By Jane Austen, Healthcare Content Strategist, Physician Billing Company
Twitter: @jane_sman
Healthcare revenue cycles struggle with claim denials. Denial management investigates each denial, determines why it was denied, and determines how to fix the problem and reduce future denials. Sophisticated and robust automation, designed to meet providers’ needs and achieved through AI and ML, empowers organizations to achieve overarching process improvement that reduces first-pass denials and better addresses them when they occur.
What is denial management in medical billing and RCM?
Medical billing and revenue cycle management (RCM) require denial management to investigate, analyze, and resolve denied insurance claims. RCM optimizes revenue cycle administrative and clinical processes for financial performance and efficiency.
Denied healthcare claims cost physicians a lot of money every year. Strong denial management could have prevented this. 65% of claims are never resubmitted despite many recoverable denials. Denial management is essential for RCM to reduce denied claims and improve cash flow.
How to reduce claim denials with AI and automation?
Ensuring accurate eligibility
From the first patient interaction, errors and inefficiencies can cause problems. AI-automated benefit verifications reduce denials and improve revenue cycle accuracy. Registration and eligibility issues deny 23.9% of claims. Many of these result from coverage changes between scheduling and the appointment or inaccurate benefit information in the EHR. Intelligent automation can improve your EDI Real-Time Eligibility checks by checking coverage more often and reducing benefit pull errors, reducing denials due to inaccurate or out-of-date benefit information.
Automated prior authorization
Prior authorization and medical necessity issues cause most denials. Unless process improvements are made, denials will rise as prior authorizations rise. The hospital revenue cycle’s most laborious step is prior authorization. An end-to-end AI-powered prior authorization solution reduces denials by automating multiple steps, from determining if an authorization is needed to submitting prior auth requests with EHR data to checking statuses. This reduces denial-causing errors.
Claims status checks
Healthcare claims status checks are necessary but not always valuable. Checking a claim status usually suffices. However, manually checking a claim status takes 14 minutes, and considering the number of claims and the optimal frequency of checking them, this is impossible for humans to do. Automation is ideal for simple, repetitive tasks. How does this reduce denials? Most revenue cycle departments are chronically behind in claims, leaving little time to rework denials that could be recovered or improve revenue cycle processes to prevent denials. 48% of claim rejections and denials go unresolved. Automating claims status checks may not reduce denials, but it saves so much staff time that it can be used for other revenue cycle steps, including reworking denials.
AI-powered denials management
AI can help health systems recover more denied claims. AI-powered denials management starts with automated claims status checks. Artificial intelligence can fix simple errors and resubmit a “denied” claim. For more complicated errors, AI can pass the denial to a human but provide detailed patient and denial information, speeding up rework. AI-powered denials management systems work denials and collect all available reimbursements, increasing revenue and reducing A/R days.
Deep learning insights into denials
Artificial intelligence can analyze claims data and provide actionable insights, unlike RPA solutions. In one health system, missing prior authorizations and medical necessity caused a drug denial. This information helped the hospital identify and fix this issue. AI can uncover process improvements in your company’s daily data.
Simple Tech Solutions to Reduce Denied Claims and Improve Practice Efficiency
Denied claims drain resources and lower cash flow and revenue for most physician practices. Practices may underestimate the impact. Nearly 90% of denied claims are avoidable, and 50% to 65% are never reworked, leaving a lot of money on the table—money that could be used to hire new staff, replace outdated technology, or improve the office. Reworking a denial costs $25, not including time and overhead, which increases the impact.
Revenue cycle inefficiencies cause denials. With decreasing reimbursements and rising patient payments, practices must do everything they can to avoid revenue decline. Start by decreasing denials. Revenue cycle management technology can reduce and improve denials management in three ways.
Using AI Modeling to Maximize Revenue
Preventing claim denials saves time
AI and automation help providers reduce preventable denials. Providers can run automated front-end checks to catch and fix errors before submitting claims. Data-driven denial management workflows using Claim Source and Claim Scrubber can automatically review claims line by line.
AI programs “learn” from claims data to preventable denials. This can identify claims workflow flaws and predict future denials.
AI Advantage’s innovative analytics help providers predict, prevent, and process denials. This solution works before and after claim denial. AI advantage predictive denials edits high-risk claims before payer submission. After claims are denied, AI advantage denial finds the most likely denials for reimbursement.
Monitor payer rules
Providers must know payer rules to catch preventable errors. Staff struggle to track every change on hundreds of payer websites. Health recorded over 100,000 payer policy changes for coding and reimbursement in two years during the COVID-19 pandemic. Multiple health system staff checking the same notifications is inefficient. Automated Payer Alerts monitors over 60,000 payer web pages and sends staff a daily email digest.
Predictive models use this functionality. Data models must quickly integrate changing variables to accurately predict reimbursement. Data models using outdated payer rules will predict reimbursement incorrectly. AI modeling can continuously reinterpret data to calculate new scores and reprioritize accounts.
Maximize reimbursement with eligibility checks and prior authorizations
Four in ten providers have trouble tracking pre-authorizations, and nearly five in ten list these as their top three reasons for denials. Automated solutions can flag pre-authorization, find the necessary documentation, and provide real-time authorization status reports. Electronic prior authorizations prompt checks at every touchpoint in the patient journey and pull patient data to verify medical necessity. Many eligibility issues arise during patient access.
Health’s online prior authorization workflow automates inquiries and real-time authorization checks. This solution simplifies prior authorization submission with multiple payer connections. AI guides users to the right payer and connection type. Health fully automates submission back-ends. This helps users find and access the right payer portal for faster, more accurate submissions.
Prioritize recoverable claims
Denials teams often rework higher-value claims, but if an appeal is unlikely, the ROI will be low. “By scouring the provider’s own systems of record, an AI platform can analyze the history of the claim in question, as well as similar claims, to uncover such insights as past appeal success and percentage of reimbursement recovered,” says Health’s Chief Commercial Officer Jason Considine.
Multiple smaller claims that are approved will have a greater impact on the provider’s bottom line than higher-value claims that are denied.
Health’s ai denial helps staff prioritize profitable resubmissions. This new solution automatically categorizes denials by likelihood of approval, saving staff time on low-value claims. use it with an advantage predictive denial to maximize reimbursement. predictive denials flags claims at risk of denial so staff can intervene before submission.
Automating repetitive tasks reduces staffing pressures
Automated claims management tools free up staff time and boost revenue cycle productivity. Automation saves 22 minutes per claim status inquiry, according to CAQH. That takes a lot of time and money each month.
AI and automation can boost efficiency, but some employees worry that AI will eliminate jobs. Considine suggests that AI should be used to “handle repetitive tasks at scale and perform complex analysis, while employees focus on reworking claims most likely to realize reimbursement” in claims management. Anything that relieves overworked healthcare teams should be considered amid labor shortages.
AI and automated claim denial prevention
AI efficiency, accuracy, and intelligence can reduce denials, even though it’s new. Comprehensive medical claim recovery help providers submit clean claims the first time. Healthcare providers should use claims management tools to optimize denial workflow and maximize reimbursements.