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患者特异性药物-药物相互作用警示对警示频率的影响:一项初步研究。

The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study.

机构信息

University of Washington Seattle, WA, USA.

Alameda Health System, Newark Wellness, CA, USA.

出版信息

Ann Pharmacother. 2019 Nov;53(11):1087-1092. doi: 10.1177/1060028019863419. Epub 2019 Jul 11.

Abstract

False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts. For each of the DDI pairs, 2 algorithms featuring different values for modifying factors were made. DDI alerts from the 7 drug pairs were collected over 30 days. Outcome measures included the number of DDI alerts generated before and after patient-specific algorithm application to the same patients over the same time period. A total of 14 algorithms were generated, and each was evaluated by comparing the number of alerts generated by our existing, customized clinical decision support (CDS) software and the patient-specific algorithms. The CDS DDI alerting software generated an average of 185.3 alerts per drug pair over the 30-day study period. Patient-specific algorithms reduced the number of alerts resulting from the algorithms by 11.3% to 93.5%. Patient-specific DDI alerting is an innovative and effective approach to reduce the number of DDI alerts, may potentially increase the appropriateness of alerts, and may decrease the potential for alert fatigue.

摘要

假阳性药物相互作用警报很常见,导致警报疲劳,从而导致医生忽略重要警报。需要开发一种方法来呈现适合患者的警报,以帮助恢复警报的相关性。本研究旨在评估患者特异性药物相互作用 (DDI) 警报减少警报负担的潜力。该项目在一家三级保健医疗中心进行。选择了最常遇到的七种 DDI 警报来开发基于算法的患者特异性 DDI 警报。对于每个 DDI 对,都制作了两个具有不同修正因素值的算法。在 30 天内收集了来自 7 种药物对的 DDI 警报。结果指标包括在同一时间段内对同一患者应用患者特异性算法前后生成的 DDI 警报数量。总共生成了 14 个算法,并通过比较我们现有的定制临床决策支持 (CDS) 软件和患者特异性算法生成的警报数量来评估每个算法。CDS DDI 警报软件在 30 天的研究期间平均为每对药物生成 185.3 个警报。患者特异性算法将算法生成的警报数量减少了 11.3%,至 93.5%。患者特异性 DDI 警报是一种创新且有效的方法,可以减少 DDI 警报的数量,可能潜在地提高警报的适当性,并可能降低警报疲劳的可能性。

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