Medical Review · SVRS
Statistically Valid Random Sampling for healthcare claim audits.
AEGIS ISD's Statistically Valid Random Sampling (SVRS) module runs claim-sample selection and overpayment extrapolation aligned with HHS-OIG RAT-STATS conventions. Cochran's-formula sample sizing, Fisher-Yates random selection, and Mean Per Unit extrapolation produce reproducible, documented audit outputs for Medicaid, Medicare, and commercial program integrity reviews. SVRS is part of AEGIS ISD Medical Review and samples claims from Case Management cases.
Last updated: October 19, 2026
Why SVRS matters
Audit defensibility from the first sampled claim.
State Medicaid program integrity
State Medicaid Fraud Control Units (MFCUs) and Program Integrity Units must demonstrate statistically valid sampling and extrapolation when projecting overpayments to a provider universe. AEGIS SVRS records the parameter set, random seed, selected claims, and Mean Per Unit projection for the audit work paper package.
Medicare and commercial audits
Audits across Medicare, Medicare Advantage, and commercial lines of business commonly follow HHS-OIG RAT-STATS conventions. AEGIS SVRS records its inputs, seed, and results in an SVRS methodology PDF and an extrapolation PDF that can be saved to the case.
False Claims Act exposure mitigation
When investigations escalate to qui tam or FCA referral, methodology defensibility is no longer optional. AEGIS SVRS keeps the parameters, random seed, selected claims, and saved extrapolation worksheets with the case, so the method can be explained and reproduced when a referral is made.
Methodology
Three pillars: sizing, selection, extrapolation.
SVRS computes the sample, draws it without bias, and projects results back to the universe with explicit confidence intervals. Every step is parameterized, logged, and reproducible.
Sample-size determination — Cochran's formula
Sample size is computed from the user-supplied confidence level (commonly 90%, 95%, or 99%), margin of error, and expected error rate. The result is corrected for finite-population effects (FPC) when the universe is small relative to the unbounded sample. AEGIS surfaces every input value and the derived sample size in the methodology PDF, so auditors can replicate the calculation by hand if needed.
Random selection — Fisher-Yates shuffle
AEGIS uses a seeded Fisher-Yates shuffle to draw a simple random sample without bias. The seed is retained with the sample, so the selection can be reproduced from the same claim universe. A preview shows the sample size and draw before the sample is saved.
Extrapolation — Mean Per Unit
Once medical-review determinations are recorded against each sampled unit, AEGIS computes the Mean Per Unit point estimate, the standard error, and the lower and upper confidence bounds. The projected overpayment is the point estimate scaled to the universe size, with the lower bound typically used as the conservative recovery target. Probe, nested, and member-universe samples use a one-sided 90% lower bound based on Student's t distribution.
RAT-STATS alignment
Aligned with HHS-OIG RAT-STATS conventions.
The HHS-OIG RAT-STATS application is the reference sampling and extrapolation tool for federal healthcare audits. AEGIS SVRS is aligned with RAT-STATS conventions for parameter input, random-seed disclosure, sample-size derivation, and Mean Per Unit output, so its documentation is familiar to auditors who work with RAT-STATS.
For a step-by-step look at sampling frames, sample size, seeds, and the 90% lower bound, read our guide: SVRS for overpayment estimates: a practical guide.
Familiar parameters
Confidence level, margin of error, expected error rate, finite population correction, and universe size are recorded in the same conceptual structure RAT-STATS uses.
Familiar outputs
Sample size, random seed, selected claims, point estimate, standard error, confidence bounds, and projected overpayment are documented in the SVRS and extrapolation PDFs and the claims workbook.
Workflow integration
SVRS plugs into the investigation lifecycle.
SVRS is not a standalone calculator. It runs inside the AEGIS investigation workspace, taking selected claim universes from Case Management cases, sending the sampled claims through Medical Review, and projecting the findings back to the universe for recovery.
See how sampling fits Medicaid program integrity and SIU investigations.
