Concurrent Abstracting Produces Less Errors

Manual abstracting does not equal control.
Curating data does.

Some cancer registrars resist AI-automation of abstracting data because they believe manual work equals accuracy and quality. They fear that letting AI assistance will reduce their role or compromise accuracy.

False Perception: “Manual abstracting equals control.”

In fact, the opposite is true. Manual abstraction often chains cancer registrars to repetitive typing and retrospective record-chasing, leaving less time for judgment and interpretation. Concurrent abstracting with automation changes the role: registrars become data curators and quality stewards, validating AI-captured inputs and focusing on clinical nuance.

Process improvement literature backs this shift. Lean Six Sigma interventions in oncology registries show that redesigned workflows with phased data capture reduce error rates and improve data completeness (Fidelbo et al., 2023). Similarly, Kaizen-based continuous improvement models highlight how small, incremental workflow changes—like phased abstraction—enhance efficiency and user adoption (Samara & Harry, 2025).

Truth: AI-assisted abstracting equals quality AND control.

Article was first published on LinkedIn.

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