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护理实践中数据科学实施趋势:2021 年文献回顾。

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

机构信息

University of Wisconsin-Madison School of Nursing, Madison, Wisconsin, United States.

Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States.

出版信息

Appl Clin Inform. 2023 May;14(3):585-593. doi: 10.1055/a-2088-2893. Epub 2023 May 7.

Abstract

OBJECTIVES

The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

METHODS

We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework.

RESULTS

Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care.

CONCLUSION

In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

摘要

目的

本研究旨在对 2021 年文献中基于数据科学的应用程序在结构或结果相关的护士敏感指标方面的实施情况进行综述,从而为读者提供有关所关注的护理指标趋势、患者人群和重点关注的环境、以及在实施这些工具过程中发现的经验教训和挑战的信息。

方法

我们对文献进行了严格的描述性综述,以确定 2021 年发表的相关研究。我们提取了有关模型开发、实施相关策略和措施、经验教训以及挑战和利益相关者参与的数据。我们还评估了数据科学应用实施报告是否符合人工智能(AI)驱动的决策支持系统的临床研究和评估(DECIDE-AI)框架的指导原则。

结果

在 PubMed(NLM)和 CINAHL(EBSCOhost)中发现的 4943 篇文章中,有 11 篇最终被纳入综述和数据提取。利用数据科学开发的系统针对成年患者人群,并主要部署在医院环境中。目标临床领域包括死亡率/恶化、利用/资源分配和医院获得性感染/COVID-19。开发团队的组成和所涉及的利益相关者类型各不相同。研究团队更频繁地报告实施方法,而不是实施结果。大多数研究提供了经验教训,可以为未来在医疗保健中实施数据科学系统提供信息。

结论

2021 年,很少有研究报告数据科学驱动的应用程序在结构或结果相关的护士敏感指标方面的实施情况。为了使这些系统能够在医疗保健环境中成功采用,需要解决在实施策略方面共享的差距。

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