Close the Gap: Hematology

Clinical data tells the story pathology cannot.

Roughly 8–12% of hematologic malignancies are clinically diagnosed rather than pathologically confirmed (Deppen et al., 2020; CDC USCS, 2025). Without deliberate data curation, they may never be reviewed or abstracted by the cancer registry.

When medical disease indices (MDI) are pre-filtered and do not include all ICD-10 codes, critical encounters, i.e., CBC trends, infusion records, chemotherapeutic agents, or other cancer-directed prescriptions, can slip by unnoticed. Each missed case erodes cancer registry credibility, distorts hospital analytics, and weakens the national story told through surveillance data.

What to look for beyond pathology

• Leukemias: CLL, SLL, CML, AML, ALL, MPNs diagnosed from peripheral blood, flow cytometry, or molecular studies.
• Myeloproliferative neoplasms: PV, ET, MF, CEL confirmed through JAK2, CALR, or MPL mutations and CBC trends.
• Plasma cell disorders: multiple myeloma, MGUS, amyloidosis defined by M-protein and imaging rather than biopsy.
• Palliative or end-stage: frail, end-stage patients, or hospice referrals without procedural confirmation.

Even a “normal” bone marrow report does not exclude a reportable case. Completeness begins where curiosity extends—across the MDI, medication lists, oncology notes, and molecular results that bridge laboratory and clinical data.

Which codes reveal the hematologic cases?

MDI case finding review should include these code sets:

• ICD-10 diagnostic codes: C81.x–C96.x, D47.x, Z85.6x
• Symptom-related codes: D64.x, R63.4, R53.83, R59.x
• HCPCS/chemotherapeutic codes: J9312, J9041, J9025, J9047, J9311, J9211, etc

Efficient case finding and pre-abstraction of all hematology can be accomplished in real-time, without additional time or staff, by deploying an AI-driven case finding and pre-abstraction software solution.

Actionable Tip: Review of a limited set of ICD-10 codes is not enough. Routine case finding audits should also be conducted and include:

• Comparing 2-3 months of unfiltered MDI encounters with the cancer cancer registry accession register.
• Track gaps and identify root causes for each gap.
• Incorporate hematology clinic notes, molecular tests, and pharmacy data into the audit.
• Review text notes or reasons for exclusion documented at the time of case finding.
• Present findings to leadership and the Cancer Committee with a data-driven corrective action plan.

Data curation transforms fragmentation into accuracy—and restores visibility to every patient behind the data.

Note: First published on LinkedIn.