The Effects of Integrated IT Support on the Prehospital Stroke Process: Results from a Realistic Experiment.

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2019-05-23 eCollection Date: 2019-09-01 DOI:10.1007/s41666-019-00053-4
Magnus Andersson Hagiwara, Lars Lundberg, Bengt Arne Sjöqvist, Hanna Maurin Söderholm
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Abstract

Stroke is a serious condition and the stroke chain of care is a complex. The present study aims to explore the impact of a computerised decision support system (CDSS) for the prehospital stroke process, with focus on work processes and performance. The study used an exploratory approach with a randomised controlled crossover design in a realistic contextualised simulation experiment. The study compared clinical performance among 11 emergency medical services (EMS) teams of 22 EMS clinicians using (1) a computerised decision support system (CDSS) and (2) their usual paper-based process support. Data collection consisted of video recordings, postquestionnaires and post-interviews, and data were analysed using a combination of qualitative and quantitative approaches. In this experiment, using a CDSS improved patient assessment, decision making and compliance to process recommendations. Minimal impact of the CDSS was found on EMS clinicians' self-efficacy, suggesting that even though the system was found to be cumbersome to use it did not have any negative effects on self-efficacy. Negative effects of the CDSS include increased on-scene time and a cognitive burden of using the system, affecting patient interaction and collaboration with team members. The CDSS's overall process advantage to the prehospital stroke process is assumed to lead to a prehospital care that is both safer and of higher quality. The key to user acceptance of a system such as this CDSS is the relative advantages of improved documentation process and the resulting patient journal. This could improve the overall prehospital stroke process.

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综合信息技术支持对院前卒中流程的影响:现实实验的结果。
脑卒中是一种严重的疾病,脑卒中的护理链也非常复杂。本研究旨在探讨计算机化决策支持系统(CDSS)对院前卒中流程的影响,重点关注工作流程和绩效。该研究采用了一种探索性方法,在现实情境模拟实验中进行随机对照交叉设计。研究比较了由 22 名急救医生组成的 11 个急救医疗服务(EMS)团队在使用(1)计算机化决策支持系统(CDSS)和(2)常规纸质流程支持时的临床表现。数据收集包括视频记录、事后问卷调查和事后访谈,数据分析采用定性和定量相结合的方法。在这项实验中,使用 CDSS 改善了患者评估、决策制定和对流程建议的遵从。CDSS 对急救临床医生自我效能感的影响极小,这表明即使系统使用繁琐,也不会对自我效能感产生任何负面影响。CDSS 的负面影响包括增加现场时间和使用系统的认知负担,影响与患者的互动以及与团队成员的协作。CDSS 在院前卒中流程中的整体优势被认为会带来更安全、更高质量的院前救治。用户接受 CDSS 等系统的关键在于其改进的记录流程和由此产生的患者日志的相对优势。这可以改善整个院前卒中流程。
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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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