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Modified versions of these algorithms (i.e., adaptation of the number of healthcare encounters) were also tested. For each algorithm, sensitivity, specificity, positive\/negative predictive values, and their respective 95% confidence intervals (95%CI) were calculated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the RAMQ database, some previously published algorithms and modified versions of these algorithms were found to be valid for identifying patients suffering from chronic lumbar pain (sensitivity: 0.65, 95%CI: 0.59\u20130.71; specificity: 0.83, 95%CI: 0.79\u20130.87), chronic back pain (sensitivity: 0.70, 95%CI: 0.64\u20130.76; specificity: 0.73, 95%CI: 0.68\u20130.78), and chronic neck\/back pain (sensitivity: 0.71, 95%CI: 0.65\u20130.76; specificity: 0.78, 95%CI: 0.72\u20130.82). Algorithms to identify patients with other types of chronic pain showed low sensitivity: complex regional pain syndrome (\u22640.07), fibromyalgia (\u22640.42), and neuropathic pain (\u22640.39).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study provides evidence supporting the value of the RAMQ administrative database for conducting research on certain types of chronic pain disorders including back and neck pain. Users should, however, be cautious about the limitations of this database for studying other types of chronic pain syndromes such as complex regional pain syndrome, fibromyalgia, and neuropathic pain. 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