Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.
Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.
Clin Transl Sci. 2024 Oct;17(10):e70044. doi: 10.1111/cts.70044.
Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient-specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health-system-wide, mixed-methods study. Clinicians representing multiple practices and care settings (N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient-specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0-100 points) of each alert, the perceived impact of each alert on stress and decision-making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision-making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician-guided design of PGx alerts in the era of digital medicine.
基于机器学习的药物基因组学（PGx）生物标志物可以整合到电子健康记录（EHR）中，为临床医生提供药物治疗效果的个体化预测。然而，目前 EHR 中的药物警报在很大程度上是通用的（不是针对患者的），这导致了临床医生压力和倦怠的增加。提高 PGx 警报的可用性是当务之急。因此，这项工作旨在通过一项全系统的混合方法研究，确定最佳 PGx 警报设计原则。邀请来自城市、农村和服务不足地区的多个实践和护理环境的临床医生（N=1062）完成一项电子调查，比较三种西酞普兰药物警报的可用性，作为一个案例研究。警报 1 包含了一个关于西酞普兰代谢的药物基因组学影响的通用警告。警报 2 和 3 提供了西酞普兰疗效的患者特定预测，信息深度不同。主要结果包括每个警报的系统可用性量表评分（0-100 分）、每个警报对压力和决策的感知影响以及临床医生对警报改进的建议。次要结果包括按临床医生年龄、实践类型和地理环境评估警报偏好。定性信息的捕获为定量信息提供了背景。最终队列包括 305 名具有地理和临床多样性的临床医生。无论年龄、实践类型或地理环境如何，与更详细的版本（警报 3）和通用警报（警报 1）相比，简化的个体化警报（警报 2）被认为对决策和压力更有帮助。研究结果强调了在数字医学时代，需要临床医生指导设计 PGx 警报。
