Canan Chelsea, Polinski Jennifer M, Alexander G Caleb, Kowal Mary K, Brennan Troyen A, Shrank William H
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
CVS Health, Woonsocket, RI, USA.
J Am Med Inform Assoc. 2017 Nov 1;24(6):1204-1210. doi: 10.1093/jamia/ocx066.
Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use.
We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis.
Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis.
Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings.
Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility.
改进识别非医疗性阿片类药物使用的方法有助于将医疗保健资源导向有需求的个体。利用电子医疗保健索赔或记录的大型数据库进行监测的自动化算法是实现这一目标的潜在手段。在这项系统评价中，我们回顾了此类算法在检测非医疗性阿片类药物使用方面的效用、验证尝试及应用情况。
我们在PubMed和Embase中检索了描述可自动化算法的文章，这些算法利用电子医疗保健索赔或记录来识别可能存在非医疗性阿片类药物使用情况的患者或开处方者。我们评估了算法的开发、验证和性能特征以及它们的应用场景。研究的变异性使得无法进行荟萃分析。
在纳入的15种算法中，10种针对患者，2种针对开处方者，2种同时针对两者，1种识别具有高滥用潜力的药物。大多数以患者为中心的算法（67%）使用处方药索赔和/或医疗索赔，将药物滥用和/或依赖的诊断代码作为参考标准。11种算法通过回归建模开发。4种使用自然语言处理、数据挖掘、审计分析或因子分析。
自动化算法可促进人群层面的监测。然而，确定非医疗性阿片类药物使用并没有真正的金标准。使用者必须认识到识别假阳性和相反的假阴性的影响。很少有算法在实际环境中得到应用。
自动化算法可能有助于识别最有可能需要针对非医疗性阿片类药物使用进行更深入筛查和/或干预的患者和/或开处方者。在实际环境中进行更多的实施研究将阐明它们的效用。
