Currently, there are few programs freely available to remove identifiers while leaving the remainder of the pathology report text intact.
Cancer registries obtain free text pathology reports from pathology laboratories and currently data entry in most registries relies on the interpretation of the free text in pathology reports by trained coders.
The system takes free-text pathology reports, breaks them into sections, maps free-text data within these sections to NCI Metathesaurus concepts, and stores these concepts and conceptual relationships.
To detect negations of medical entities in free-text pathology reports with different approaches, and evaluate their performances.
We address the task of extracting information from free-text pathology reports, focusing on staging information encoded by the TNM (tumour-node-metastases) and ACPS (Australian clinico-pathological stage) systems.
We have demonstrated that it is possible to create an open-source deidentification program which performs well on free-text pathology reports.
This work demonstrates that an open-source tool can be built which performs well in the domain of free-text pathology reports and emphasizes the challenges inherent in such reports.
However, the free text in pathology reports is primarily used as a tool for communication between clinicians to enable them to make informed management choices about their patients, not as a tool for cancer registration [ 6].
While HIPAA allows ages less than 90 years to be included in de-identified reports, we feel that it is safer to exclude all ages from the text of pathology reports if possible.
In that study, however, histologic diagnoses were abstracted from the text of archival pathology reports and not confirmed by central pathology review.
