title: SNOMED-encoded surgical pathology databases: a tool for epidemiologic investigation.
abstract: Pathology departments have invested considerable energy, sometimes extending over several decades, toward coding their anatomic pathology reports. As a result of these labors, there is now a vast amount of electronically coded data from surgical pathology reports, holding a wealth of information relevant to virtually every recognized pathologic entity. The original intent of the Systematized Nomenclature of Medicine (SNOMED) was to prepare population-based disease data from pathology reports, but no such studies have emerged in the medical literature. This is due in part to the nonuniform, idiosyncratic, and incomplete manner in which most SNOMED databases are constructed. Automatic (computer-driven) coding provides uniformity and completeness of SNOMED databases and offers the possibility of customized recoding for an entire collection of reports using any nomenclature and any set of coding algorithms. In prior investigations, we described a computer program that SNOMED-codes surgical pathology reports, and we provided an analysis of a large surgical pathology SNOMED database. In this report, we describe the importance of coded surgical pathology databases for research, teaching, hospital administration, and public health, and we explain the functional differences between coded databases and free-text collections of surgical pathology data. Surgical pathology departments and vendors of laboratory information systems can ensure that surgical report files can be automatically coded or recoded with any chosen nomenclature by adhering to simple guidelines.
authors.affiliation: Department of Pathology, Baltimore Veterans' Administration Medical Center, MD 21201-1524, USA. berman.jules@baltimore.va.gov
authors.forename: J J
authors.initials: JJ
authors.lastname: Berman
authors.forename: G W
authors.initials: GW
authors.lastname: Moore
mesh_terms: D004194:Disease / Q000145:classification*; D006801:Humans; D007256:Information Systems; D009323:Natural Language Processing; D010340:Pathology, Surgical