Weitzman Quality Institute, Community Health Center, Inc., Middletown, Connecticut, USA.
J Am Med Inform Assoc. 2013 Dec;20(e2):e275-80. doi: 10.1136/amiajnl-2013-001856. Epub 2013 Jul 31.
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.
开发并验证一种准确的方法，以利用多地点社区健康中心的电子健康记录 (EHR) 数据识别慢性疼痛患者。
我们使用与疼痛相关的现成数据元素（诊断代码[国际疾病分类，第 9 修订版；ICD-9]、患者报告的疼痛评分和阿片类药物处方）在我们的 EHR 系统中识别慢性疼痛患者。使用病历回顾来评估这些数据元素在其所有组合中的准确性。我们根据这些评估开发了一种更准确识别慢性疼痛患者的算法。通过将其与 381 份随机患者病历中患者治疗医生记录的慢性疼痛文档进行比较，验证该算法的准确性。
新算法结合了疼痛评分、处方药物和 ICD-9 代码，其灵敏度和特异性分别为 84.8%和 97.7%。该算法比疼痛评分（88.7%）或 ICD-9 代码（93.2%）更准确（95.0%）。受试者工作特征曲线为 0.981。
仅使用结构化电子数据识别慢性疼痛患者的简单方法并不存在，因为单独的数据元素（如疼痛评分或 ICD-9 代码）不够准确。我们开发并验证了一种使用组合元素准确识别慢性疼痛患者的算法。
我们得出了一种有用的方法，该方法结合了 EHR 中易于获取的元素，以高精度识别慢性疼痛。对于那些有兴趣在大型数据集（用于研究、评估或质量改进目的）中识别慢性疼痛患者的人来说，这种方法应该很有用。
