Identifying Patients With Inflammatory Bowel Diseases in an Administrative Health Claims Database Do Algorithms Generate Similar Findings?

• Published in: Inquiry (Chicago) Vol. 56; pp. 1 - 8
• Main Authors: Ye, Yizhou, Manne, Sudhakar, Bennett, Dimitri
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA Sage Publications, Inc 2019; SAGE Publications; SAGE PUBLICATIONS, INC; SAGE Publishing
• Subjects: Algorithms; Ambulatory care; Classification; Colitis, Ulcerative - classification; Colitis, Ulcerative - epidemiology; Crohn Disease - classification; Crohn Disease - epidemiology; Crohn's disease; Databases, Factual - statistics & numerical data; Disease; Electronic health records; Female; Health care policy; Health insurance; Health maintenance organizations; HMOs; Humans; Inflammatory bowel disease; Inflammatory Bowel Diseases - classification; Inflammatory Bowel Diseases - epidemiology; Insurance Claim Review - statistics & numerical data; Literature reviews; Male; Medical records; Medical treatment; Observational studies; Original Research; Patients; Personal health; Pharmacy; Population; Validity; United States > US; retrospective studies; administrative claims database; ulcerative colitis; inflammatory bowel diseases; Crohn’s disease; observational study
• ISSN: 0046-9580; 1945-7243; 1945-7243
• DOI: 10.1177/0046958019887816

Application of selective algorithms to administrative health claims databases allows detection of specific patients and disease or treatment outcomes. This study identified and applied different algorithms to a single data set to compare the numbers of patients with different inflammatory bowel disease classifications identified by each algorithm. A literature review was performed to identify algorithms developed to define inflammatory bowel disease patients, including ulcerative colitis, Crohn’s disease, and inflammatory bowel disease unspecified in routinely collected administrative claims databases. Based on the study population, validation methods, and results, selected algorithms were applied to the Optum Clinformatics® Data Mart database from June 2000 to March 2017. The patient cohorts identified by each algorithm were compared. Three different algorithms were identified from literature review and selected for comparison (A, B, and C). Each identified different numbers of patients with any form of inflammatory bowel disease (323 833; 246 953, and 171 537 patients, respectively). The proportions of patients with ulcerative colitis, Crohn’s disease, and inflammatory bowel disease unspecified were 32.0% to 47.5%, 38.6% to 43.8%, and 8.7% to 26.6% of the total population with inflammatory bowel disease, respectively, depending on the algorithm applied. Only 5.1% of patients with inflammatory bowel disease unspecified were identified by all 3 algorithms. Algorithm C identified the smallest cohort for each disease category except inflammatory bowel disease unspecified. This study is the first to compare numbers of inflammatory bowel disease patients identified by different algorithms from a single database. The differences between results highlight the need for validation of algorithms to accurately identify inflammatory bowel disease patients.