文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

英格兰国民保健署信托机构中脓毒症数字警报的看法和用途:医护专业人员的定性研究。

Views and Uses of Sepsis Digital Alerts in National Health Service Trusts in England: Qualitative Study With Health Care Professionals.

机构信息

Nuffield Department of Primary Care Health Sciences, Medical Division, University of Oxford, Oxford, United Kingdom.

National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.

出版信息

JMIR Hum Factors. 2024 Oct 15;11:e56949. doi: 10.2196/56949.


DOI:10.2196/56949
PMID:39405513
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11522658/
Abstract

BACKGROUND: Sepsis is a common cause of serious illness and death. Sepsis management remains challenging and suboptimal. To support rapid sepsis diagnosis and treatment, screening tools have been embedded into hospital digital systems to appear as digital alerts. The implementation of digital alerts to improve the management of sepsis and deterioration is a complex intervention that has to fit with team workflow and the views and practices of hospital staff. Despite the importance of human decision-making and behavior in optimal implementation, there are limited qualitative studies that explore the views and experiences of health care professionals regarding digital alerts as sepsis or deterioration computerized clinician decision support systems (CCDSSs). OBJECTIVE: This study aims to explore the views and experiences of health care professionals on the use of sepsis or deterioration CCDSSs and to identify barriers and facilitators to their implementation and use in National Health Service (NHS) hospitals. METHODS: We conducted a qualitative, multisite study with unstructured observations and semistructured interviews with health care professionals from emergency departments, outreach teams, and intensive or acute units in 3 NHS hospital trusts in England. Data from both interviews and observations were analyzed together inductively using thematic analysis. RESULTS: A total of 22 health care professionals were interviewed, and 12 observation sessions were undertaken. A total of four themes regarding digital alerts were identified: (1) support decision-making as nested in electronic health records, but never substitute professionals' knowledge and experience; (2) remind to take action according to the context, such as the hospital unit and the job role; (3) improve the alerts and their introduction, by making them more accessible, easy to use, not intrusive, more accurate, as well as integrated across the whole health care system; and (4) contextual factors affecting views and use of alerts in the NHS trusts. Digital alerts are more optimally used in general hospital units with a lower senior decision maker:patient ratio and by health care professionals with experience of a similar technology. Better use of the alerts was associated with quality improvement initiatives and continuous sepsis training. The trusts' features, such as the presence of a 24/7 emergency outreach team, good technological resources, and staffing and teamwork, favored a more optimal use. CONCLUSIONS: Trust implementation of sepsis or deterioration CCDSSs requires support on multiple levels and at all phases of the intervention, starting from a prego-live analysis addressing organizational needs and readiness. Advancements toward minimally disruptive and smart digital alerts as sepsis or deterioration CCDSSs, which are more accurate and specific but at the same time scalable and accessible, require policy changes and investments in multidisciplinary research.

摘要

背景:脓毒症是导致严重疾病和死亡的常见原因。脓毒症的管理仍然具有挑战性且不尽如人意。为了支持快速脓毒症诊断和治疗,筛选工具已嵌入医院数字系统中,作为数字警报出现。实施数字警报以改善脓毒症和病情恶化的管理是一项复杂的干预措施,必须与团队工作流程以及医院工作人员的观点和实践相适应。尽管在最佳实施中人类决策和行为至关重要,但很少有定性研究探讨医疗保健专业人员对脓毒症或恶化计算机化临床医生决策支持系统 (CCDSS) 的看法和经验。

目的:本研究旨在探讨医疗保健专业人员对使用脓毒症或恶化 CCDSS 的看法和经验,并确定在英国国民保健服务 (NHS) 医院实施和使用这些系统的障碍和促进因素。

方法:我们在英格兰的 3 家 NHS 医院信托基金的急诊部门、外展团队和重症监护或急症病房中,对医疗保健专业人员进行了一项定性、多地点的研究,包括非结构化观察和半结构化访谈。使用主题分析对访谈和观察数据进行了归纳分析。

