D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center (UTHealth), Houston, Texas, United States.
Division of Population Health and Evidence-Based Practice, Healthcare Transformation Initiatives, The University of Texas Health Science Center at Houston (UTHealth) John P. and Kathrine G. McGovern Medical School, Houston, Texas, United States.
Appl Clin Inform. 2023 Oct;14(5):923-931. doi: 10.1055/a-2178-0197. Epub 2023 Sep 19.
Medication discrepancies between clinical systems may pose a patient safety hazard. In this paper, we identify challenges and quantify medication discrepancies across transitions of care.
We used structured clinical data and free-text hospital discharge summaries to compare active medications' lists at four time points: preadmission (outpatient), at-admission (inpatient), at-discharge (inpatient), and postdischarge (outpatient). Medication lists were normalized to RxNorm. RxNorm identifiers were further processed using the RxNav API to identify the ingredient. The specific drugs and ingredients from inpatient and outpatient medication lists were compared.
Using RxNorm drugs, the median percentage intersection when comparing active medication lists within the same electronic health record system ranged between 94.1 and 100% indicating substantial overlap. Similarly, when using RxNorm ingredients the median percentage intersection was 94.1 to 100%. In contrast, the median percentage intersection when comparing active medication lists across EHR systems was significantly lower (RxNorm drugs: 6.1-7.1%; RxNorm ingredients: 29.4-35.0%) indicating that the active medication lists were significantly less similar ( < 0.05).Medication lists in the same EHR system are more similar to each other (fewer discrepancies) than medication lists in different EHR systems when comparing specific RxNorm drug and the more general RxNorm ingredients at transitions of care. Transitions of care that require interoperability between two EHR systems are associated with more discrepancies than transitions where medication changes are expected (e.g., at-admission vs. at-discharge). Challenges included lack of access to structured, standardized medication data across systems, and difficulty distinguishing medications from orderable supplies such as lancets and diabetic test strips.
Despite the challenges to medication normalization, there are opportunities to identify and assist with medication reconciliation across transitions of care between institutions.
临床系统之间的用药差异可能对患者安全构成威胁。本文旨在确定在不同医疗场景下的用药差异，并对其进行量化。
我们使用结构化的临床数据和医院出院小结中的自由文本，比较了四个时间点的患者用药清单：入院前（门诊）、入院时（住院）、出院时（住院）和出院后（门诊）。用药清单被归一化为 RxNorm 标准。进一步使用 RxNav API 处理 RxNorm 标识符，以确定药物成分。比较了住院和门诊用药清单中的特定药物和成分。
使用 RxNorm 药物时，在同一电子病历系统内比较用药清单的交集中位数在 94.1%到 100%之间，表明存在大量重叠。同样，当使用 RxNorm 成分时，交集中位数为 94.1%到 100%。相比之下，当比较不同电子病历系统中的用药清单时，交集中位数明显较低（RxNorm 药物：6.1%至 7.1%；RxNorm 成分：29.4%至 35.0%），这表明用药清单的相似度明显较低（ < 0.05）。在同一电子病历系统中，用药清单之间的相似度更高（差异更小），而在不同电子病历系统中，用药清单之间的相似度较低，特别是在需要两个电子病历系统之间互操作的医疗场景中，差异更为明显（如入院时与出院时）。挑战包括缺乏跨系统获取结构化、标准化用药数据的途径，以及难以区分药物和可订购的供应品（如采血针和糖尿病测试条）。
尽管在药物标准化方面存在挑战，但仍有机会在机构间的不同医疗场景下识别和协助用药调整。
