Queensland Digital Health Centre, Centre for Health Services Research, The University of Queensland, Herston, QLD, Australia.
The University of Queensland-Ochsner Clinical School, Brisbane, QLD, Australia.
Int J Med Inform. 2024 Oct;190:105559. doi: 10.1016/j.ijmedinf.2024.105559. Epub 2024 Jul 18.
Hospitals are increasingly turning to patients for valuable feedback regarding their care experience. A common method to collect this information is patient reported experience measures (PREMs) surveys. Health care workers report qualitative PREMs as more interesting, relevant, and informative than quantitative survey responses. However, a major barrier to utilising qualitative PREMs data to drive quality improvements is a lack of resources to analyse the data. This scoping review aimed to review the methods used to analyse qualitative PREMs survey data from routine hospital care.
We utilised the JBI scoping review methodology, and searched four databases for articles from 2013 to 2023 which analysed qualitative PREMs survey data from routine care in hospitals. Study characteristics were extracted, as well as the analysis method - specifically, whether the study used traditional manual analysis methods in which the researcher reads the text and categorise the data, or automated methods utilising computers and algorithms to read and categorise the data.
From 960 unique articles, 123 went through full-text review and 54 were deemed eligible. 75.9 % used only manual content analysis methods to analyse the qualitative responses, 16.7 % of studies used a combination of manual and automated methods, and only 7.4 % used exclusively automated methods. Automated methods were used in 27.5 % of studies published 2019-2023, compared to 14.3 % of studies published 2013-2018. All bar one study using automated methods focused on investigating the validity of the automated methodology or used it to complement manual content analysis.
The studies included in this review show a transition from traditional time-consuming manual analyses to computerised methods enabling analysis at a larger scale. As the volume of PREMs data collected grows, efficient and effective ways to analyse qualitative PREMs data at scale are required to enable health services to capture the patient voice and drive consumer-centred improvements in care.
医院越来越多地向患者寻求有关其护理体验的宝贵反馈。收集这些信息的常用方法是患者报告的体验测量（PREM）调查。医疗保健工作者报告称，与定量调查回复相比，定性 PREM 更有趣、更相关、更具信息量。然而，利用定性 PREM 数据来推动质量改进的一个主要障碍是缺乏分析数据的资源。本范围审查旨在审查从常规医院护理中分析定性 PREM 调查数据的方法。
我们使用 JBI 范围审查方法，从 2013 年至 2023 年在四个数据库中搜索分析常规医院护理中定性 PREM 调查数据的文章。提取研究特征，以及分析方法-具体而言，研究是否使用传统的手动分析方法，即研究人员阅读文本并对数据进行分类，或使用计算机和算法阅读和分类数据的自动化方法。
从 960 篇独特的文章中，有 123 篇经过全文审查，有 54 篇被认为符合条件。75.9%的文章仅使用手动内容分析方法分析定性回复，16.7%的研究使用手动和自动化方法相结合，只有 7.4%的研究完全使用自动化方法。在 2019-2023 年发表的研究中，有 27.5%使用了自动化方法，而在 2013-2018 年发表的研究中，这一比例为 14.3%。使用自动化方法的研究无一例外地专注于调查自动化方法的有效性，或者使用它来补充手动内容分析。
本综述中包含的研究表明，从传统的耗时手动分析向计算机化方法的转变正在进行，这种方法可以进行更大规模的分析。随着 PREM 数据收集量的增加，需要高效、有效的方法来大规模分析定性 PREM 数据，以使卫生服务部门能够捕捉患者的意见，并推动以患者为中心的护理改进。
