Suppr超能文献

低资源环境下基于机构的新生儿护理学习型医疗系统的开发与实施经验:Neotree

Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree.

作者信息

Heys Michelle, Kesler Erin, Sassoon Yali, Wilson Emma, Fitzgerald Felicity, Gannon Hannah, Hull-Bailey Tim, Chimhini Gwendoline, Khan Nushrat, Cortina-Borja Mario, Nkhoma Deliwe, Chiyaka Tarisai, Stevenson Alex, Crehan Caroline, Chiume Msandeni Esther, Chimhuya Simbarashe

机构信息

Population, Policy and Practice Research and Teaching Department University College London Great Ormond Street Institute of Child Health London UK.

Children's Hospital of Philadelphia General, Thoracic, and Fetal Surgery Newborn Intensive Care Unit Philadelphia USA.

出版信息

Learn Health Syst. 2022 Apr 6;7(1):e10310. doi: 10.1002/lrh2.10310. eCollection 2023 Jan.

Abstract

INTRODUCTION

Improving peri- and postnatal facility-based care in low-resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost-effective, simple, evidence-based interventions. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high-resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS.

METHODS

Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co-develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low-cost hardware and state-of-the-art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree.

RESULTS

Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement. Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID-19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects.

CONCLUSION

Human-centred digital development of a QI system for newborn care has demonstrated the potential of a sustainable learning healthcare system to improve newborn care and outcomes in LRS. Pilot implementation evaluation is ongoing in three of the four aforementioned hospitals (two in Zimbabwe and one in Malawi) and a larger scale clinical cost effectiveness trial is planned.

摘要

引言

在资源匮乏地区改善围产期和产后基于医疗机构的护理,每天可挽救6000多名婴儿的生命。每年240万例新生儿死亡和200万例死产中,大多数发生在资源匮乏地区的医疗机构,通过实施具有成本效益、简单且基于证据的干预措施是可以预防的。然而,在四分之一入住新生儿病房的婴儿会死亡的医疗系统中,实施这些干预措施具有挑战性。在资源丰富地区,医疗系统强化越来越多地通过学习型医疗系统来实现,以优化护理质量,但这种方法在资源匮乏地区很少见。

方法

自2014年以来,我们在孟加拉国、马拉维、津巴布韦和英国开展工作,共同开发并试点Neotree系统:一款带有数据可视化、链接和导出功能的安卓应用程序。其低成本硬件和先进软件用于支持医疗专业人员改善床边产后护理,并深入了解人群健康趋势。在此,我们总结Neotree的形成性概念化、开发和初步实施经验。

结果

迄今为止,来自约18000名婴儿以及四家医院(津巴布韦两家、马拉维两家)的400名医疗专业人员的数据显示,该系统具有高度可接受性、可行性、易用性,并提高了医疗专业人员提供新生儿护理的能力。数据还凸显了新生儿护理和质量改进方面的知识差距。在外部危机期间,例如2019年冠状病毒病(COVID-19)大流行期间,实施过程具有韧性且提供了信息。我们已经证明临床护理有所改善,且数据用于质量改进(QI)项目。

结论

以人为主导的新生儿护理质量改进系统的数字化开发,已证明可持续学习型医疗系统在资源匮乏地区改善新生儿护理及结局方面的潜力。上述四家医院中的三家(津巴布韦两家、马拉维一家)正在进行试点实施评估,并计划开展更大规模的临床成本效益试验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1592/9835040/c7a030829dd5/LRH2-7-e10310-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验