Using electronic health records data to identify patients with chronic pain in a primary care setting

• Published in: Journal of the American Medical Informatics Association : JAMIA 2013-12, Vol.20 (e2), p.e275-e280
• Main Authors: Tian, Terrence Y, Zlateva, Ianita, Anderson, Daren R
• Format: Article
• Language: English
• Subjects: Algorithms; Chronic Pain - diagnosis; Community Health Centers; Connecticut; Data Mining; Electronic Health Records; Humans; Primary Health Care; Quality Improvement; Research and Applications
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1136/amiajnl-2013-001856

To develop and validate an accurate method to identify patients with chronic pain using electronic health records (EHR) data at a multisite community health center. We identified patients with chronic pain in our EHR system using readily available data elements pertaining to pain: diagnostic codes (International Classification of Disease, revision 9; ICD-9), patient-reported pain scores, and opioid prescription medications. Medical chart reviews were used to evaluate the accuracy of these data elements in all of their combinations. We developed an algorithm to identify chronic pain patients more accurately based on these evaluations. The algorithm's results were validated for accuracy by comparing them with the documentation of chronic pain by the patient's treating clinician in 381 random patient charts. The new algorithm, which combines pain scores, prescription medications, and ICD-9 codes, has a sensitivity and specificity of 84.8% and 97.7%, respectively. The algorithm was more accurate (95.0%) than pain scores (88.7%) or ICD-9 codes (93.2%) alone. The receiver operating characteristic was 0.981. A straightforward method for identifying chronic pain patients solely using structured electronic data does not exist because individual data elements, such as pain scores or ICD-9 codes, are not sufficiently accurate. We developed and validated an algorithm that uses a combination of elements to identify chronic pain patients accurately. We derived a useful method that combines readily available elements from an EHR to identify chronic pain with high accuracy. This method should prove useful to those interested in identifying chronic pain patients in large datasets for research, evaluation or quality improvement purposes.