PDF(Pubmed)
Abstract：
Despite the impact of physical abuse on children, it is often underdiagnosed, especially among children evaluated in emergency departments (EDs). Electronic clinical decision support (CDS) can improve the recognition of child physical abuse.
We aimed to develop and test the usability of a natural language processing-based child abuse CDS system, known as the Child Abuse Clinical Decision Support (CA-CDS), to alert ED clinicians about high-risk injuries suggestive of abuse in infants\' charts.
Informed by available evidence, a multidisciplinary team, including an expert in user design, developed the CA-CDS prototype that provided evidence-based recommendations for the evaluation and management of suspected child abuse when triggered by documentation of a high-risk injury. Content was customized for medical versus nursing providers and initial versus subsequent exposure to the alert. To assess the usability of and refine the CA-CDS, we interviewed 24 clinicians from 4 EDs about their interactions with the prototype. Interview transcripts were coded and analyzed using conventional content analysis.
Overall, 5 main categories of themes emerged from the study. CA-CDS benefits included providing an extra layer of protection, providing evidence-based recommendations, and alerting the entire clinical ED team. The user-centered, workflow-compatible design included soft-stop alert configuration, editable and automatic documentation, and attention-grabbing formatting. Recommendations for improvement included consolidating content, clearer design elements, and adding a hyperlink with additional resources. Barriers to future implementation included alert fatigue, hesitancy to change, and concerns regarding documentation. Facilitators of future implementation included stakeholder buy-in, provider education, and sharing the test characteristics. On the basis of user feedback, iterative modifications were made to the prototype.
With its user-centered design and evidence-based content, the CA-CDS can aid providers in the real-time recognition and evaluation of infant physical abuse and has the potential to reduce the number of missed cases.
We aimed to develop and test the usability of a natural language processing-based child abuse CDS system, known as the Child Abuse Clinical Decision Support (CA-CDS), to alert ED clinicians about high-risk injuries suggestive of abuse in infants\' charts.
Informed by available evidence, a multidisciplinary team, including an expert in user design, developed the CA-CDS prototype that provided evidence-based recommendations for the evaluation and management of suspected child abuse when triggered by documentation of a high-risk injury. Content was customized for medical versus nursing providers and initial versus subsequent exposure to the alert. To assess the usability of and refine the CA-CDS, we interviewed 24 clinicians from 4 EDs about their interactions with the prototype. Interview transcripts were coded and analyzed using conventional content analysis.
Overall, 5 main categories of themes emerged from the study. CA-CDS benefits included providing an extra layer of protection, providing evidence-based recommendations, and alerting the entire clinical ED team. The user-centered, workflow-compatible design included soft-stop alert configuration, editable and automatic documentation, and attention-grabbing formatting. Recommendations for improvement included consolidating content, clearer design elements, and adding a hyperlink with additional resources. Barriers to future implementation included alert fatigue, hesitancy to change, and concerns regarding documentation. Facilitators of future implementation included stakeholder buy-in, provider education, and sharing the test characteristics. On the basis of user feedback, iterative modifications were made to the prototype.
With its user-centered design and evidence-based content, the CA-CDS can aid providers in the real-time recognition and evaluation of infant physical abuse and has the potential to reduce the number of missed cases.
摘要：
背景：尽管身体虐待对儿童有影响，它经常被诊断不足，特别是在普通急诊科（ED）评估的儿童以及属于种族或少数族裔群体的儿童中。电子临床决策支持（CDS）可以提高对儿童身体虐待的认识。
目的：我们旨在开发和测试基于自然语言处理的虐待儿童CDS系统的可用性，被称为虐待儿童临床决策支持(CA-CDS)，提醒ED临床医生注意暗示婴儿虐待的高危伤害。
方法：根据现有证据，多学科小组,包括用户设计专家，开发了CA-CDS原型，该原型提供了基于证据的建议，用于评估和管理由高风险伤害记录引发的可疑虐待儿童行为。内容是针对医疗与护理提供者以及最初与随后的警报暴露定制的。要评估CA-CDS的可用性并完善CA-CDS，我们采访了来自4个ED的24名临床医生，了解他们与原型的相互作用。使用常规内容分析对访谈记录进行编码和分析。
结果：总体而言，研究中出现了5个主要类别的主题。CA-CDS的好处包括提供额外的保护层，提供基于证据的建议，并提醒整个临床ED团队。以用户为中心,与工作流兼容的设计包括软停止警报配置，可编辑和自动文档，和引人注意的格式。改进建议包括整合内容、更清晰的设计元素，并添加具有额外资源的超链接。未来实施的障碍包括警觉疲劳，犹豫不决地改变，以及对文档的关注。未来实施的促进者包括利益相关者的支持，提供者教育，并分享测试特征。根据用户反馈，对原型进行了迭代修改。
结论：以用户为中心的设计和基于证据的内容，CA-CDS可以帮助提供者实时识别和评估婴儿身体虐待，并有可能减少遗漏病例的数量。
目的：我们旨在开发和测试基于自然语言处理的虐待儿童CDS系统的可用性，被称为虐待儿童临床决策支持(CA-CDS)，提醒ED临床医生注意暗示婴儿虐待的高危伤害。
方法：根据现有证据，多学科小组,包括用户设计专家，开发了CA-CDS原型，该原型提供了基于证据的建议，用于评估和管理由高风险伤害记录引发的可疑虐待儿童行为。内容是针对医疗与护理提供者以及最初与随后的警报暴露定制的。要评估CA-CDS的可用性并完善CA-CDS，我们采访了来自4个ED的24名临床医生，了解他们与原型的相互作用。使用常规内容分析对访谈记录进行编码和分析。
结果：总体而言，研究中出现了5个主要类别的主题。CA-CDS的好处包括提供额外的保护层，提供基于证据的建议，并提醒整个临床ED团队。以用户为中心,与工作流兼容的设计包括软停止警报配置，可编辑和自动文档，和引人注意的格式。改进建议包括整合内容、更清晰的设计元素，并添加具有额外资源的超链接。未来实施的障碍包括警觉疲劳，犹豫不决地改变，以及对文档的关注。未来实施的促进者包括利益相关者的支持，提供者教育，并分享测试特征。根据用户反馈，对原型进行了迭代修改。
结论：以用户为中心的设计和基于证据的内容，CA-CDS可以帮助提供者实时识别和评估婴儿身体虐待，并有可能减少遗漏病例的数量。
