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用于识别引发药师干预的住院时间的评分评估:整合到临床决策支持系统中。

Evaluation of a score for identifying hospital stays that trigger a pharmacist intervention: integration into a clinical decision support system.

作者信息

Robert Laurine, Vidoni Nathalie, Gérard Erwin, Chazard Emmanuel, Odou Pascal, Rousselière Chloé, Décaudin Bertrand

机构信息

CHU Lille, Institut de Pharmacie, Lille F-59000, France.

Pharmacy Department, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium.

出版信息

JAMIA Open. 2025 May 5;8(3):ooaf030. doi: 10.1093/jamiaopen/ooaf030. eCollection 2025 Jun.

Abstract

OBJECTIVES

The objective of the study was to determine, after medication review, the patient risk score threshold that would distinguish between stays with prescriptions triggering pharmacist intervention (PI) and stays with prescriptions not triggering PI.

MATERIALS AND METHODS

The study was retrospective and observational, conducted in the clinical pharmacy team. The patient risk score was adapted from a Canadian score and was integrated in the clinical decision support system (CDSS). For each hospital stay, the score was calculated at the beginning of hospitalization and we retrospectively showed if a medication review and a PI were conducted. Then, the optimal patient risk score threshold was determined to help pharmacist in optimizing medication review.

RESULTS

During the study, 973 (56.7%) medication reviews were performed and 248 (25.5%) led to a PI. After analyzing sensitivity, specificity, and positive predictive value of different thresholds, the threshold of 4 was deemed discriminating to identify hospital stays likely to lead to a PI following a medication review. At this threshold, 600 hospital stays would have been detected (33.3% of the latter led to a PI), and 5.0% of stays with a medication review would not have been detected even though they were hospital stays that had triggered a PI.

DISCUSSION AND CONCLUSION

Integration of a patient risk score in a CDSS can help clinical pharmacist to target hospital stays likely to trigger a PI. However, an optimal threshold is difficult to determine. Constructing and using a score in practice should be organized with the local clinical pharmacy team, in order to understand the tool's limitations and maximize its use in detecting at-risk drug prescriptions.

摘要

目的

本研究的目的是在进行用药审查后,确定能够区分引发药师干预(PI)的住院处方和未引发PI的住院处方的患者风险评分阈值。

材料与方法

本研究为回顾性观察研究,在临床药学团队中开展。患者风险评分改编自加拿大的一个评分,并整合到临床决策支持系统(CDSS)中。对于每次住院,在住院开始时计算评分,并回顾性显示是否进行了用药审查和PI。然后,确定最佳的患者风险评分阈值,以帮助药师优化用药审查。

结果

在研究期间,进行了973次(56.7%)用药审查,其中248次(25.5%)导致了PI。在分析不同阈值的敏感性、特异性和阳性预测值后,4分的阈值被认为具有区分性,可识别出用药审查后可能导致PI的住院情况。在此阈值下,将检测到600次住院(其中33.3%导致了PI),并且5.0%进行了用药审查的住院情况即使引发了PI也不会被检测到。

讨论与结论

将患者风险评分整合到CDSS中可帮助临床药师确定可能引发PI的住院情况。然而,最佳阈值难以确定。在实践中构建和使用评分应与当地临床药学团队共同组织,以便了解该工具的局限性,并最大限度地利用其来检测有风险的药物处方。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69b6/12051848/5ffe0d57c563/ooaf030f1.jpg

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