Screening for High Utilizing Somatizing Patients Using a Prediction Rule Derived From the Management Information System of an HMO: A Preliminary Study

Abstract

Background Somatization is a common, costly problem with great morbidity, but there has been no effective screening method to identify these patients and target them for treatment. Objectives We tested a hypothesis that we could identify high utilizing somatizing patients from a management information system (MIS) by total number of visits and what we termed "somatization potential," the percentage of visits for which ICD-9 primary diagnosis codes represented disorders in the musculoskeletal, nervous, or gastrointestinal systems or ill-defined complaints. Methods We identified 883 high users from the MIS of a large staff model HMO as those having six or more visits during the year studied (65th percentile). A physician rater, without knowledge of hypotheses and predictors, then reviewed the medical records of these patients and identified somatizing patients (n = 122) and nonsomatizing patients (n = 761). In two-thirds of the population (the derivation set), we used logistic regression to refine our hypothesis and identify predictors of somatization available from the MIS: demographic data, all medical encounters, and primary diagnoses made by usual care physicians (ICD-9 codes). We then tested our prediction model in the remaining one-third of the population (the validation set) to validate its usefulness. Results The derivation set contained the following significant correlates of somatization: gender, total number of visits, and percent of visits with somatization potentia…