The pattern layer that gets sharper as your case volume grows. BETA
Cross-Case Pattern Intelligence software from Medrecords AI builds a tenant-separated, privacy-preserving baseline from the cases already processed in your own account, sharpening duplicate detection, anomaly signals, and benchmark comparisons as that history grows. Nothing crosses into another organization's data; the model improves inside the walls of your own tenant.
Duplicate detection, calibrated to your own casework.
Every duplicate your account has already caught — the 11 removed from Case #IME-4812 among them — becomes a reference point for the next file. Repeated exposure to your own document structures and packet patterns sharpens the match threshold instead of relying on generic heuristics alone.
Anomaly signals that learn your case mix.
A quarantined page like p.140 — a wrong-patient insert caught in Case #IME-4812 — isn't just flagged once. Its shape becomes part of what the platform watches for across your future files, so the anomaly detector gets more attuned to what actually shows up in your tenant's casework.
Benchmarks that sharpen as your volume grows.
Every resolved case in your account adds another comparable point to your own Case Outcome Benchmarks. If you'd rather compare against cases outside your organization too, that's a separate opt-in program — Private Benchmark Network — not something this feature does on its own.
Your data trains your account. Never anyone else's.
Cross-Case Pattern Intelligence never claims shared-model training unless that is technically and contractually true for your account. No other organization's identifiable case data is ever exposed to your tenant, and none of yours is exposed to theirs.
Model improvement stays clearly distinct from customer-specific retrieval and from aggregate benchmarking. If you want to compare against cases outside your own organization, that's Private Benchmark Network — a separate, opt-in program, never something this feature does by default.
From processed cases to a sharper baseline.
Three steps, nothing you have to configure — it runs alongside the features you already use.
No extra step — duplicate detection, anomaly flags, and benchmarks already run on every case you process.
Patterns from your own duplicate catches, anomalies, and resolved cases are compiled inside your tenant, nowhere else.
Duplicate detection, anomaly signals, and benchmark comparisons calibrate to your own case mix as volume grows.
Who runs on a sharper baseline.
Any team processing volume inside one account benefits — the baseline is built from your own file mix, whatever that is.
Duplicate and anomaly detection calibrated to your own claim mix.
For carriersA baseline that reflects the specific book of business you manage.
For TPAsRepeat clients and repeat defendants surface faster over time.
For law firmsAnomaly detection tuned to the packet patterns your evaluators actually see.
For IME orgsCross-Case Pattern Intelligence, answered.
It's a pattern layer that builds a tenant-separated baseline from the cases already processed inside your own account, then uses that baseline to sharpen duplicate detection, anomaly signals, and benchmark comparisons over time. The more your account processes, the more calibrated those features get.
No. Cross-Case Pattern Intelligence never claims shared-model training unless that is technically and contractually true for your account. By default, the baseline is built and used entirely inside your own tenant — no other organization's identifiable case data is exposed to your account, and none of yours is exposed to theirs.
Cross-Case Pattern Intelligence improves your own account's features from your own case history — nothing leaves your tenant. Private Benchmark Network is a separate, opt-in program where participating organizations contribute de-identified, aggregated data to a shared benchmark. The two are not the same feature, and one never implies the other.
Yes, in beta. Cross-Case Pattern Intelligence is live and testable now; we're refining it hands-on with early customers, and if your case volume and use case are a good fit, we'll work with you directly on calibration.
Only cases already processed inside your own account: document structure, duplicate patterns, and the anomaly and benchmark signals already surfaced elsewhere on the platform. It does not retrieve records from providers, and it does not read cases outside your tenant.
Related capabilities.
A structured fingerprint per case, powering cohort matching.
The opt-in program for pooling de-identified data across organizations.
The base detection layer this pattern baseline sharpens over time.
Anomaly flags that benefit from the same tenant baseline.
Let your own case history make the platform sharper.
Join the beta and we'll calibrate the baseline against your own case mix. Handled under our BAA; never used to train a shared model without your consent.