Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review.

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2023-03-01 DOI:10.1007/s41666-023-00129-2
Luís Irgang, Henrik Barth, Magnus Holmén
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引用次数: 2

Abstract

Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions.

Supplementary information: The online version contains supplementary material available at 10.1007/s41666-023-00129-2.

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数据驱动技术在预防手术部位感染中作为价值创造的推动者:系统综述。
尽管现代医学取得了进步,但使用数据驱动技术(DDTs)来预防手术部位感染(ssi)仍然是一个主要挑战。学者们认识到,数据管理是感染预防的下一个前沿领域,但与使用ddt减轻SSI风险因素的好处和优势有关的许多方面在文献中仍不清楚且未得到充分探讨。本研究探讨了ddt如何在预防ssi中实现价值创造。本研究采用系统的文献综述方法和PRISMA声明来分析来自七个数据库的同行评议文章。审查纳入了59篇文章,并通过描述性分析和专题分析进行了分析。研究结果表明,在过去的5年里,人们对ddt预防SSI的兴趣越来越大,机器学习和智能手机应用程序被广泛用于SSI预防。ddt主要用于清洁和清洁污染手术中预防ssi,并常用于术后阶段患者相关数据的管理。ddt能够创造九种价值类别,这些价值类别分为四个维度:成本/牺牲、功能/工具、体验/享乐和象征/表达。本研究对滴滴涕在SSI预防中的应用所带来的价值创造方面提供了独特而系统的概述,并建议需要在四个领域进行进一步的研究:价值共同创造和产品服务系统、污染和肮脏手术中的滴滴涕、数据的合法性和可解释性以及数据驱动的干预措施。补充信息:在线版本包含补充资料,下载地址:10.1007/s41666-023-00129-2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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