FDA-deliverable outcomes evidence without building from scratch
Every healthcare AI company that pursues serious enterprise sales or FDA submission eventually needs a paired-clinician outcomes study — pre-registered, IRB-approved, blinded reviewer panel, reproducible statistics. Building it from scratch costs six-figure budgets and 9–12 months. Skippy Outcomes Eval sells the methodology Skippy built for its own clinical-value evaluation: pre-validated protocol templates, 50+ board-certified reviewers, reproducible analysis pipelines, and FDA SaMD-formatted output. First, we prove it on ourselves. Then we sell it.
Accuracy benchmarks don't answer the outcomes question
FDA SaMD pre-submissions, EU AI Act high-risk-system conformity, and serious enterprise health-system pilots all eventually ask: “Show me evidence that using your AI improves clinical decisions.” LLM accuracy benchmarks don't answer that. Citation-grounding benchmarks don't answer that. The answer is a paired-clinician decision-quality study with blinded expert review, pre-registered hypotheses, and IRB-approved protocol.
Skippy Outcomes Eval reduces IRB review to 4–6 weeks and total time-to-completion to 5–8 months — by starting from proven-acceptable templates rather than building from scratch.
Every component needed for FDA-grade outcomes evidence
Pre-registered protocol template
The OSF/ClinicalTrials.gov registration template built for Skippy's own clinical-value study — customized per engagement. Primary hypothesis structure, within-subjects paired design, sample-size calculator, and pre-specified analysis plan. Customers customize ~15%; the rest is proven-acceptable by prior IRB reviews.
IRB protocol package
Pre-formatted IRB submission documents: protocol synopsis, informed consent template, recruitment materials, data-management plan, risk/benefit analysis. Reduces IRB approval time from 8+ weeks to 4–6 weeks because reviewers see a familiar, structurally sound protocol.
50+ board-certified reviewer panel
Skippy maintains a network of 50+ board-certified clinicians across primary care, oncology, cardiology, psychiatry, and emergency medicine. Per-engagement assignment with blinding enforcement. Inter-rater reliability (κ ≥ 0.7) required before scoring begins.
Pre-validated statistical analysis pipelines
Reproducible analysis containers: paired t-test + Wilcoxon fallback, bootstrap 95% CIs (1,000 resamples), Cohen's κ + Krippendorff's α for inter-rater reliability, Holm-corrected subgroup analysis, and pre-registered sensitivity analyses. Every engagement runs the same containers — output is bit-identical for fixed inputs.
Five-dimension decision-quality rubric
Accuracy, citation appropriateness, uncertainty acknowledgment, boundary recognition, and patient-context integration — a vendored rubric customizable per use case. Domain-specific dimensions available for oncology, primary care, and psychiatry engagements.
FDA-deliverable output format
Final analysis formatted to match FDA SaMD clinical-evaluation submission expectations. Pre-registration reference, blinded reviewer panel provenance, statistical analysis containers, and deviation documentation — structured to LOI → Qualification Plan → Data Submission workflow.
Sized to your study requirements
| Tier | Scope | Price | Timeline |
|---|---|---|---|
| Pilot | Single specialty · 100 paired cases | Custom | 5–6 months |
| Standard | Multi-specialty · 200–400 paired cases | Custom | 6–8 months |
| Enterprise | Multi-site · 400–800 paired cases · multi-IRB | Custom | 7–10 months |
| Annual repeat | Repeat study, prior methodology reused | Custom | 4–6 months |
Compares favorably to from-scratch build cost for equivalent quality.
Healthcare AI companies that need to show their evidence
Skippy Outcomes Eval will not be available until Skippy has completed its own outcomes study and published the results. Selling methodology before using it yourself is hollow. The pre-registration, reviewer panel, and analysis pipelines are built for Skippy's own clinical-value evaluation first — per SWOT plan 15. The first external customer engagement begins only after that paper is published. Protocol templates will be open-sourced on OSF. The value is the panel, the execution, and the analysis infrastructure — not the template.
Need outcomes evidence for your healthcare AI?
Join the waitlist. We sequence pilots after Skippy's own outcomes paper publishes.