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 potential. The c-statistic, equivalent to the area under the ROC curve, was 0.90. In the validation set, the explanatory power was less with a still impressive c-statistic of 0.78. A predicted probability of 0.04 identified almost all somatizers, whereas a predicted probability of 0.40 identified about half of all somatizers but produced few false positives. CONCLUSIONS: We have developed and validated a prediction model from the MIS that helps to distinguish chronic somatizing patients from other high utilizing patients. Our method requires corroboration but carries the promise of providing clinicians and health plan directors with an inexpensive, simple approach for identifying the common somatizing patient and, in turn, targeting them for treatment. The screener does not require clinicians' time.
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 potential. The c-statistic, equivalent to the area under the ROC curve, was 0.90. In the validation set, the explanatory power was less with a still impressive c-statistic of 0.78. A predicted probability of 0.04 identified almost all somatizers, whereas a predicted probability of 0.40 identified about half of all somatizers but produced few false positives. CONCLUSIONS: We have developed and validated a prediction model from the MIS that helps to distinguish chronic somatizing patients from other high utilizing patients. Our method requires corroboration but carries the promise of providing clinicians and health plan directors with an inexpensive, simple approach for identifying the common somatizing patient and, in turn, targeting them for treatment. The screener does not require clinicians' time.
Authors: Robert C Smith; Elie Korban; Mohammed Kanj; Robert Haddad; Judith S Lyles; Catherine Lein; Joseph C Gardiner; Annemarie Hodges; Francesca C Dwamena; John Coffey; Clare Collins Journal: Psychother Psychosom Date: 2004 Jan-Feb Impact factor: 17.659
Authors: Robert C Smith; Joseph C Gardiner; Zhehui Luo; Susan Schooley; Lois Lamerato; Kathryn Rost Journal: J Gen Intern Med Date: 2009-04-30 Impact factor: 5.128