Healthcare dashboard technologies and data visualization for lipid management: A scoping review.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS BMC Medical Informatics and Decision Making Pub Date : 2024-11-21 DOI:10.1186/s12911-024-02730-w
Mahnaz Samadbeik, Teyl Engstrom, Elton H Lobo, Karem Kostner, Jodie A Austin, Jason D Pole, Clair Sullivan
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Abstract

Background: Lipid disorders significantly increase cardiovascular disease (CVD) risk, the leading cause of mortality worldwide. Effective lipid management is critical for improving health outcomes. Traditional screening methods face challenges due to data complexity and the need for timely decision-making. Data visualization and dashboard technologies offer clear, actionable insights and supporting informed decision-making. This study investigates the use of these technologies in lipid management and their impacts on the quadruple aim of healthcare.

Methods: This scoping review followed the Joanna Briggs Institute (JBI) approach, focusing on studies involving dashboard technologies or data visualization in lipid management. A comprehensive search across multiple databases (Embase, Web of Science, PubMed, Scopus, CINAHL) and gray literature was conducted, including English-language publications from 2014 to 2024. Data were analyzed using quantitative descriptive and qualitative content analysis to evaluate the key features, clinical applications, and outcomes.

Results: Twenty-seven studies met the inclusion criteria, primarily focusing on dashboard utilization by physicians for managing diabetes and CVD, utilizing electronic medical records and clinical guidelines. Key analysis methods included comparing key performance indicators (KPIs) (85.2%) and trend analysis (74.1%). Lipid management workflows emphasized prevention (88.9%) and treatment planning (77.8%). Interventions included care packages (comprehensive sets of interventions for patient care), decision support systems, web-based tools, and mobile health solutions. Regarding Quadruple Aim outcomes: 12 studies focused on improving population health (8 positive, 4 no change), 9 on clinical outcomes (5 positive, 4 no change), 6 on provider work life (5 positive), 5 on patient experience (positive changes in education and time management), and 2 on cost reduction (1 positive, 1 negative).

Conclusions: Dashboards are important tools in managing lipid disorders in managing lipid disorders, integrating with educational tools, collaborative care models, and decision support systems. Although they are effective in enhancing population health and clinical experiences, their impact on patient outcomes and cost reduction requires further exploration. Future research should focus on detailed evaluations of dashboard impacts on patient outcomes and cost-effectiveness, emphasizing precision prevention of chronic diseases.

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用于血脂管理的医疗仪表板技术和数据可视化:范围综述。
背景:血脂紊乱会大大增加心血管疾病(CVD)的风险,而心血管疾病是导致全球死亡的主要原因。有效的血脂管理对改善健康状况至关重要。由于数据的复杂性和及时决策的需要,传统的筛查方法面临着挑战。数据可视化和仪表板技术可提供清晰、可操作的见解,支持知情决策。本研究调查了这些技术在血脂管理中的应用及其对医疗保健四重目标的影响:本范围综述采用乔安娜-布里格斯研究所(JBI)的方法,重点关注血脂管理中涉及仪表盘技术或数据可视化的研究。我们对多个数据库(Embase、Web of Science、PubMed、Scopus、CINAHL)和灰色文献进行了全面检索,包括2014年至2024年的英文出版物。采用定量描述性和定性内容分析法对数据进行了分析,以评估其主要特征、临床应用和结果:结果:27 项研究符合纳入标准,主要关注医生利用电子病历和临床指南管理糖尿病和心血管疾病的仪表盘使用情况。主要分析方法包括比较关键绩效指标(KPI)(85.2%)和趋势分析(74.1%)。血脂管理工作流程强调预防(88.9%)和治疗计划(77.8%)。干预措施包括护理包(用于患者护理的综合干预措施)、决策支持系统、网络工具和移动医疗解决方案。关于四重目标成果:12 项研究关注改善人群健康(8 项积极,4 项无变化),9 项关注临床结果(5 项积极,4 项无变化),6 项关注医疗服务提供者的工作生活(5 项积极),5 项关注患者体验(教育和时间管理方面的积极变化),2 项关注降低成本(1 项积极,1 项消极):结论:仪表板是管理血脂紊乱的重要工具,可与教育工具、协作护理模式和决策支持系统相结合。虽然仪表板能有效提高人群健康水平和临床经验,但其对患者预后和降低成本的影响还需进一步探讨。未来的研究应侧重于详细评估仪表盘对患者预后和成本效益的影响,强调慢性疾病的精准预防。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
期刊最新文献
Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data. Multimodal machine learning for language and speech markers identification in mental health. Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning. Healthcare dashboard technologies and data visualization for lipid management: A scoping review. Predictive model for congenital heart disease in children of Pakistan by using structural equation modeling.
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