背景:结合机器学习衍生内容的临床决策支持(CDS)工具有可能通过增强临床医生的专业知识来改变临床护理。为了实现这种潜力,这些工具必须设计成适合使用它们的临床医生的动态工作系统。我们建议使用学术细节-专家在特定的健康IT工具中对临床医生进行个人访问-作为一种方法,以确保对该工具及其证据基础的正确理解,并确定影响该工具实施的因素。
目的:本研究旨在评估学术细节,作为一种方法,同时确保对基于急诊科的CDS工具的正确理解,以防止未来跌倒,并通过对所得定性数据的分析,确定影响临床医生使用该工具的因素。
方法:以前,我们的团队设计了一个CDS工具来识别65岁及以上未来跌倒风险最高的患者,并向临床医生发出中断警报,建议将患者转诊到活动和跌倒诊所进行基于证据的预防性干预。我们进行了10分钟的学术详细访谈(n=16)与驻地急诊医师和高级实践提供者,他们在实践中遇到了我们的CDS工具。我们进行了归纳,基于团队的内容分析,以确定影响临床医生使用CDS工具的因素。
结果:确定了影响临床医生使用CDS的以下几类因素:(1)CDS工具设计的方面(2)临床医生对CDS或转诊过程的理解(或误解),(3)急诊科环境的繁忙性质,(4)临床医生对患者及其相关跌倒风险的看法,和(5)转诊过程的不透明度。此外,进行了临床医生教育,以解决有关CDS工具或转诊过程的任何误解,例如,证明通过CDS进行转诊是多么简单,并明确转诊到哪个诊所.
结论:我们的研究表明,使用学术细节来支持健康信息技术的实施,使我们能够确定影响临床医生使用CDS的因素,同时对临床医生进行教育,以确保对CDS工具和干预措施的正确理解。因此,学术细节可以为工具实施的实时调整提供信息,例如,用于介绍工具的语言的改进,并对CDS工具进行更大规模的重新设计,以更好地适应临床医生的动态工作环境。
BACKGROUND: Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians\' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool\'s implementation.
OBJECTIVE: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians\' use of the tool through an analysis of the resultant qualitative data.
METHODS: Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians\' use of the CDS tool.
RESULTS: The following categories of factors that impacted clinicians\' use of the CDS were identified: (1) aspects of the CDS tool\'s design (2) clinicians\' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians\' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to.
CONCLUSIONS: Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians\' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool\'s implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians.