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Eval Harness Build Add-On — 4 Weeks, $40K, Production-Grade Healthcare AI Evaluation

A healthcare AI feature with no eval harness is a feature nobody can defend. Not in front of a hospital security committee. Not in front of an FDA reviewer asking how model performance is monitored. Not in front of a lawyer in a discovery deposition six months after a missed diagnosis. The eval harness is what turns “we tested it” into “we can show you the test results, the test set methodology, the calibration curve across patient cohorts, and the drift detection alerts from the past 90 days.” Without it, the team has nothing to point at.

This add-on builds that harness. Four weeks. $40,000 fixed. Production-grade evaluation framework with healthcare-specific metrics, clinical test set, drift monitoring, and reporting layer ready for hospital security review and ongoing operations.

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Why Healthcare AI Eval Harnesses Are Fundamentally Different

The general AI evaluation tools — LangSmith, Braintrust, Arize, Phoenix, Comet Opik — handle a lot of what production AI evaluation needs. Trace capture. LLM-as-judge. Dataset versioning. Regression testing. These tools work well and we use them as foundational infrastructure when appropriate.

What general tools do not handle, and what healthcare AI eval harnesses have to handle, falls into four areas:

Ground truth from de-identified historical encounters, not curated test sets. A general AI eval suite uses curated input-output pairs. A healthcare AI eval suite has to derive ground truth from messy historical clinical encounters where the actual outcome is sometimes ambiguous, sometimes only knowable months later, and sometimes contested across clinicians. Test set construction is itself a clinical methodology problem, not a software problem.

Clinical metrics that depend on the condition and the workflow. Sensitivity matters disproportionately in sepsis prediction — missing a real case has worse consequences than a false alarm. Specificity matters disproportionately in routine screening — overcalling causes alert fatigue and unnecessary workups. Calibration matters for any output that drives a clinical decision. PPV and NPV depend on prevalence in the specific patient population. The right metric depends on the clinical use case; the wrong metric will optimize the model for the wrong outcome.

Fairness across patient cohorts. A model that performs at 92% sensitivity overall but 79% on patients with darker skin tones, or 84% on female patients, or 76% on patients over 75, is not safe to deploy unsupervised. Healthcare AI eval has to slice performance by patient demographics and surface disparities. The 2024–2025 FDA guidance on AI/ML SaMD makes this explicit.

Override-rate and clinician-trust signal. Clinicians voting with their feet — by overriding or ignoring AI output — is one of the most powerful signals about real-world model performance. Eval harnesses for healthcare AI have to capture override data from production and feed it back into model improvement.

These differences are why a general AI eval tool is not enough. The general tool is the substrate; healthcare-specific metrics, methodology, and reporting are the layer this add-on builds on top.

The Four-Week Build Schedule

Two two-week phases. Each ends with a working artifact.

Phase 1 (Weeks 1–2) — Test Set Design and Metric Selection

The team works with the clinical sponsor to define the success criteria for the AI feature, derives test cases from de-identified historical encounters (typically 200–1,000 cases for an initial harness), and selects the metrics that match the clinical use case. Sensitivity-weighted versus specificity-weighted? Calibration curve thresholds? Fairness slicing across which patient demographics? The selection is documented and signed off by the clinical sponsor before any code is written.

End of Phase 1: a written eval methodology document with test set definition, metric selection rationale, and acceptance thresholds. This is the document that goes into hospital security review and (if applicable) FDA submission.

Phase 2 (Weeks 3–4) — Eval Harness Build and Reporting Layer

The engineering team builds the actual harness. Test runner integrated with the AI feature. Metric calculation. Cohort slicing for fairness. Calibration plot generation. Drift monitoring scaffolding. Reporting layer that produces the documentation hospital security review will reference. CI/CD integration so eval runs automatically on every model or prompt template change.

End of Phase 2: a working eval harness, executed once against the current AI feature with results captured. Reporting layer produces a model performance report that goes into the Pilot-Ready Sprint deliverables.

Five Components Every Healthcare AI Eval Harness Needs

1. Test set with clinical ground truth. Derived from de-identified historical encounters. Curated to cover the clinical scenarios the AI is expected to handle, plus edge cases that test the boundary of safe behavior. Cohort-stratified by demographics for fairness evaluation.

2. Metric calculation framework. Sensitivity, specificity, PPV, NPV, calibration error, F1 (when appropriate), AUROC, AUPRC. Healthcare-specific metrics: time-to-detection for early warning models, RECIST measurement accuracy for oncology imaging, RPM alert false-positive rate. Selected per use case, not one-size-fits-all.

3. Cohort slicing for fairness. Performance broken out by age band, gender, race/ethnicity (where ethically appropriate to measure), insurance status, and primary spoken language. Disparities surfaced as separate metrics.

4. Calibration and confidence reporting. Is the model confident when it should be confident and uncertain when it should be uncertain? Calibration curves with confidence intervals. Threshold-based suppression patterns when calibration is below the safety bar.

5. Drift detection scaffolding. Streaming evaluation against production traffic. Drift alerts when current performance diverges from baseline beyond defined thresholds. Tied to incident response runbook. Full drift monitoring is operational in Pilot-Ready Sprint — the harness from this add-on is what Pilot-Ready turns into live monitoring.

