Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing
Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing
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DOI：
10.1093/pm/pnw283
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发表时间：
2017-10-01
期刊：
影响因子：
3.1
通讯作者：
Elliott, Thomas E.
中科院分区：
文献类型：
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作者：
Haller, Irina V.;Renier, Colleen M.;Elliott, Thomas E.
使用阿片类药物治疗慢性非癌性疼痛的临床指南建议在开始阿片类药物治疗之前评估异常药物相关行为的风险。尽管最近处方阿片类药物误用和滥用的情况急剧增加，但临床医生对筛查工具的使用仍然没有得到充分利用。这项研究评估了自然语言处理 (NLP) 以及其他数据提取技术，对考虑接受阿片类药物治疗的患者进行风险评估，作为预测阿片类药物滥用的一种手段。通过使用 2007 年 1 月 1 日至 2012 年 12 月 31 日期间至少有一项阿片类药物协议的 3,668 名慢性非癌症疼痛患者的回顾性队列，我们检查了电子健康记录结构化和非结构化数据的可用性，以填充阿片类药物风险工具 (ORT) 和其他选定的结果。使用 NLP 技术确定临床记录中临床医生记录的阿片类药物协议违规情况，然后对记录进行手动审核。通过手动审核确认，NLP 算法在识别阿片类药物协议违规方面具有 96.1% 的敏感性、92.8% 的特异性和 92.6% 的阳性预测值。在最近达成阿片类药物滥用协议时，自动 ORT 确定 42.8% 的患者为阿片类药物滥用低风险，28.2% 为中度风险，29.0% 为高风险。在协议签订后的一年内，22.5% 的患者违反了阿片类药物协议。与低/中风险患者相比，高风险患者违反阿片类药物协议的可能性高出三倍。我们的研究结果表明，NLP 技术具有潜在的实用性，可支持临床医生筛选考虑长期阿片类药物治疗的慢性非癌症疼痛患者。
Clinical guidelines for the use of opioids in chronic noncancer pain recommend assessing risk for aberrant drug-related behaviors prior to initiating opioid therapy. Despite recent dramatic increases in prescription opioid misuse and abuse, use of screening tools by clinicians continues to be underutilized. This research evaluated natural language processing (NLP) together with other data extraction techniques for risk assessment of patients considered for opioid therapy as a means of predicting opioid abuse.Using a retrospective cohort of 3,668 chronic noncancer pain patients with at least one opioid agreement between January 1, 2007, and December 31, 2012, we examined the availability of electronic health record structured and unstructured data to populate the Opioid Risk Tool (ORT) and other selected outcomes. Clinician-documented opioid agreement violations in the clinical notes were determined using NLP techniques followed by manual review of the notes.Confirmed through manual review, the NLP algorithm had 96.1% sensitivity, 92.8% specificity, and 92.6% positive predictive value in identifying opioid agreement violation. At the time of most recent opioid agreement, automated ORT identified 42.8% of patients as at low risk, 28.2% as at moderate risk, and 29.0% as at high risk for opioid abuse. During a year following the agreement, 22.5% of patients had opioid agreement violations. Patients classified as high risk were three times more likely to violate opioid agreements compared with those with low/moderate risk.Our findings suggest that NLP techniques have potential utility to support clinicians in screening chronic noncancer pain patients considered for long-term opioid therapy.