Turku University Hospital, Department of Nursing Science, University of Turku, Turku, Finland.
Department of Computer Science, Aalto University, Espoo, Finland.
J Nurs Manag. 2022 Nov;30(8):3726-3735. doi: 10.1111/jonm.13802. Epub 2022 Sep 27.
The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing-sensitive indicators in acute cardiac care.
Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data-based solutions that automatically extract and help interpret data from electronic health records.
This is a deductive descriptive study that followed the theory of value-added analysis. A random sample from electronic health records of 230 patients was analysed for selected indicators. The data included documentation in structured and free-text format.
One thousand six hundred seventy-six expressions were extracted and divided into (1) established and (2) unestablished expressions, providing positive, neutral and negative descriptions related to care quality.
Electronic health records provide a potential source of information for information systems to support assessment of care quality. More research is warranted to develop, test and evaluate the effectiveness of such tools in practice.
Knowledge-based health care management would benefit from the development and implementation of advanced information systems, which use continuously generated already available real-time big data for improved data access and interpretation to better support nursing management in quality assessment.
本研究旨在探讨通过急性心脏护理中的护理敏感指标,利用电子健康记录评估护理质量的潜力。
护理质量是一个多方面的现象,使其整体评估变得困难。急性心脏护理单元的质量评估系统可以从基于大数据的解决方案中受益,这些解决方案可以自动提取和帮助解释电子健康记录中的数据。
这是一项遵循增值分析理论的演绎描述性研究。对 230 名患者的电子健康记录进行随机抽样,分析选定的指标。数据包括结构化和自由文本格式的文档。
共提取了 1676 个表达式,并分为(1)已建立的和(2)未建立的表达式,提供了与护理质量相关的积极、中性和负面描述。
电子健康记录为信息系统提供了信息来源,以支持护理质量评估。需要进一步研究来开发、测试和评估此类工具在实践中的有效性。
基于知识的医疗保健管理将受益于先进信息系统的开发和实施,这些系统使用持续生成的现有实时大数据来改善数据访问和解释,以更好地支持护理管理在质量评估中的作用。