OnCo
ideasIdea

No mCODE, no payment: tie oncology reimbursement to a minimal structured record

Hospitals would only be paid for cancer treatment if they record a small, standard set of facts (diagnosis, stage, biomarkers, treatment, outcome) in a shared format that any computer can read.

mCODE (minimal Common Oncology Data Elements) is an HL7 FHIR implementation guide of roughly 90 elements covering the cancer patient, disease, genomics, treatment and outcomes. Adoption is voluntary and patchy. The proposal is for Medicare, the NHS and national insurers to make conformant mCODE capture a condition of payment for systemic anticancer therapy, phased over three years, mirroring how Meaningful Use forced EHR adoption in the US and how the NHS Systemic Anti-Cancer Therapy (SACT) dataset made chemotherapy reporting near-universal in England.

Hypothesis
Within three years of a payment mandate, more than 90 percent of treated patients in the paying system will have a complete, conformant mCODE record, and the time to answer a standard real-world question (for example, first-line treatment patterns for a new approval) will fall from years to weeks.
Rationale
Payment conditions are the only lever that has ever produced near-complete clinical datasets at national scale: SACT in England and Meaningful Use in the US both worked where voluntary standards did not. mCODE already has vendor implementations (Epic, Flatiron, Varian) so the marginal cost is configuration, not invention.
What would test it
A two-year pilot in one payer region (a US state Medicaid programme or one NHS integrated care system) paying a small per-record bonus for conformant mCODE capture, with completeness and timeliness audited against the SACT or registry gold standard; compare completeness with matched control regions.
Maturity
speculative
Who has to act
payer
Cost to try
Medium ($1M to $50M)
Years to first evidence
3
Bottlenecks it attacks
  • Data silos · Records, scans, genomes and outcomes sit in separate systems that cannot talk. Every patient's experience is lost to the next.
  • Weak real-world evidence and registries · We do not reliably know what happens to patients after approval, so we cannot tell which drugs deliver in practice.

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Real-world evidence

bottlenecks

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Data silosWeak real-world evidence and registries