title: Comparing methods for identifying pancreatic cancer patients using electronic data sources.
abstract: We sought to determine the accuracy of two electronic methods of identifying pancreatic cancer in a cohort of pancreatic cyst patients, and to examine the reasons for identification failure. We used the International Classification of Diseases, 9(th) Edition (ICD-9) codes and natural language processing (NLP) technology to identify pancreatic cancer in these patients. We compared both methods to a human-validated gold-standard surgical database. Both ICD-9 codes and NLP technology achieved high sensitivity for identifying pancreatic cancer, but the ICD-9 code method achieved markedly lower specificity and PPV compared to the NLP method. The NLP method required only slightly greater expenditures of time and effort compared to the ICD-9 code method. We identified several variables influencing the accuracy of ICD-9 codes to identify cancer patients including: the identification algorithm, kind of cancer to be identified, presence of other conditions similar to cancer, and presence of conditions that are precancerous.
authors.affiliation: Regenstrief Institute, Inc.
authors.forename: Jeff
authors.initials: J
authors.lastname: Friedlin
authors.forename: Marc
authors.initials: M
authors.lastname: Overhage
authors.forename: Mohammed A
authors.initials: MA
authors.lastname: Al-Haddad
authors.forename: Joshua A
authors.initials: JA
authors.lastname: Waters
authors.forename: J Juan R
authors.initials: JJ
authors.lastname: Aguilar-Saavedra
authors.forename: Joe
authors.initials: J
authors.lastname: Kesterson
authors.forename: Max
authors.initials: M
authors.lastname: Schmidt
mesh_terms: D000465:Algorithms; D059019:Clinical Coding; D006801:Humans; D038801:International Classification of Diseases; D009323:Natural Language Processing; D010190:Pancreatic Neoplasms; D012680:Sensitivity and Specificity