Medical Informatics & Clinical Epidemiology.
School of Medicine.
AMIA Annu Symp Proc. 2022 Feb 21;2021:773-782. eCollection 2021.
Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical notes and record medication information in them. The medication information in notes may be used for medication reconciliation to improve the medication lists' accuracy. However, accurately extracting patient's current medications from free-text narratives is challenging. In this study, we first explored the discrepancies between medication documentation in medication lists and progress notes for glaucoma patients by manually reviewing patients' charts. Next, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Lastly, a prototype tool for medication reconciliation using the developed model was demonstrated. In the future, the model has the potential to be incorporated into the EHR system to help with realtime medication reconciliation.
电子健康记录（EHR）中的药物数据的准确性对于患者护理和研究至关重要，但许多研究表明，药物清单经常包含错误。相比之下，医生通常更关注临床记录并在其中记录药物信息。记录中的药物信息可用于药物重整以提高药物清单的准确性。然而，从自由文本叙述中准确提取患者当前的药物是具有挑战性的。在这项研究中，我们首先通过手动审查患者图表来探索青光眼患者的药物清单和进展记录中的药物记录差异。接下来，我们开发并验证了一个命名实体识别模型，以从进展记录中识别当前药物和用药依从性。最后，展示了一个使用所开发模型进行药物重整的原型工具。将来，该模型有可能被纳入 EHR 系统，以帮助进行实时药物重整。
