Keep Registry Data Clean in Real-Time

The longer you wait, the more you miss.
Concurrent abstracting keeps data clean.

Many cancer registrars argue that waiting until treatment ends ensures completeness and accuracy. But when months pass, records are harder to locate, the volume of data to review is greater, more details are missed or entered incorrectly, and errors go unnoticed.

False perception: “Abstracting 4+ months after diagnosis is more accurate and complete.”

Concurrent abstraction, supported by intelligent systems, improves accuracy because data is captured when source records are fresh and discrepancies are easiest to resolve. The UCSF Cancer Registrar Workload and Staffing Study found that registries using concurrent methods closed cases in 1–4 months of first contact, with fewer missed data points, compared to 6+ months for retrospective models (Hailer et al., Nat’l Cancer Registrars Assoc, 2024).

Automation strengthens this further. Computer-assisted coding studies show AI can auto-populate up to 43% of fields accurately, reducing human error and freeing registrars to focus and visually review more complex data items (de Las Pozas et al, J Registry Mgmt, 2023). Real-time quality edits surface issues immediately, rather than months later when correcting them is more costly, time-consuming, or at risk of going unnoticed.

Truth: Phased, or concurrent abstracting with AI automation ensures data is not only timely, but more accurate—because errors are addressed as they arise.

This article was originally published on LinkedIn.

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