One year into COVID-19, physicians are still stretched thin, especially with stringent Centers for Medicare and Medicaid Services coding requirements that burden already overworked healthcare providers.
But analytics programs, especially combined with artificial intelligence, can help by automating coding and quality reporting, while alleviating clinician burnout and reducing errors.
Healthcare IT News interviewed Mark Halford, senior vice president for client services, life sciences and healthcare, at WNS, a developer of revenue cycle management tools. He spoke about billing applications that can notify doctors about how they’re doing with CMS guidelines, and described how analytics helps with adherence to appropriate treatment protocols.
Q: How can billing information systems draw on CMS clinical guidelines and warn physicians when they’re falling outside of the guidelines?
A: CMS guidelines prescribe clear procedures and supporting documentation correlated to specific clinical scenarios. The revenue cycle management system embeds these guidelines to drive two key goals.
First, real-time help and information functions are available through the system to guide healthcare professionals at the point of data entry. Second, in the area of pre-claim submission the system validates the recommended and required clinical documentation (against the existing documents). Any gaps or errors are highlighted and accordingly resolved. This results in the avoidance of potential denials and unnecessary administration, while positively impacting quality ratings.
Q: How can data analytics examine and automate the latest treatment protocols?
A: This can be done by embedding the latest clinical standards – from universities across the U.S., including Stanford and Yale – as a rules engine that is updated annually. This engine allows for 75% of the claims to be approved automatically. For the remaining 25%, there is a scope for dialog regarding treatments that are more recent and/or outside the guidelines; 24% are resolved by nurses; and only 1% require peer-to-peer discussion for resolution.
To power the combination of AI and clinical oversight, each program has a clinically specialized physician advisory committee. The PAC oversees the clinical underpinnings of each program and manages modalities. It is responsible for the clinical integrity of the program, which includes taking into consideration the most recent treatment developments.
Q: How can AI observe physicians filling out EHR fields and prompt them to complete records according to specific guidelines?
A: AI provides a real-time help function, offering assistance pertinent to a clinical scenario at the point of data entry. This help function is an algorithm based on national, state and provider organization guidelines. If the correct and/or complete data is not recorded, AI will highlight any shortfalls at the pre-submission stage and prompt the provider to rectify.
Q: What are the results and the time saved that healthcare providers can experience through the implementation of data analytics?
A: Deploying AI across the claims and revenue cycle management process ultimately speeds up payer payments (to providers), reduces denials, and lowers the cost and time of administration. Downstream impact includes reduced re-work, more accurate coding, higher quality rating and optimized reimbursement.
Re-work effort required to improve clinical documentation can be cut down by more than 30-40%. This significantly decreases the burden on the clinical staff and eliminates the need for repeatedly accessing the patient file to secure reimbursement.
Leveraging data analytics on payment patterns, injury types, medical documentation completeness and denial trends offers deep insights into payer reimbursement techniques. This can improve revenue integrity, documentation quality and compliance. Specific data-rich algorithms guide providers on the techniques to improve payment velocity.