Can staff override the AI prioritization?

Yes. The prioritized worklist is a recommendation. Staff can override scores based on contextual knowledge — for example, a claim with a known payer issue that the model does not account for. Overrides are logged and used to improve model accuracy.

How long until the scoring model is accurate for our practice?

Initial scoring uses industry benchmarks and becomes practice-specific within 60–90 days as Sarva processes your claims and learns your payer recovery patterns. Accuracy improves continuously as more data is accumulated.

Use Case

Healthcare AR Prioritization with Recovery Probability Scoring

Aging AR prioritization scores every outstanding claim by its recovery probability and expected financial yield, so follow-up effort is directed at the claims most likely to result in payment. Sarva uses payer history, claim age, claim type, denial patterns, and historical recovery data to generate a priority score — replacing the traditional approach of working aging reports oldest-first or largest-balance-first.

Prioritize Your Aging AR

The Business Problem

Traditional AR follow-up uses simple sorting: oldest first, or largest balance first. Neither approach optimizes for recovery. A 120-day-old $5,000 claim with a payer that rarely pays after 90 days is lower yield than a 45-day-old $800 claim with a reliable payer. Without prioritization intelligence, staff waste time on low-probability accounts while high-probability claims age further.

Affected Team

AR follow-up specialists and revenue cycle leadership. Efficient prioritization directly impacts cash recovery and AR days — two metrics that determine revenue cycle health.

How It Works

Current Manual Workflow

A biller exports an AR aging report sorted by age or balance. They work the list top-to-bottom, calling payers and documenting outcomes. Prioritization is based on gut feel and individual experience. No systematic scoring or probability analysis is applied. The result is uneven recovery rates and unpredictable cash flow.

Automated Workflow with Sarva

Sarva scores every aging claim using a recovery probability model that considers payer history, claim age, claim type, historical recovery rates for similar claims, and denial patterns. Claims are ranked by expected yield (probability multiplied by claim amount). Staff — or Sarva's autonomous follow-up — work the highest-yield claims first, maximizing recovery per hour of effort.

Inputs

AR aging data with claim details
Payer-specific recovery history
Claim age and submission history
Denial and appeal outcomes
Historical recovery patterns by claim type

Outputs

Priority score per claim
Ranked worklist by expected yield
Recovery probability by payer and claim type
Estimated recovery value
Staff allocation recommendations

Human Oversight

Staff review the prioritized worklist and can override scores based on contextual knowledge. Sarva's scoring is a recommendation — not a mandate. Staff handle the highest-priority claims that require human intervention while Sarva autonomously follows up on routine items.

Security Considerations

All AR data is encrypted and processed in an isolated environment. No data shared between customers. Full audit trails. HIPAA BAA included. SOC 2 Type II certified.

Integration Considerations

Sarva reads AR data from your practice management system or billing software. The prioritization model adapts to your specific payer mix and recovery patterns over the first 60–90 days. The prioritized worklist can be viewed in Sarva's dashboard or exported to your existing AR management tools.

Limitations

The recovery probability model requires historical data to calibrate. New practices or those with limited historical data will see less accurate scoring initially. Scoring improves as Sarva processes more of your claims and learns your payer-specific recovery patterns. The model cannot predict payer insolvency or sudden policy changes.

Key Performance Indicators

Recovery rate per staff hour

Target

2x improvement

AR days

Target

< 30

AR over 90 days

Target

< 15%

High-yield claims worked first

Target

> 90%

See how this workflow applies to your practice.

Prioritize Your Aging AR

Related Connectors

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Frequently Asked Questions

Sarva analyzes historical recovery data for your practice — which payers pay after 60, 90, 120 days, which claim types are recovered most often, and which denial reasons are successfully appealed. This produces a probability score per claim that is multiplied by claim amount to generate an expected yield ranking.

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