You’re the PM for OncoGuide, a Series D healthcare AI company that sells a clinical decision support product to 120 US oncology clinics (mix of academic centers and community practices). OncoGuide integrates into Epic and Cerner via FHIR and is used by ~3,500 oncologists and ~90,000 active cancer patients/year across partner sites.
OncoGuide’s revenue model is per-site annual contracts ($250K–$1.2M ARR depending on volume) plus a smaller per-test fee when the product triggers molecular testing workflows. The company is competing with Tempus, Foundation Medicine, and several EHR-native analytics tools. Your differentiation today is workflow integration and fast turnaround on real-world evidence insights.
The CEO wants to expand into a high-stakes module: predicting patient response to immunotherapy (primarily PD-1/PD-L1 and CTLA-4 inhibitors) to help oncologists decide whether to prescribe immunotherapy, combine it with chemo, or pursue alternative regimens.
Immunotherapy is clinically transformative for some patients but expensive and risky:
| Persona | Setting | Primary Job-to-be-Done | Key Pain Points |
|---|---|---|---|
| Community Oncologist (Dr. Patel) | 20–40 patients/day | “Choose a regimen I can defend to patient + payer quickly.” | Limited time; inconsistent biomarker availability; fears missing a responder. |
| Academic Oncologist (Dr. Nguyen) | Tumor boards, trials | “Personalize therapy using the latest evidence.” | Wants transparency and subgroup evidence; skeptical of black-box scores. |
| Oncology Pharmacist (Sam) | Prior auth + safety | “Ensure safe, guideline-aligned therapy.” | Needs documentation; worries about off-label suggestions and toxicity. |
| Patient (Maria, 58) | Newly diagnosed metastatic NSCLC | “Understand my odds and trade-offs.” | Confused by probabilities; anxious about side effects and costs. |
OncoGuide already ingests:
However, data quality is uneven:
Clinics are asking for help answering: “Is this patient likely to respond to immunotherapy, and what’s the expected benefit vs risk?”
Your internal analysis across partner sites suggests:
Commercially, the sales team reports that 30% of renewal conversations mention “need stronger treatment selection support,” and two large health systems have threatened churn unless OncoGuide can show measurable impact on outcomes and cost.
Design the product (not just the ML model) that delivers immunotherapy response prediction in a way clinicians will trust and adopt.
We’re evaluating your ability to translate a complex ML capability into a safe, adoptable product: crisp problem framing, user-centered design, prioritization under constraints, and measurable success criteria with clear trade-offs.