National Clinician Scholars Program, Yale School of Medicine, New Haven, CT, 06511, USA.
VA Connecticut Healthcare System, West Haven, CT, USA.
J Gen Intern Med. 2021 May;36(5):1264-1270. doi: 10.1007/s11606-020-06339-3. Epub 2020 Nov 11.
An important strategy to address the opioid overdose epidemic involves identifying people at elevated risk of overdose, particularly those with opioid use disorder (OUD). However, it is unclear to what degree OUD diagnoses in administrative data are inaccurate.
To estimate the prevalence of inaccurate diagnoses of OUD among patients with incident OUD diagnoses.
A random sample of 90 patients with incident OUD diagnoses associated with an index in-person encounter between October 1, 2016, and June 1, 2018, in three Veterans Health Administration medical centers.
Direct chart review of all encounter notes, referrals, prescriptions, and laboratory values within a 120-day window before and after the index encounter. Using all available chart data, we determined whether the diagnosis of OUD was likely accurate, likely inaccurate, or of indeterminate accuracy. We then performed a bivariate analysis to assess demographic or clinical characteristics associated with likely inaccurate diagnoses.
We identified 1337 veterans with incident OUD diagnoses. In the chart verification subsample, we assessed 26 (29%) OUD diagnoses as likely inaccurate; 20 due to systems error and 6 due to clinical error; additionally, 8 had insufficient information to determine accuracy. Veterans with likely inaccurate diagnoses were more likely to be younger and prescribed opioids for pain. Clinical settings associated with likely inaccurate diagnoses were non-mental health clinical settings, group visits, and non-patient care settings.
Our study identified significant levels of likely inaccurate OUD diagnoses among veterans with incident OUD diagnoses. The majority of these cases reflected readily addressable systems errors. The smaller proportion due to clinical errors and those with insufficient documentation may be addressed by increased training for clinicians. If these inaccuracies are prevalent throughout the VHA, they could complicate health services research and health systems responses.
解决阿片类药物过量流行的一个重要策略是确定处于过量风险升高的人群,尤其是那些有阿片类药物使用障碍(OUD)的人群。然而,行政数据中 OUD 诊断的不准确程度尚不清楚。
评估新诊断 OUD 患者中 OUD 诊断不准确的发生率。
从 2016 年 10 月 1 日至 2018 年 6 月 1 日期间在三个退伍军人健康管理局医疗中心进行的与指数面对面就诊相关的新诊断 OUD 的 90 例患者中随机抽取的一个样本。
直接查阅索引就诊前和后 120 天内所有就诊记录、转介、处方和实验室值的所有记录。利用所有可用的图表数据,我们确定 OUD 诊断是否可能准确、可能不准确或不确定。然后,我们进行了二元分析,以评估与可能不准确的诊断相关的人口统计学或临床特征。
我们确定了 1337 名患有新诊断 OUD 的退伍军人。在图表验证子样本中,我们评估了 26 个(29%)的 OUD 诊断为可能不准确;20 个是由于系统错误,6 个是由于临床错误;此外,8 个的准确性信息不足。有可疑不准确诊断的退伍军人更年轻,且因疼痛而开阿片类药物。可能不准确的诊断相关的临床环境是精神健康非临床环境、小组就诊和非患者护理环境。
我们的研究发现,新诊断 OUD 的退伍军人中存在大量可能不准确的 OUD 诊断。这些病例中大多数反映了容易解决的系统错误。较小比例的临床错误和信息不足的病例可以通过增加对临床医生的培训来解决。如果这些不准确现象在整个退伍军人健康管理局普遍存在,可能会使卫生服务研究和卫生系统应对变得复杂。