University of South Florida, Tampa, Florida.
J Registry Manag. 2024 Summer;51(2):69-74.
This study aimed to develop and validate an algorithm for the identification of opioid use disorder (OUD) in pregnant patients using electronic medical record (EMR) data.
A cohort of pregnant patients from a single institution was used to develop and validate the algorithm. Five algorithm components were used, and chart reviews were conducted to confirm OUD diagnoses based on established criteria. Positive predictive values (PPV) of each of the algorithm's components were assessed.
Of the 334 charts identified by the algorithm, 256 true cases were confirmed. The overall PPV of the algorithm was 76.6%, with 100% accuracy for outpatient medication lists, and high PPVs ranging from 81.3% to 93.4% across other algorithm components.
The study highlights the significance of a multifaceted approach in identifying OUD among pregnant patients, aiming to improve patient care and target interventions for patients at risk.
本研究旨在利用电子病历（EMR）数据开发和验证一种用于识别孕妇阿片类药物使用障碍（OUD）的算法。
本研究使用来自单一机构的孕妇队列来开发和验证该算法。该算法使用了五个算法组件，并进行了病历审查，以根据既定标准确认 OUD 诊断。评估了算法各个组件的阳性预测值（PPV）。
该算法确定了 334 份病历，其中 256 份被确认为真实病例。该算法的总体 PPV 为 76.6%，门诊药物清单的准确率为 100%，其他算法组件的 PPV 也较高，范围从 81.3%到 93.4%。
本研究强调了在识别孕妇 OUD 时采用多方面方法的重要性，旨在改善患者护理，并针对有风险的患者进行干预。
