What data does the predictive model use?

The model analyzes over 800 data points per claim including payer identity, code combinations, modifier usage, patient demographics, provider history, eligibility status, authorization status, and historical denial outcomes for similar claims.

How accurate are the denial predictions?

Prediction accuracy improves over time as the model learns your practice and payer mix. Most practices see meaningful reduction in preventable denials within 30 days. The system flags false positives for staff review, which improves model accuracy over time.

Does Sarva prevent all denials?

No. Predictive prevention targets preventable denials — coding errors, eligibility issues, authorization gaps. Non-preventable denials like retrospective medical necessity audits or payer policy changes are not fully predictable. Sarva reduces preventable denials significantly but cannot guarantee zero denials.

Use Case

Predictive Denial Prevention Before Claims Are Submitted

Predictive denial prevention uses machine learning models trained on millions of claim outcomes to identify denial-prone patterns before a claim is submitted. Sarva analyzes over 800 data points per claim — including payer history, code combinations, patient demographics, and provider patterns — to generate a denial risk score and recommend corrective action in under 500 milliseconds.

Review Your Denial Workflow

The Business Problem

The traditional denial management approach is reactive: submit claims, wait for denials, rework them, resubmit. This cycle costs time, money, and staff morale. By the time a denial is received, the claim has already aged 14–30 days. Some denials are never appealed, resulting in permanent revenue loss. The shift from reactive denial management to proactive denial prevention is the single biggest revenue recovery opportunity for most practices.

Affected Team

Revenue cycle leadership, billing, coding, and denials team. CFOs and practice managers see the financial impact in delayed cash flow and written-off revenue.

How It Works

Current Manual Workflow

Claims are submitted and denials arrive days or weeks later. A denials specialist reviews each denial, categorizes the reason, determines if it is appealable, and initiates rework. The process is entirely reactive — by the time the team sees the denial, the damage is done and the claim has aged.

Automated Workflow with Sarva

Sarva analyzes each claim before submission using predictive models trained on millions of historical claim outcomes. The model identifies patterns that led to denials for the same payer, code combination, and patient attributes. Claims scoring above a configurable risk threshold are routed for targeted review with a specific explanation of the predicted denial risk — not a generic alert.

Inputs

Claim data (all codes, modifiers, demographics)
Payer-specific historical denial patterns
Provider billing history
Patient eligibility and authorization status
NCCI and payer-specific edit libraries

Outputs

Denial risk score (0–100)
Predicted denial category (medical necessity, coding, eligibility, auth, etc.)
Recommended preventive action
Clean claim ready for submission
Denial prevention analytics dashboard

Human Oversight

High-risk claims are routed to coding staff with specific guidance. Staff confirm the correction and release the claim. The system learns from each correction, improving prediction accuracy over time. Low-risk claims pass through without manual intervention.

Security Considerations

All claim analysis occurs in an isolated, encrypted environment. No claim data is shared between customers. Model training uses your data only — your PHI never trains shared models. SOC 2 Type II and HIPAA compliant with signed BAA.

Integration Considerations

Sarva integrates with your clearinghouse, practice management system, or EHR billing module. The predictive models are pre-trained but adapt to your practice and payer mix over the first 60–90 days, learning your specific denial patterns.

Limitations

Predictive models cannot anticipate every denial cause. Novel payer policy changes, retrospective audits, and clinical documentation disputes are not fully predictable. Sarva reduces preventable denials significantly but cannot guarantee zero denials. Model accuracy improves over time as more data is processed.

Key Performance Indicators

Preventable denial rate

Target

< 2%

First-pass acceptance rate

Target

> 94%

Predicted denials caught

Target

> 85%

Revenue protected per month

Target

Practice-specific

See how this workflow applies to your practice.

Review Your Denial Workflow

Related Connectors

Frequently Asked Questions

Claim scrubbing applies static rules (NCCI edits, formatting checks). Predictive denial prevention uses machine learning trained on actual claim outcomes to identify patterns that lead to denials — including patterns that are not covered by static rules. It is a more sophisticated, data-driven approach.

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