When the Patient Builds Better AI Than the Hospital

By Kamil Banc | November 14, 2025
last verified: 2025-11-14
strategytoolsimplementation

cat claims.txt

[1] AI Catches Specialist Misdiagnosis

Steve Brown used AI preparation before oncologist appointments to catch a misdiagnosis that multiple specialists had missed.

[2] Two Hours Preparation Pattern

Brown spent two hours with AI before each monthly oncologist appointment rehearsing conversations and testing specific hypotheses.

[3] Mutation-Based Drug Discovery

AI preparation surfaced drug alternative based on Brown's tumor mutations which Mayo Clinic confirmed leading to remission.

[4] Non-Technical Patient Success

Lisa Booth uses CureWise AI system for metastatic breast cancer treatment preparation without any programming background required.

[5] Research Time Reduction

Structured AI preparation reduces vendor research time from six hours of manual work to forty minutes of synthesis.

cat evidence.txt

quote

"Cancer grows exponentially. Delaying the right decision by three months changes survival odds."

Kamil Banc
statistics

  • 10 minutes per month

    Average time patients get with oncologists to make cancer treatment decisions

  • 2 hours preparation

    Time Steve Brown spent with AI before each oncologist appointment

  • 6 hours to 40 minutes

    Reduction in vendor research time when using AI for synthesis versus manual research

sources
cite: kbanc.com/claims-library/when-the-patient-builds-better-ai-than-the-hospital

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Banc, Kamil (2025, November 14, 2025). When the Patient Builds Better AI Than the Hospital. AI Adopters Club. https://aiadopters.club/p/when-the-patient-builds-better-ai

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Banc, Kamil (2025). When the Patient Builds Better AI Than the Hospital [Structured Claims]. Retrieved from https://kbanc.com/claims-library/when-the-patient-builds-better-ai-than-the-hospital

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context

Brown's methodology involves five structured steps: dumping full context into AI, requesting three conflicting recommendations, prompting AI to argue against preferred options, identifying knowledge gaps, and rehearsing conversations. The pattern was developed through Brown's experience with a rare cancer diagnosis and has been formalized into CureWise, a system now used by other cancer patients. The approach requires no coding skills and can be adapted for business contexts including project approvals, vendor evaluations, and performance reviews. The key insight is using AI to prepare specific hypotheses rather than vague questions, enabling more productive use of limited expert time.

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