They say a stranger is just a friend you haven’t met yet. At Sift Healthcare, we quip that many receivables are just denials you haven’t met yet. Because any receivable can become a denial, health systems need meaningful and accessible intelligence around their payments. Sift's Payments Intelligence Platform uses machine learning to demystify healthcare payments.
Today, I'm sharing some of the more basic intelligence we provide around protecting receivables and accelerating cash.
1) Target your ‘Zero allowed Zero Paid’ claims.
The first place to look for hidden denials is in your zero allowed, zero paid claims. Start by running a report on all closed balances where the paid and allowed amounts are zero. Sometimes payers won't pay but will send remittances without a denial reason code. These denials may be hidden because denial reports and worklists are often configured to report only on amounts associated with reason codes mapped as a denial.
2) Find and examine your clinical validation denials.
In some cases, revenue cycle leaders are blissfully unaware that clinical validation denials exist. Many A/R systems fail to acknowledge these denials because of the timing and manner of issuance. Payers issue clinical validation denials via letter instead of remittance, often retrospectively, after sending the initial payment once the claim is closed.
To address clinical validation denials, you first need to understand where these letters are routed and how they are logged (potentially by HIM, Case Management, Utilization Management, or your correspondence scanning team). Once logged, you can begin to target which DRGs or diagnoses are common targets for clinical validation denials and work with CDI and physician leadership to ensure the clinical evidence supports the physician documentation that led to your added secondary diagnosis that led to the higher-weighted DRG or CC/MCC capture.
3) Closely monitor your ‘patient/member’ and ‘benefits exceeded’ denials for dates of service in the first quarter of each plan year.
Many payer systems are late to update or reset their member rosters each plan year. This is especially true for many State Medicaid plans. These delays lead to excess eligibility and benefits exhausted denials in the first 1-3 months of a calendar year (or plan year). Many patient/member and benefits exceeded denials are mapped to the Patient Responsibility (PR) group code, which many A/R systems are configured to auto-adjudicate to self-pay, bypassing the denials management workflow altogether. Eventually, many of these payers will self-correct and issue recoupments and repayments independently without staff intervening. However, it's worth examining the trend of eligibility/benefits denials by payer to identify exactly when their plan year has not been refreshed and accelerate cash by proactively alerting your payer rep to the issue.
4) Establish leading and lagging KPIs for denial overturns to isolate problems in your Open Aged Trial Balance for denied and outstanding claims.
Everybody knows how much cash they brought in last month, but I always ask, “How much of your cash last month was from overturned denials?”. Very few have answers, and many A/R systems or reporting products cannot adequately map initial denial volumes to final payment outcomes.
To establish these KPIs, you first need to know how much you’re overturning historically to establish baseline (tracking these KPIs is part of Sift’s ML-enabled Rev/Track reporting suite). Using this lagging indicator, you can identify when your staff’s time was better spent on the $100 overturn of the $1,000 claim than on the more complex $14,000 claim for an overturn of $0. Without establishing your predictive leading indicators, traditional follow-up prioritization often directs staff to spend more time for less money on the more complex, higher dollar claims. Sometimes even when the gross amount or expected revenue is the size of a boulder, you won’t be able to squeeze water out of the rock. Healthcare organizations that leverage Sift's ML-generated propensity-to-overturn scores find 99% of their overturned dollars in just 40% of the denials they receive. Integrating Sift's propensity-to-overturn scoring into your staff’s A/R system helps you prioritize the good (overturn dollars) and automate the bad.
To learn more about Sift's denials intelligence or how our ML-based denials scoring works, contact me at email@example.com.
Interested in a *free* Denials Assessment for your health system? Click here to learn more and see if your organization qualifies.