AI agent evaluation in Philadelphia.

The technical reality of institutional evaluation in Philadelphia.

Questions from Philadelphia buyers.

How do we verify AI-drafted payer and provider correspondence is faithful before specialists sign it?

Claim-level verification against the case record, run before the draft reaches the queue. The harness decomposes each draft (appeal response, records request reply, denial correspondence) into atomic assertions and verifies each against the case file and policy library: supported, unsupported (the fabrication class: asserting clinical facts or policy provisions not in the record), contradicted (misstating what the record shows: the class that loses appeals), and omitted (material record facts missing: the class that weakens cases silently). Required regulatory language gets deterministic checks per correspondence type and jurisdiction. Calibrated judges carry the volume after agreement with your appeals specialists' grades is demonstrated, with gates tiered by visibility: external and DOI-facing correspondence holds the strictest bars. The specialist's review stays, transformed: reading a verified draft with flagged uncertainties is a minutes-long check; reading an unverified one defensively is the half-hour the automation was meant to recover. The edit-distance logs then prove which one your program is, with numbers.

Litigation has decades of TAR validation practice. How does LLM-based review tagging fit that frame?

Better than most new technology, because the validation vocabulary already exists. Courts and opposing parties evaluating technology-assisted review expect documented protocols: how the system was trained or configured, validation sampling with statistical bounds (elusion and recall estimates from blind control sets), and defensible documentation of the workflow. LLM-based tagging slots into exactly that frame: a blind control set drawn and coded by attorneys establishes ground truth; the system's recall and precision per issue tag get measured against it with confidence intervals; elusion testing on the discard pile bounds what responsive material the process might have missed; and every prompt and model version is logged so the protocol survives a meet-and-confer or a motion. What changes from classic TAR is favorable: configuration is inspectable prompt text rather than opaque model weights, and per-issue performance is measurable cheaply enough to validate continuously rather than once. We build the harness and the protocol document together, in the vocabulary your litigation-support team and outside counsel already defend.

Can evaluation verify every claim in generated pharma content traces to the approved library?

Yes, and traceability is the property that makes generated commercial content MLR-viable at all. The gate: your MLR-approved claim library (claims, approved phrasings, required fair-balance pairings, reference list) is the structured source of truth; generated assets (rep emails, web copy, congress materials) get decomposed into claims; and every claim must trace to a library entry with its approved phrasing intact and its fair-balance requirements satisfied, with untraceable claims, drifted phrasings, and orphaned superlatives flagged before the asset enters MLR review. The evaluation layer on top verifies the gate itself: seeded-violation suites (drafts with known drift: a comparative claim the library never approved, a dropped qualifier, an off-label implication) measure the gate's recall, which is the number your MLR leadership actually needs to trust the pipeline. The operational effect compounds: assets entering review pre-verified cut cycles, and the gate's recall history becomes part of your commercial-compliance documentation. The boundary stays bright: MLR approves; the gate ensures what reaches MLR was built from what MLR already approved.

What does committee-grade reporting mean for evaluation results in institutional settings?

Reporting built for the people who own the risk but not the technology, in the format their committees already trust. The structure: a front page in institutional units (error rates per correspondence type with confidence bounds, faithfulness rates against the thresholds the committee approved, trend lines since last review), an explicit exceptions section (what failed, what was done, what changed: committees trust reports that surface problems more than reports that lack them), methodology in an appendix (sampling design, judge calibration provenance, versions) for the member who asks how the numbers were made, and re-derivability throughout: every figure traceable to versioned runs a skeptical auditor could re-execute. Cadence matters as much as content: quarterly summaries aligned to existing governance calendars, with interim alerts only for band breaches, because committees manage by exception. We build the report template with your governance owner in week one, since the artifact that satisfies a hospital compliance committee, a firm's professional-responsibility partner, and a pharma quality council differs in dialect even when the underlying evidence is identical.