Build a Hospital-Facing AI Oncology Decision-Support Platform

1. 1

   Make interoperability the core product

   The foundational value is standardizing fragmented, multi-source patient data (EHRs, labs, imaging, pathology) into one interoperable database that AI can actually use. As context, Iakan Health built its platform on exactly this data-standardization backbone. This unglamorous data engineering, not the model alone, is the real product and the hardest part to build.

2. 2

   Layer treatment and survival prediction models

   On top of the clean data, build models that predict treatment effectiveness and patient survival, the outputs hospitals and their partners value. Model quality depends entirely on the data foundation beneath it. Honest uncertainty and validation are essential for clinical use.

3. 3

   Validate rigorously for clinical trust

   Survival and treatment predictions influence high-stakes decisions, so rigorous validation and clear communication of limits are non-negotiable. Depending on claims, regulatory considerations may apply. Clinicians will not trust or adopt unvalidated predictions.

4. 4

   Sell to multiple enterprise buyers

   The platform can serve hospitals (clinical decisions), labs, biopharma (research and drug development), and insurers (risk and outcomes), which broadens the market but requires tailoring to each buyer's needs and procurement. Multi-buyer reach is a strength and a complexity. Enterprise sales cycles are long.

5. 5

   Handle data governance and compliance

   Aggregating patient data across sources raises HIPAA, consent, and data-use-agreement obligations that must be airtight. Data partnerships with health systems require careful contracting. Governance is a gating requirement for both compliance and customer trust.

6. 6

   Build defensibility through data and integration

   The moat is the accumulated interoperable dataset and the deep integrations with customer systems, which are hard for competitors to replicate. Invest in data breadth and integration depth. This enterprise-data position distinguishes the model from consumer-facing and single-modality AI cards in this file.

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