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

Smith, Robert C.; Gardiner, Joseph C.; Armatti, Stacey; Johnson, M. S.; Lyles, Judith S.; Given, Charles W.; Lein, Catherine; Given, Barbara; Goddeeris, John H.; Korban, Elie; Haddad, Robert I.; Kanj, Mohammed

doi:10.1097/00005650-200109000-00007

Cited by 17 publications

(18 citation statements)

References 29 publications

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“…Chart review methodology to identify MUS patients is not well defined [8, 9, 10, 11]. No previous studies contained sufficient methodological detail to help us, nor did they report reliability studies [12, 13], but they were encouraging in providing rich clinical data [12, 13, 14, 15, 16].…”

Section: Introductionmentioning

confidence: 99%

A Method for Rating Charts to Identify and Classify Patients with Medically Unexplained Symptoms

Smith

Korban

Kanj

et al. 2003

Psychother Psychosom

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Background: As part of conducting a randomized control trial (RCT) to treat chronically high utilizing patients with medically unexplained symptoms (MUS), we developed the chart rating method reported here to identify and classify MUS subjects. Method: Intended at this point only as a research tool, the method is comprehensive, uses explicit guidelines, and requires clinician raters. It distinguishes primary organic disease patients from those with primary MUS, quantifies medical comorbidities in primary MUS patients, and also distinguishes subgroups among MUS patients that we call somatization (resembles DSM-IV somatoform disorders) and minor acute illness (MAI) which differs from DSM-IV somatoform definitions. Scoring rules are used to generate the diagnoses above. The rules may be set according to the investigator’s needs, from highly sensitive to highly specific. Results: We found high levels of agreement with the gold standard for MUS vs. organic disease (97.6%) and among raters for the key individual chart elements rated (92–96%). The method identified 206 MUS subjects and the extent of their medical comorbidities for entry into a RCT. It also identified somatization and MAI; the latter supports the validity of this newly reported MAI syndrome. Conclusion: We concluded that this method offered research potential for identifying MUS patients, for quantifying their medical comorbidities, and for classifying MUS subgroups.

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Section: Introductionmentioning

confidence: 99%

A Method for Rating Charts to Identify and Classify Patients with Medically Unexplained Symptoms

Smith

Korban

Kanj

et al. 2003

Psychother Psychosom

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“…To improve system-wide diagnosis/identification, our research team [15] and Barsky et al [16] reported that administrative databases (ADB) can provide useful information in identifying somatizing patients. The performance of such ADB screening programs needs replication in different populations before they can be incorporated into system-wide efforts to improve identification (and treatment) of somatization.…”

Section: Introductionmentioning

confidence: 99%

“…From data used to identify subjects for a RCT -- to treat somatization in primary care [8] -- we report in this paper the test of our earlier ADB screener [15]. The initial screening study demonstrated that selected ICD-9 primary diagnosis codes identified somatization: all codes in the musculoskeletal, nervous, gastrointestinal, and ill-defined body systems, which we labeled as having “somatization potential.” Based on results from this study [15], we hypothesized in the study reported here that greater somatization potential would identify somatizing patients in addition to female gender and greater health care utilization.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Accuracy of Predicting Somatization From Patients’ ICD-9 Diagnoses

Smith

Gardiner

Luo³

et al. 2009

Psychosomatic Medicine

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OBJECTIVES-Our earlier study demonstrated that ICD-9 codes and other data from the administrative database (ADB) effectively identified somatization. To further develop this simple screening method, we hypothesized, in a new and different population, that ADB screening would identify somatizing patients by increasing numbers of visits, female gender, and greater percent of ICD-9 primary diagnosis codes in musculoskeletal, nervous, gastrointestinal, and ill-defined body systems; we labeled these codes as having "somatization potential." METHODS-Using a prospective observational design in a staff model HMO, we evaluated 1364 patients from 18-65 years old who had 8 or more visits yearly in the two years before study. Clinician raters applied a reliable method of medical chart review to identify patients meeting criteria for somatization. We randomly selected 2/3 for the derivation set (N=901) for logistic regression to evaluate the contribution of potential ADB correlates (age, gender, all encounters, primary diagnosis codes , revenue codes, and charges) of a diagnosis of somatization. This prediction rule was then applied to the remaining 1/3 of subjects, the validation set (N=463). CONCLUSIONS-These data support our earlier findings that selected ICD-9 diagnoses in the ADB predict somatization, suggesting their potential in identifying a common, costly, and usually unrecognized problem. The demonstrated stability of ICD-9 codes for diagnosing somatization indicates that the next step in research be taken, as we outline here. RESULTS-Patients

