Identifying Measures of Suboptimal Healthcare Interaction (SOHI) to Develop a Claims-Based Model for Predicting Patients with Inflammatory Bowel Disease at Risk for SOHI

Abstract

Abstract Background Understanding the demographic and clinical characteristics of patients with Inflammatory Bowel Disease (IBD) who are likely to experience poor disease outcomes may allow early interventions that can improve health outcomes. Objectives To describe demographic and clinical characteristics of patients with ulcerative colitis (UC) and Crohn’s disease (CD) with the presence of at least one Suboptimal Healthcare Interaction (SOHI) event, which can inform the development of a model to predict SOHI in members with IBD based on insurance claims, with the goal of offering these patients some additional intervention. Methods We identified commercially insured individuals with IBD between 01 January 2019 and 31 December 2019 using Optum Labs’ administrative claims database. The primary cohort was stratified on the presence or absence of ≥ 1 SOHI event (a SOHI-defining data point or characteristic at a specific time point) during the baseline observation period. SOHI was deployed as the basis for the development of a model to predict which individuals with IBD were most likely to continue to have SOHI within a 1-year timeframe (follow-up SOHI) using insurance claims data. All baseline characteristics were analyzed descriptively. Multivariable logistic regression was used to examine the association of follow-up SOHI with baseline characteristics. Results Of 19,824 individuals, 6872 (34.7%) were found to have follow-up SOHI. Individuals with follow-up SOHI were more likely to have had similar SOHI events in the baseline period than those with non-SOHI. A significantly greater proportion of individuals with SOHI had ≥ 1 claims-based C-reactive protein (CRP) test order and ≥ 1 CRP lab results compared with non-SOHI. Individuals with follow-up SOHI were more likely to incur higher healthcare expenditures and resource utilization as compared with non-SOHI individuals. A few of the most important variables used to predict follow-up SOHI included baseline mesalamine use, count of baseline opioid fills, count of baseline oral corticosteroid fills, baseline extraintestinal manifestations of disease, proxy for baseline SOHI, and index IBD provider specialty. Conclusion Individuals with SOHI are likely to have higher expenditures, higher healthcare resource utilization, uncontrolled disease, and higher CRP lab results as compared with non-SOHI members. Distinguishing SOHI and non-SOHI patients in a dataset could efficiently identify potential cases of poor future IBD outcomes.