# Case Outcome Benchmarks — Medrecords AI > Success-likelihood and case-value ranges from comparable resolved cases — matched on injury, treatment, and venue, every driver cited to your record. Canonical page: https://medrecords.ai/product/benchmarks/ --- New — **Missing Records Detection:** flags every visit, provider, and date missing from the file. [See how →](https://medrecords.ai/product/missing-records-identification/) [Home](https://medrecords.ai/) ›[Platform](https://medrecords.ai/product/chronology/) › — Case Outcome Benchmarks Case Outcome Benchmarks ## Know the range before you set the number. "Ranges from comparable resolved cases — every case-side driver cited to your record." Case outcome benchmarks from Medrecords AI give success-likelihood and indicated-value ranges built from public court records and reported settlements — matched on injury, treatment course, and venue, state by state. The drivers come from your cited record; the cohort is always shown; the valuation lives in a work-product workspace, never in the evidence file. [Book a demo](https://medrecords.ai/demo/) [Test a file ](https://medrecords.ai/test-a-file/?src=product-benchmarks) Valuation · Ochoa v. Delgado — work product · separate file Success likelihood — 72–81% Indicated range — $145k–$240k · median $186k + — Objective imaging — 4.2 mm herniation — MRI·s3·sl18 + — ESI injection + surgical consult — p.318 − — 6-week treatment gap — flag §4 + — No documented priors — p.06 1,284 comparable resolved cases · TX venues · 2019–2026 — ranges, not promises Ranges — with the cohort behind them, never a point guess State-by-state — venue-level benchmarks from public court data 0 bleed — valuation never touches the attested evidence file ### From your evidence to a defensible range. Pipeline 01 Drivers from your record Imaging findings, treatment course, gaps, priors: each one cited to its page or slice. 02 Cohort matched Comparable resolved cases from public court records and reported settlements (injury, treatment, venue, era). 03 Ranges computed Success likelihood and indicated value with the spread shown — percentiles, not a single number. 04 Kept separate A work-product workspace. Nothing crosses into the attested record, exports, or chronology. ### Grounded in the evidence. Honest about the data. Evidence-derived drivers Valuation factors come from your cited record: imaging findings, injections, surgical recommendations, gaps, priors. Click any driver to the page it came from. Comparable-case cohorts Resolved verdicts and reported settlements matched on injury, treatment course, venue, and era. The cohort size and composition are always on screen. Ranges with the spread shown Success likelihood and indicated value as percentile ranges. Where the data is thin, it says so. No false precision. Venue intelligence, state by state County and state-level differences surfaced from public court data: the same injury does not resolve the same way in every venue. Work-product separation Valuation lives in its own workspace with its own exports. It never appears in the attested record, the chronology, or a served document. Benchmark your book For carriers and TPAs: reserve and settlement benchmarking against the cohort — the determination stays with the adjuster, logged and auditable. Framed so it holds up Outcome prediction fails when it pretends to be certain. 4 guardrails are built in: Decision support, not legal advice Counsel sets the demand; the adjuster makes the determination. The range informs the call, it never makes it. Discovery-aware by architecture Work-product workspace, separated exports, case-scoped access, every view logged. Selection bias, stated Tried cases over-represent public data. Cohorts disclose composition and confidence instead of hiding it. Evaluators excluded (by design) IMEs and QMEs never see outcome data. Their neutrality is their product, and this protects it. Built on the same spine as everything else here: [if we can't cite it, we don't say it](https://medrecords.ai/product/citations/) — for drivers, that means your record; for benchmarks, the cohort. ### The same range, 4 different decisions. One cohort, read through each team's lens: demand, reserve, merit, exposure. [ Personal injury Set the demand with a range you can defend to the client — and to the mediator. For PI firms ](https://medrecords.ai/solutions/personal-injury-plaintiff/) [ Claims & carriers Reserve against the cohort, not the anchor, and see which files are outliers early. For insurance ](https://medrecords.ai/solutions/insurance/) [ TPAs Benchmark outcomes across clients, desks, and jurisdictions with one yardstick. For TPAs ](https://medrecords.ai/solutions/tpas/) [ Legal nurse consultants Screen merit with a range attached — a stronger handoff than a gut read. For LNCs ](https://medrecords.ai/solutions/legal-nurse-consultants/) Independent evaluators never see outcome data, by design. Neutrality is their product. FAQ ### Outcome benchmarks, answered straight. Neither. It is decision support: a range built from comparable resolved cases plus the cited drivers in your own record. Counsel sets the demand and the strategy; the adjuster makes the determination. The range informs the call. It never makes it. Public court records, verdict reporters, and reported settlement data across U.S. jurisdictions, refreshed on a rolling basis. Every benchmark shows its cohort: size, composition, venues, and era — and thin cohorts are labeled as thin instead of extrapolated. It is built not to. Valuation lives in a work-product workspace with its own exports, separate from the attested record, the chronology, and anything you serve. Access is case-scoped and logged. Consult your own counsel on privilege practice — the architecture is designed to support it. The case-side drivers are extracted from your cited record: objective imaging findings, injections and surgical recommendations, treatment gaps, documented priors — and each driver links to the page or DICOM slice it came from. The cohort supplies the base rates; your record supplies the adjustments. Because the data is honest only as a distribution. Verdicts over-represent tried cases, venues differ, and injuries resolve differently by treatment course. You get percentile ranges with the spread shown. False precision is how valuation tools lose credibility. No, deliberately. IMEs, QMEs, and peer reviewers never see outcome data on this platform. An evaluator whose tooling predicts case value hands opposing counsel a bias argument; excluding them protects the opinion. ### Price the next case against the evidence. Bring a closed file you already know the answer to, and see where the range would have put you. [Book a demo](https://medrecords.ai/demo/) [Test a file ](https://medrecords.ai/test-a-file/?src=product-benchmarks)