A qui tam relator alleges that a physician-owned toxicology lab paid independent marketers for every specimen they steered its way. Discovery produces the usual mountain: three years of claims data with CPT codes and rendering providers; bank records for the lab and two affiliated LLCs; marketing agreements; commission ledgers; and a phone log between the lab’s owner and its top referring physicians.
The case team does what most case teams do. Claims data goes in one spreadsheet tab. Wire transfers go in another. The commission ledger gets its own workbook. Someone builds a pivot table of referral volume by provider. Someone else color-codes the payments that landed within a week of a referral spike.
Six weeks later, the team knows a great deal about each dataset and almost nothing about how they connect. That is not a diligence problem. It is a tool problem.
The scheme is a relationship, not a row.
Anti-Kickback Statute and Stark Law theories live or die on relationships. Remuneration flowing to a referral source. A physician with a financial interest in the entity billing for his orders. A marketer sitting between a lab and a dozen practices, paid per specimen through a shell that never appears on a claim form.
None of that is visible in any single row of any single spreadsheet. The claims data shows a physician ordering tests. The bank records show an LLC receiving wires. The commission ledger shows a marketer getting paid. The scheme only exists in the connections between those three facts, and connections are exactly what spreadsheets are built to ignore. Every tab is an island. Every VLOOKUP between tabs is a manual bridge someone has to think to build, and the bridges nobody thinks to build are where the case hides.
A second problem is volume. A mid-size FCA matter can involve hundreds of thousands of claim lines, thousands of transactions, and dozens of entities with overlapping ownership. Past a few thousand rows, spreadsheet review stops being analysis and becomes sampling. You find what you already suspected and miss what you never thought to filter for.
What the same evidence looks like as a network
Load that same discovery into SentVi, and the case changes shape. Claims data, bank records, phone logs, and corporate records import into one knowledge base, and every physician, entity, account, and payment becomes a node or a relationship on a link chart.
Now the questions that took weeks of cross-tab work become visual:
Who actually connects the lab to its referring physicians? Social network analysis ranks centrality across the whole network. The marketer who appears nowhere on the claims data lights up as the most connected node in the chart, because the phone records, the commission ledger, and the LLC bank account all run through him.
Does money track referrals? SentVi’s timeline view puts remuneration and referral volume on the same axis. When commission payments step up in March, and a practice’s specimen volume doubles in April, you see it as a pattern across the network, not as a coincidence you have to argue from two separate exhibits.
Where does the money actually go? Shortest-path analysis traces the route between any two entities. Lab to shell LLC to marketer to a management company owned by a referring physician’s spouse: four hops that would take a paralegal a week to assemble from bank records, rendered in seconds.
This works for defense as well as for relators and the government. If the payments your client made track fair market value services and not referral volume, the network view shows that too, and shows it in a form you can put in front of opposing counsel or a mediator.
From chart to case
The output is not just an internal tool. SentVi link charts export to reports, images, and presentation-ready visuals, and the free SentVi Reader lets co-counsel, experts, and clients open interactive charts and drill into the underlying evidence without a license. The relationship map that organized your investigation becomes the demonstrative that explains it.
Healthcare fraud enforcement is not slowing down, and neither is the size of the datasets these cases generate. The teams that keep working them one tab at a time will keep finding what they already suspected. The teams that map the relationships will find the scheme.
If you have a matter with claims data, bank records, and a theory that lives in the connections, run it through SentVi. The free trial works on your own data, and the first chart usually makes the argument for you. Get started by requesting a demonstration today: Book a demo