1. Define the universe
Investigators import claims into the case as claim universes and can narrow the sampling frame by CPT or HCPCS procedure codes. The universe size is recorded with the sample.
2. Configure parameters
Confidence level, margin of error, and expected error rate are configured through a guided form, starting from your organization's defaults. The form previews the Cochran's-formula sample size before the sample is drawn so reviewers can validate parameters first.
3. Generate the sample
AEGIS draws the sample using a seeded Fisher-Yates shuffle. The seed and the selected claims are saved with the sample, and the sampled claims are ready for medical review.
4. Determine each sampled unit
Reviewers complete medical-review determinations on each sampled unit using the Medical Review — including the QA Analyst stage for peer verification before final determination — and AEGIS records the dollar impact of each determination.
5. Extrapolate to the universe
AEGIS computes the Mean Per Unit point estimate, standard error, and lower and upper confidence bounds, scaled to the universe size, with the lower confidence bound highlighted as the conservative recovery target. Each saved extrapolation worksheet is kept as an append-only run.
6. Export the documentation
Generate the SVRS methodology PDF, the extrapolation PDF, and a claims workbook with or without claim lines, and save them to the case documents for the audit work paper package.
Compliance and defensibility
Reproducible, documented, audit-ready.
Reproducible records
Each sample keeps its parameters, sample size, random seed, and selected claims. Saved extrapolation worksheets are append-only, so a recalculation creates a new run instead of overwriting the last one, and activity is captured in the platform audit trail.
HIPAA-compliant safeguards
SVRS runs inside the AEGIS investigation workspace under the same role-based access, audit logging, schema-per-tenant isolation, and Business Associate Agreement framework described on our Security and HIPAA pages.
Methodology documentation
The SVRS PDF documents the sample parameters, sample size, random seed, and a methodology statement. The extrapolation PDF documents the point estimate, standard error, and confidence bounds.
Exportable outputs
The SVRS PDF, extrapolation PDF, and claims workbook can be saved to the case documents or downloaded for inclusion in an audit work paper package or referral.
FAQ
Common questions about AEGIS SVRS.
What sample sizes does AEGIS SVRS support?
Sample sizes are computed from the confidence level, margin of error, and expected error rate using Cochran's formula, with finite population correction for the universe size. Typical SIU and program-integrity audits range from 30 to a few hundred sampled units, and AEGIS supports any sample size the parameters and population justify.
Is the methodology defensible to auditors?
AEGIS SVRS is aligned with HHS-OIG RAT-STATS sampling conventions and records the statistics auditors look for: confidence level, margin of error, sample size derivation, random seed, and the Mean Per Unit point estimate with confidence bounds. The SVRS and extrapolation PDFs can be saved to the case documents for the audit work paper package.
Can we use our own confidence and error parameters?
Yes. Confidence level (90%, 95%, or 99%), margin of error, and expected error rate are set for each sample, and administrators can set organization-wide defaults. The parameters and random seed are saved with the sample so the draw can be reproduced.
Does AEGIS support probe and follow-up samples?
Yes. In addition to simple random samples, AEGIS supports probe, nested, and member-universe samples. Their extrapolations use a one-sided 90% lower confidence bound based on Student's t distribution, and each saved extrapolation worksheet is kept as an append-only run.
What documentation does SVRS produce?
An SVRS PDF with the sample parameters, sample size, random seed, and a methodology statement; an extrapolation PDF with the point estimate and confidence bounds; and a claims workbook with or without claim lines. Each can be saved to the case documents.
How does SVRS connect to medical review and recovery?
Sampled claims are reviewed in Medical Review, where reviewers record line-level decisions. The extrapolation uses those findings to project the overpayment to the universe, and recovery is then tracked as financial actions on the case in Case Management.
See SVRS in action
Tailor a demo to your audit and investigation workflows.
Tell us about your current sampling tools, the program-integrity activity SVRS would support, and the regulators or contracting bodies whose methodology disclosures matter to you. We will tailor a demo to show how SVRS fits the way your team actually runs audits.