of AI to help increase detection of missing charges to drive complete claims, accelerate cash flow, and optimize revenue.
The Financial Impact of Charge Capture
If improving charge-capture accuracy hasn’t been at the top of your priority list, it’s time to reconsider. Missing charges and associated reimbursement, combined with the time spent on post-payment audit and recovery, is estimated to cost providers the equivalent of 1% of annual revenue¹. For many hospitals and healthcare systems, that can easily equate to millions of dollars. Likewise, overcharging can be equally detrimental to your bottom line when you consider the cost of processing repayments, interest, fines, audits, and legal fees.
Drive Revenue with AI-Infused Charge Capture
This solution, dubbed “Charge Capture Advisor”, takes automated charge capture beyond rules-based systems to using AI and machine learning to autonomously identify missing charges in claims. The result: Fewer charges missed by human error and gaps in hard-coded rules-based systems, which means providers will capture significantly more revenue in the recycle.
Change Healthcare AI, used in Charge Capture Advisor, identifies potentially missing charges for services that providers performed before claims are submitted. Moreover, unlike brittle rules-based systems that must be manually maintained, Change Healthcare AI keeps getting smarter as it learns, without costly software rules updates.
The net-net: The AI drives costs out of the revenue cycle and boosts income by capturing charges otherwise lost and saves costs and resources from a technology standpoint.
The Benefits of Using AI-Infused Charge Capture Advisor
Change Healthcare’s AI for charge capture is trained on more than 500 million service lines and 180 million unique, de-identified claims that touch $245 billion in charges. This industry-leading volume of data allows the AI model to provide more accurate charge capture predictions. Moreover, Charge Capture Advisor requires no integration with existing software, freeing the IT department to focus on its core responsibilities. Missing charge predictions are accessed in real-time in the claim workflow, helping ensure claim completeness at each level. Change Healthcare’s Intelligent Healthcare Network™ includes more than 2,200 payers, 5,500 hospitals/health systems, and 900,000 physicians; and the associated claims data powers AI-infused solutions that transform the accuracy and efficiency of revenue cycle processes.
Why It Matters
“Providers are still falling short of their charge-capture potential, despite using the most sophisticated rules-based systems and meticulous manual audits,” said Nick Giannasi, Chief AI Officer at Change Healthcare. “In fact, it’s estimated that missing charges and associated reimbursement, combined with audit and recovery efforts, cost providers the equivalent of 1% of annual revenue¹. Charge Capture Advisor can help providers identify those missed charges and help improve revenue capture.”