结果:共对 22 名医疗保健专业人员进行了访谈,并进行了 12 次观察。确定了四个关于数字警报的主题:(1) 支持作为电子健康记录中的嵌套决策,但从不替代专业人员的知识和经验;(2) 根据上下文(如医院科室和工作角色)提醒采取行动;(3) 通过使警报更易于访问、易于使用、不具侵入性、更准确以及在整个医疗保健系统中集成来改进警报及其引入;(4) 影响 NHS 信托中警报看法和使用的上下文因素。在一般医院科室中,数字警报在具有较低高级决策者与患者比例的情况下以及在具有类似技术经验的医疗保健专业人员中得到了更优化的使用。更好地使用警报与质量改进举措和持续的脓毒症培训相关。信托的特点,如 24/7 紧急外展团队的存在、良好的技术资源以及人员配备和团队合作,有利于更优化地使用。

结论:脓毒症或恶化 CCDSS 的信托实施需要在多个层面和干预的所有阶段提供支持,从解决组织需求和准备情况的预上线分析开始。朝着更准确和更具体但同时更具可扩展性和可访问性的最小干扰和智能数字警报作为脓毒症或恶化 CCDSS 的发展,需要政策变革和对多学科研究的投资。

相似文献

[1]
Views and Uses of Sepsis Digital Alerts in National Health Service Trusts in England: Qualitative Study With Health Care Professionals.

JMIR Hum Factors. 2024-10-15

[2]
Evaluating mental health decision units in acute care pathways (DECISION): a quasi-experimental, qualitative and health economic evaluation.

Health Soc Care Deliv Res. 2023-12

[3]
Health professionals' experience of teamwork education in acute hospital settings: a systematic review of qualitative literature.

JBI Database System Rev Implement Rep. 2016-4

[4]
Rapid evaluation of the Special Measures for Quality and challenged provider regimes: a mixed-methods study.

Health Soc Care Deliv Res. 2023-10

[5]
The implementation, use and sustainability of a clinical decision support system for medication optimisation in primary care: A qualitative evaluation.

PLoS One. 2021

[6]
Implementation and adoption of nationwide electronic health records in secondary care in England: final qualitative results from prospective national evaluation in "early adopter" hospitals.

BMJ. 2011-10-17

[7]
Current experience and future potential of facilitating access to digital NHS primary care services in England: the Di-Facto mixed-methods study.

Health Soc Care Deliv Res. 2024-9

[8]
Prevalence of electronic screening for sepsis in National Health Service acute hospitals in England.

BMJ Health Care Inform. 2023-5

[9]

2016-5

[10]
Digital First Primary Care for those with multiple long-term conditions: a rapid review of the views of stakeholders.

Health Soc Care Deliv Res. 2024-7

引用本文的文献

[1]
Digital innovation in healthcare: quantifying the impact of digital sepsis screening tools on patient outcomes-a multi-site natural experiment.

BMJ Health Care Inform. 2025-4-27

[2]
The sepsis journey and where digital alerts can help: a qualitative, interview study with survivors and family members in England.

Front Public Health. 2025-3-26

本文引用的文献

[1]
Mobilizing pilot-based evidence for the spread and sustainability of innovations in healthcare: The role of innovation intermediaries.

Soc Sci Med. 2024-1

[2]
Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study.

Crit Care Explor. 2023-6-30

[3]
2023 Update on Sepsis and Septic Shock in Adult Patients: Management in the Emergency Department.

J Clin Med. 2023-4-28

[4]
Swiss Sepsis National Action Plan: A coordinated national action plan to stop sepsis-related preventable deaths and to improve the support of people affected by sepsis in Switzerland.

Front Med (Lausanne). 2023-2-20

[5]
Factors influencing the implementation of decision support systems for antibiotic prescription in hospitals: a systematic review.

BMC Med Inform Decis Mak. 2023-2-6

[6]
Validation of Sepsis-3 using survival analysis and clinical evaluation of quick SOFA, SIRS, and burn-specific SIRS for sepsis in burn patients with suspected infection.

PLoS One. 2023

[7]
NEWS2 and improving outcomes from sepsis.

Clin Med (Lond). 2022-11

[8]
Desired Characteristics of a Clinical Decision Support System for Early Sepsis Recognition: Interview Study Among Hospital-Based Clinicians.

JMIR Hum Factors. 2022-10-21

[9]
Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system.

NPJ Digit Med. 2022-7-21

[10]
Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.

Nat Med. 2022-7

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索