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Validation of diagnostic codes within medical services claims

  • Machelle Wilchesky
    Affiliations
    Department of Epidemiology and Biostatistics, McGill University, Morrice House, 1140 Pine Avenue West, Montreal, QCH3A 1A3, Canada
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  • Robyn M Tamblyn
    Correspondence
    Corresponding author. Tel.: 514-843-2831; fax: 514-843-1551.
    Affiliations
    Department of Epidemiology and Biostatistics, McGill University, Morrice House, 1140 Pine Avenue West, Montreal, QCH3A 1A3, Canada

    Department of Medicine, McGill University, McGill University Health Center, 687 Pine Avenue West, Montreal, QCH3A 1A1, Canada
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  • Allen Huang
    Affiliations
    Division of Geriatrics, Department of Medicine, McGill University, McGill University Health Center, 687 Pine Avenue West, Montreal, QCH3A 1A3, Canada
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      Abstract

      Objectives

      Few studies have attempted to validate the diagnostic information contained within medical service claims data, and only a small proportion of these have attempted to do so using the medical chart as a gold standard. The goal of this study is to determine the sensitivity and specificity of medical services claims diagnoses for surveillance of 14 drug disease contraindications used in drug utilization review, the Charlson comorbidity index and the Johns Hopkins Adjusted Care Group Case-Mix profile (ADGs).

      Study design and setting

      Diagnoses were abstracted from the medical charts of 14,980 patients, and were used as the “gold standard,” against which diagnoses obtained from the administrative database for the same patients were compared.

      Results

      Conditions associated with drug disease contraindications with the exception of hypertension and chronic obstructive pulmonary disease (COPD) showed a specificity of 90% or higher. Sensitivity of claims data was substantially lower, with glaucoma, hypertension, and diabetes being the most sensitive conditions at 76, 69, and 64%, respectively. Each of the 18 disease conditions contained in the Charlson comorbidity index showed high specificity, but sensitivity was more variable among conditions as well as by coding definitions. Although ADG specificity was also high, the vast majority of ADGs had sensitivities of less than 60%.

      Conclusion

      The administrative data was found to have diagnoses and conditions that were highly specific but that vary greatly by condition in terms of sensitivity. To appropriately obtain diagnostic profiles, it is recommended that data pertaining to all physician billings be used.

      Keywords

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