Healthcare-Specific Metrics That General Eval Tools Miss

The metrics general AI tools track well — task accuracy, latency, cost — are necessary but not sufficient. Healthcare AI eval needs these additional metrics:

Sensitivity at clinically meaningful thresholds. A binary classifier’s headline accuracy is less useful than its sensitivity at the threshold actually used in production.

Specificity-weighted error analysis. When false positives drive alert fatigue, the cost of a false positive matters more than the headline error rate.

Calibration error (Brier score, expected calibration error). A model that says 70% confidence should be right 70% of the time.

Decision-impact analysis. When the model is wrong, does the clinician usually catch it, or does the wrong output propagate into the chart?

Time-to-decision. For workflow AI features, the latency budget is measured against clinical workflow constraints (a 4-second response in a triage workflow is too slow).

Override-rate as outcome. Clinicians overriding the AI more than X% of the time signals model degradation, workflow mismatch, or training data drift.

Cohort-specific performance. Slice by demographics, by clinical sub-population, by encounter type.

Section 05

How This Connects to Discovery, MVP, Pilot-Ready

The eval harness is a thread that runs through the productized Sprint pathway.

Discovery Sprint. Produces the eval harness skeleton — test set design, metric selection, methodology — as one of its five outputs. The skeleton is the input to this add-on.

MVP Sprint. Builds the AI feature itself, runs the eval harness scaffolded during Discovery, surfaces edge cases that get fed back into the harness.

Eval Harness Build Add-On. When the eval harness needs to be production-grade rather than scaffolded — particularly when FDA SaMD work is in scope, or when SOC 2 / HITRUST evidence requires a fully built harness — this add-on builds the production version in 4 weeks.

Pilot-Ready Sprint. Takes the eval harness from this add-on and wires it into live drift monitoring. Eval harness moves from batch to streaming evaluation against production traffic.

Most engagements do not need the add-on as a separate engagement — the scaffolded harness from Discovery, completed during MVP, is sufficient. The add-on is for engagements where the harness needs to be deeper, more rigorous, and submission-ready earlier than the standard Sprint pathway produces.

When This Pairs With FDA SaMD Work

FDA submissions for AI/ML SaMD require a clinical evaluation methodology that meets FDA’s expectations under the 2024–2025 AI/ML guidance. This eval harness add-on produces submission-quality evidence:

Documented methodology that survives FDA reviewer scrutiny

Performance metrics across the IMDRF risk-relevant patient cohorts

Calibration evidence supporting any confidence-based suppression patterns

Fairness slicing documentation

Drift monitoring evidence supporting the Predetermined Change Control Plan (PCCP)

When This Is the Right Engagement (and When the Standard Pathway Is Enough)

This add-on is the right call when:

FDA SaMD submission requires a submission-quality eval harness earlier than Pilot-Ready Sprint produces it

SOC 2 Type II or HITRUST r2 audit requires documented eval methodology and operational eval harness as evidence

A clinical pilot is launching and the eval harness needs to be production-grade before pilot Day 1

The buyer is bringing an AI feature from elsewhere that has no eval harness, and the standard Sprint pathway would take longer than acceptable

Engagement Logistics

Pricing. $40,000 fixed. Two payment milestones: 50% on contract signature, 50% at end of Phase 1 (eval methodology document signed off).

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FAQs

Frequently Asked Questions About the Eval Harness Build Add-On

Yes. We commonly build the healthcare-specific layer on top of one of these general tools rather than building everything from scratch. The general tool provides the eval infrastructure (test runner, dataset versioning, trace capture, LLM-as-judge). The add-on provides the healthcare-specific layer — clinical metrics, cohort slicing, calibration analysis, ground truth methodology, FDA-aligned reporting. Tool selection is discussed during the kick-off based on your existing infrastructure.

De-identified historical encounters from the buyer’s existing clinical data. Either from the EHR via FHIR Bulk Data Export or from a clinical data warehouse if the buyer has one. The de-identification methodology — HIPAA Safe Harbor or Expert Determination — is selected during Phase 1. For research-grade datasets, partnerships with academic medical centers are sometimes appropriate but are typically separate from this engagement.

The eval harness is a diagnostic, not a verdict. When the AI does not meet the safety bar, the eval results tell you which scenarios are failing, which cohorts have disparities, and where the model is overconfident. Those findings feed back into prompt tuning, retrieval improvement, model re-selection, or scope reduction. The eval harness is meant to surface these issues before the clinical pilot, not after.

Two paths. (1) Internal engineering team maintains it, with the add-on producing the codebase, documentation, and runbooks they need. (2) Ongoing maintenance through Care Packages — eval harness execution, refresh, and reporting included in Standard and Premium tiers.

Less rigor is appropriate for administrative or workflow-only AI features that do not affect clinical decisions. Ambient documentation that summarizes a visit is lower-risk than a clinical decision support feature that suggests a diagnosis. The eval harness scope reflects the clinical-decision-impact of the feature. For workflow-only AI, the scaffolded harness from Discovery is usually sufficient.

Yes, with scope expansion. The base $40K add-on covers one AI feature. Each additional feature adds approximately $15K–$25K depending on test set overlap. For a buyer running 4–6 AI features across a platform, dedicated engineering rather than per-feature add-ons is usually the right model — hire healthcare AI engineers at $8K per engineer per month for ongoing eval and AI engineering work.

Drift detection scaffolding is part of the add-on output. Operational drift monitoring against production traffic is part of Pilot-Ready Sprint, which wires the harness into live streaming evaluation. Ongoing drift operations are part of Care Packages at all tiers.