Department of Family and Community Medicine, Saint Louis University School of Medicine, 1402 South Grand Blvd, St. Louis, MO, USA.
Department of Psychiatry and Behavioral Neuroscience, School of Medicine, Saint Louis University, St. Louis, MO, USA.
Addict Sci Clin Pract. 2023 Aug 17;18(1):49. doi: 10.1186/s13722-023-00405-x.
A valid opioid use disorder (OUD) identification algorithm for use in administrative medical record data would enhance investigators' ability to study consequences of OUD, OUD treatment seeking and treatment outcomes.
Existing studies indicate ICD-9 and ICD-10 codes for opioid abuse and dependence do not accurately measure OUD. However, critical appraisal of existing literature suggests alternative validation methods would improve the validity of OUD identification algorithms in administrative data. Chart abstraction may not be sufficient to validate OUD, and primary data collection via structured diagnostic interviews might be an ideal gold standard.
Generating valid OUD identification algorithms is critical for OUD research and quality measurement in real world health care settings.
有效的阿片类药物使用障碍(OUD)识别算法可用于管理医疗记录数据,从而提高研究人员研究 OUD 的后果、OUD 治疗寻求和治疗结果的能力。
正 文:现有研究表明,ICD-9 和 ICD-10 阿片类药物滥用和依赖代码不能准确衡量 OUD。然而,对现有文献的批判性评估表明,替代验证方法将提高管理数据中 OUD 识别算法的有效性。图表摘要可能不足以验证 OUD,通过结构化诊断访谈进行原始数据收集可能是理想的金标准。
结 论 和 评 论:生成有效的 OUD 识别算法对于 OUD 研究和真实世界医疗保健环境中的 OUD 质量测量至关重要。