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“…32 We defined any primary ICD-9 diagnoses in 4 body systems (gastrointestinal, neurologic, musculoskeletal, ill-defined) as having "somatization potential." During a 1-year period, in addition to female gender and increasing utilization, the more visits with somatization potential, the more likely that patients had somatization; the c-statistic for the scoring rule was 0.78 in a validation set.…”

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confidence: 99%

“…During a 1-year period, in addition to female gender and increasing utilization, the more visits with somatization potential, the more likely that patients had somatization; the c-statistic for the scoring rule was 0.78 in a validation set. 32 Although they hold promise for population-based screening, much more work is needed on ADB screeners before they can be recommended. Here are some considerations for us as we plan research to tap the enormous public health potential of ADB screening.…”

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Administrative Database Screening to Identify Somatizing Patients

Smith¹,

Gardiner²

2006

Medical Care

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P atients with medically unexplained symptoms (MUS), also called somatization, have physical symptoms with little or no disease basis. 1 When disease exists, the symptoms are inconsistent with or out of proportion to the disease. 2 Of importance, people with MUS are not necessarily abnormal. Many exhibit it but seldom or never seek care. 3 MUS becomes a medical problem when it leads to frequent health care-seeking for feared but nonexistent physical disease. 4 In this issue of Medical Care, Barsky and colleagues 5 productively focus on unique health care-utilization patterns as a way to identify the most clinically significant MUS patients.The MUS problem is very common and costly. Defined by The Diagnostic and Statistical Manual of Mental Disorders (DSM) 6 and by its abridged and derivative forms, community surveys show a very high prevalence rate, eg, abridged somatization disorder ranges from 4.4% to 22%. 7-10 The prevalence of all types of MUS in the outpatient setting is reported from 25% to 75%. 11-13 Thus, on average, approximately one-half of all outpatients have little or no physical disease explanation for their symptoms. Barsky et al demonstrated that the greater the somatization and utilization, the greater the cost, 5 which they estimated previously at $256 billion per year. 14 Clinically significant MUS, however, is even more prevalent than estimated by DSM-IV: more than three quarters of 206 MUS patients lacked a full or abridged DSM-IV somatoform diagnosis, although they had high levels of depression/anxiety and averaged 13.6 visits per year. 15 The gold standard definition of MUS came from a medical chart rating procedure conducted by trained, reliable physician raters. 16 Because researchers have relied almost entirely on DSM to identify MUS, these large numbers of "DSMnegative" patients have been completely overlooked by the field. 16 Little is known about their clinical features and prognosis.An even greater problem exists. Primary care providers (PCPs) rarely recognize MUS and, therefore, its frequent comorbid psychiatric disorders also are not diagnosed. Approximately two thirds of psychiatric problems in primary care present exclusively with MUS. 17,18 Bridges and Goldberg, 19 Katon et al,20 Kroenke et al, 21 and Kirmayer et al 22 posit that, by falsely suggesting physical/organic diseases, the somatic focus of MUS misleads physicians and contributes to their very low rates of identification of MUS and its comorbid psychiatric diseases. 13,23 For example, misdiagnosis in outpatients with depression varies from 50% to 75% and is as great as 96% in medical inpatients. 23 We have reviewed that treatment in primary care and specialty settings is effective. 24,25 However, patients exhibiting MUS first must be identified to benefit from treatment. Given the difficulties of PCPs in making the diagnosis, a good case can be made for developing MUS screeners that do not involve PCPs.Several current screeners use questionnaires or interviews of patients, but poor recall of chronic symptoms has led to ...

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

Cited by 17 publications

References 29 publications

A Method for Rating Charts to Identify and Classify Patients with Medically Unexplained Symptoms

A Method for Rating Charts to Identify and Classify Patients with Medically Unexplained Symptoms

Diagnostic Accuracy of Predicting Somatization From Patients’ ICD-9 Diagnoses

Administrative Database Screening to Identify Somatizing Patients

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