Why Claim Prioritization Matters
Revenue teams often don’t know which claims are worth chasing until time has already been spent. The Revenue Prediction Models in Talisman Solutions’ ARM-AI platform are built to change that.
By looking at how similar claims have performed in the past and comparing them with what’s currently in the system, the models estimate how likely a claim is to get paid, how long that payment may take, and how much effort recovery will require. This gives billing and AR teams a clearer way to decide where to focus.
What This Solves
- Spot Low-quality Claims Before They Are Submitted
- Avoid Spending Effort On Revenue That Is Unlikely To Be Recovered
- Focus Ar Work On Claims That Have A Realistic Chance Of Payment
Recoverability Scoring
- Payment Likelihood (60%): Based on historical payment behavior, measured against the billed amount or the Medicare allowed amount.
- Time to Recover (20%): An estimate of how long it usually takes to receive payment for similar claims and payers.
- Cost and Effort (20%): Measures the level of manual work involved, such as appeals, follow-ups, or paper submissions.
Turn Predictions Into Action
See which claims matter most and focus AR effort accordingly.
How It Works in Practice
- Before claims are sent: As charges are posted, the system reviews recoverability and flags potential issues early. This gives teams a chance to make corrections before claims are submitted and denied.
- Within accounts receivable: Recoverability scores are assigned to every AR line item. Work queues can be sorted so teams focus on claims that are more likely to pay, rather than defaulting to an oldest-first approach.
- After denials: Post-denial audit reports provide expected recovery amounts, estimated timelines, and clear ownership of the next step. Claims are grouped by provider, patient, biller, or payer to simplify follow-up.
- Ongoing improvement: The recommendation engine points out common issues and suggests actions that can improve the chances of payment on future claims.
How the Models Are Built
The platform applies different modeling techniques based on the type of problem being addressed. Neural networks are used in situations where patterns are complex and non-linear. For outcome prediction, regression and classification models are used. ARIMA models are used for time-based forecasting to estimate recovery timelines and revenue flow.
For language-based tasks, the platform uses smaller, domain-trained language models. These models are faster to run and more cost-effective, while still delivering relevant results through Retrieval Augmented Generation. To avoid depending on a single method, multiple models are used together.
Data Used for Prediction
Predictions are based on a mix of clinical, operational, and financial data. This includes patient and provider details, CPT codes, modifiers, and diagnoses, chargeand adjustment data, primary and secondary insurance information, and historical denial and payment behavior. As outcomes are recorded, the models continue to learn and adjust.
How it helps
It tells the team which claims should be worked on first, how much effort recovery is likely to involve, and when revenue is expected to come in. Because of this, teams do not have to wait for denials to decide what to do next. Work can be planned ahead of time, focus stays on the right claims, and AR becomes easier to manage overall.