Healthcare costs are rising, and denial rates are following suit. Commercial claim denials increased by 20% between 2022 and 2023, while Medicare Advantage denials rose by nearly 56%. To contain costs, payers can use technology to streamline the denial process — helping to swiftly assess medical necessity and flag gaps in clinical documentation.
Providers can arm themselves with solutions of their own to demonstrate the quality and necessity of the care they are providing by capturing the complete patient story in the right way, effectively preventing denials before they happen.
Why traditional denials management falls short
Traditionally, coding teams address denials by appealing claims after they have been submitted. This is a time-consuming and costly process: Providers spend $19.7 billion annually fighting denials alone. However, around half of denials eventually get paid, highlighting the opportunity to prevent them in the first place.
Retrospective appeals create an expensive problem. The longer a claim goes unpaid, the more likely it is to be written off. According to the AHA, 50% of health systems have more than $100 million lagging in accounts receivable for claims more than 6 months old, significantly tying up resources. Thirty-five percent of health systems report losses of $50 million or more from foregone payments on denied claims once appeals are exhausted.
Downstream, the effects of denials materialize. Patients whose claims have been denied tend to give lower quality and satisfaction scores — rating clinical care 8.2 points lower on Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys, scoring that affects quality ratings and overall hospital reimbursement.
With a proactive approach, organizations can prevent denials before they happen, saving significant time and resources. This approach reduces write-offs, captures more billed revenue and enhances revenue cycle efficiency to improve cash flow and the ability of organizations to support daily operations and achieve greater efficiency.
Unlock the potential of prospective denials management with technology
The onus is on providers to show the full care story to get paid. Providers may have the information to justify a claim but struggle with payer documentation requirements. In-house coding and clinical documentation integrity teams continually scramble to help providers stay updated on what information to document and how to present it. Integrated software that delivers that information as providers are charting can greatly relieve this burden.
"We marry clinical, financial and operational information to show what was done to the patient, coded to support the rationale for getting paid correctly," explains Jason Burke, vice president of revenue cycle solutions at Solventum.
This AI-powered technology uses machine learning (ML) to analyze clinical, financial and operational data — drawing from organization-specific insights and industry payer intelligence to generate a score based on the likelihood of a claim being paid. This score can mitigate denials before they happen and conserve resources.
Keys to successful ML-powered prospective denial prevention
Healthcare leaders across the country are hungry to adopt AI into their workflows to offload manual processes and keep ahead of surging denials.
The right solution provides actionable insights for prospective denials prevention and scores how likely a claim is to be denied. "We clarify which specific documentation or coding opportunity can be addressed and provide clinical indicators and appropriate guidelines to help the clinician address in real time," says Diana Ortiz, RN, JD, senior business director of revenue cycle at Solventum. With this intelligence, most mid-revenue cycle denials can be avoided completely.
Further considerations for using ML in a successful denials management process include:
- Resource allocation — Many denied claims can be addressed, but some may never be paid regardless of appeal efforts. Knowing which to focus on can save time and money. "Without insights, organizations tackle all denials in the same way. It wastes resources," emphasizes Burke.
- Team coordination — Organizations can reduce waste by allocating issues to the correct teams from the start. Denials can originate from gaps in many points of the revenue cycle — from registration to prior authorization. "Healthcare organizations continue to have many siloed teams working independently to solve problems. We can help them put comprehensive intelligence in front of the right users," Ortiz highlights.
- Workflow integration — Embedded tools can alert staff to potential issues in real time, enhancing existing workflows rather than disrupting them.
- Continuous learning and adaptation — ML models must be updated regularly to account for changes in payer policies. "The right solution is focused on sustainability. The rules of the game change. The action you take today might not be the same in a year," Ortiz notes.
With an accurate view of gaps in payer documentation requirements, providers can focus their time and energy on documenting what they need, not what they don't. "We guide providers to not try to boil the ocean, but to focus on the things that are most meaningful for their payer-provider payment model," Ortiz stresses. Over time, organizations can expect to see measures of progress and returns on investment, including lower denial rates (especially in avoidable categories).
Denials can significantly drain a healthcare organization's finances, but much of that can be prevented. By integrating adaptive ML insights and documentation alerts into clinical workflows, providers can show the quality of the care they provide and fully justify payment for the services rendered. Healthcare organizations that shift from a retrospective to a prospective approach to denials management can reduce waste and capture lost revenue. The right partner can help you integrate the latest ML tools and give insights to avoid denials before they happen.
