Development of a 5-Step Electronic Medical Record-Based Algorithm to Identify Patients with Opioid Use Disorder in Pregnancy.

Q4 Medicine Journal of registry management Pub Date : 2024-01-01

Kimberly Fryer, Chinyere N Reid, Chaitanya Chaphalkar, Jennifer Marshall, Laura Szalacha, Kimberly Johnson, Tanner Wright, Caitlin Read, Ayesha Khan, Anna Wilson, Meera Ratani, Kaitlyn Cox, Angela Tavolieri, Melanny Sampayo, Rachel Su, Kelly Campbell, Jason L Salemi

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Abstract

Objectives: 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.

Materials and methods: 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.

Results: 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.

Discussion and conclusion: 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.

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开发基于电子病历的五步算法，以识别妊娠期阿片类药物使用障碍患者。

目的：本研究旨在利用电子病历（EMR）数据，开发并验证一种识别妊娠期患者阿片类药物使用障碍（OUD）的算法：本研究旨在利用电子病历（EMR）数据，开发并验证一种识别孕妇阿片类药物使用障碍（OUD）的算法：材料与方法：研究人员利用一家医疗机构的一组妊娠期患者数据开发并验证了该算法。使用了五种算法组件，并进行了病历审查，以根据既定标准确认 OUD 诊断。对算法各组成部分的阳性预测值（PPV）进行了评估：结果：在该算法确定的 334 份病历中，有 256 个真实病例得到确诊。该算法的总体PPV值为76.6%，其中门诊用药清单的准确率为100%，其他算法组成部分的PPV值从81.3%到93.4%不等：讨论与结论：这项研究强调了采用多方面方法识别妊娠期患者中的 OUD 的重要性，其目的是改善患者护理并对高危患者进行有针对性的干预。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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