医疗保健仪表板的开发、实施和评估方法:范围审查。

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS JMIR Medical Informatics Pub Date : 2024-12-10 DOI:10.2196/59828
Danielle Helminski, Jeremy B Sussman, Paul N Pfeiffer, Alex N Kokaly, Allison Ranusch, Anjana Deep Renji, Laura J Damschroder, Zach Landis-Lewis, Jacob E Kurlander
{"title":"医疗保健仪表板的开发、实施和评估方法:范围审查。","authors":"Danielle Helminski, Jeremy B Sussman, Paul N Pfeiffer, Alex N Kokaly, Allison Ranusch, Anjana Deep Renji, Laura J Damschroder, Zach Landis-Lewis, Jacob E Kurlander","doi":"10.2196/59828","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Dashboards have become ubiquitous in health care settings, but to achieve their goals, they must be developed, implemented, and evaluated using methods that help ensure they meet the needs of end users and are suited to the barriers and facilitators of the local context.</p><p><strong>Objective: </strong>This scoping review aimed to explore published literature on health care dashboards to characterize the methods used to identify factors affecting uptake, strategies used to increase dashboard uptake, and evaluation methods, as well as dashboard characteristics and context.</p><p><strong>Methods: </strong>MEDLINE, Embase, Web of Science, and the Cochrane Library were searched from inception through July 2020. Studies were included if they described the development or evaluation of a health care dashboard with publication from 2018-2020. Clinical setting, purpose (categorized as clinical, administrative, or both), end user, design characteristics, methods used to identify factors affecting uptake, strategies to increase uptake, and evaluation methods were extracted.</p><p><strong>Results: </strong>From 116 publications, we extracted data for 118 dashboards. Inpatient (45/118, 38.1%) and outpatient (42/118, 35.6%) settings were most common. Most dashboards had ≥2 stated purposes (84/118, 71.2%); of these, 54 of 118 (45.8%) were administrative, 43 of 118 (36.4%) were clinical, and 20 of 118 (16.9%) had both purposes. Most dashboards included frontline clinical staff as end users (97/118, 82.2%). To identify factors affecting dashboard uptake, half involved end users in the design process (59/118, 50%); fewer described formative usability testing (26/118, 22%) or use of any theory or framework to guide development, implementation, or evaluation (24/118, 20.3%). The most common strategies used to increase uptake included education (60/118, 50.8%); audit and feedback (59/118, 50%); and advisory boards (54/118, 45.8%). Evaluations of dashboards (84/118, 71.2%) were mostly quantitative (60/118, 50.8%), with fewer using only qualitative methods (6/118, 5.1%) or a combination of quantitative and qualitative methods (18/118, 15.2%).</p><p><strong>Conclusions: </strong>Most dashboards forego steps during development to ensure they suit the needs of end users and the clinical context; qualitative evaluation-which can provide insight into ways to improve dashboard effectiveness-is uncommon. Education and audit and feedback are frequently used to increase uptake. These findings illustrate the need for promulgation of best practices in dashboard development and will be useful to dashboard planners.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e59828"},"PeriodicalIF":3.1000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11651422/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development, Implementation, and Evaluation Methods for Dashboards in Health Care: Scoping Review.\",\"authors\":\"Danielle Helminski, Jeremy B Sussman, Paul N Pfeiffer, Alex N Kokaly, Allison Ranusch, Anjana Deep Renji, Laura J Damschroder, Zach Landis-Lewis, Jacob E Kurlander\",\"doi\":\"10.2196/59828\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Dashboards have become ubiquitous in health care settings, but to achieve their goals, they must be developed, implemented, and evaluated using methods that help ensure they meet the needs of end users and are suited to the barriers and facilitators of the local context.</p><p><strong>Objective: </strong>This scoping review aimed to explore published literature on health care dashboards to characterize the methods used to identify factors affecting uptake, strategies used to increase dashboard uptake, and evaluation methods, as well as dashboard characteristics and context.</p><p><strong>Methods: </strong>MEDLINE, Embase, Web of Science, and the Cochrane Library were searched from inception through July 2020. Studies were included if they described the development or evaluation of a health care dashboard with publication from 2018-2020. Clinical setting, purpose (categorized as clinical, administrative, or both), end user, design characteristics, methods used to identify factors affecting uptake, strategies to increase uptake, and evaluation methods were extracted.</p><p><strong>Results: </strong>From 116 publications, we extracted data for 118 dashboards. Inpatient (45/118, 38.1%) and outpatient (42/118, 35.6%) settings were most common. Most dashboards had ≥2 stated purposes (84/118, 71.2%); of these, 54 of 118 (45.8%) were administrative, 43 of 118 (36.4%) were clinical, and 20 of 118 (16.9%) had both purposes. Most dashboards included frontline clinical staff as end users (97/118, 82.2%). To identify factors affecting dashboard uptake, half involved end users in the design process (59/118, 50%); fewer described formative usability testing (26/118, 22%) or use of any theory or framework to guide development, implementation, or evaluation (24/118, 20.3%). The most common strategies used to increase uptake included education (60/118, 50.8%); audit and feedback (59/118, 50%); and advisory boards (54/118, 45.8%). Evaluations of dashboards (84/118, 71.2%) were mostly quantitative (60/118, 50.8%), with fewer using only qualitative methods (6/118, 5.1%) or a combination of quantitative and qualitative methods (18/118, 15.2%).</p><p><strong>Conclusions: </strong>Most dashboards forego steps during development to ensure they suit the needs of end users and the clinical context; qualitative evaluation-which can provide insight into ways to improve dashboard effectiveness-is uncommon. Education and audit and feedback are frequently used to increase uptake. These findings illustrate the need for promulgation of best practices in dashboard development and will be useful to dashboard planners.</p>\",\"PeriodicalId\":56334,\"journal\":{\"name\":\"JMIR Medical Informatics\",\"volume\":\"12 \",\"pages\":\"e59828\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11651422/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/59828\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/59828","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0

摘要

背景:仪表板在医疗保健环境中已经无处不在,但要实现其目标,必须使用有助于确保其满足最终用户需求并适合当地环境障碍和促进因素的方法来开发、实施和评估仪表板。目的:本综述旨在探讨已发表的关于医疗保健仪表板的文献,以描述用于确定影响仪表板吸收因素的方法、用于增加仪表板吸收的策略、评估方法以及仪表板特征和背景。方法:检索MEDLINE、Embase、Web of Science和Cochrane Library从成立到2020年7月。如果研究描述了2018-2020年发布的医疗保健仪表板的开发或评估,则将其纳入研究。提取了临床环境、目的(分类为临床、行政或两者)、最终用户、设计特征、用于确定影响摄取因素的方法、增加摄取的策略和评估方法。结果:从116篇出版物中,我们提取了118个仪表板的数据。住院(45/118,38.1%)和门诊(42/118,35.6%)最常见。大多数仪表板有≥2个规定的用途(84/118,71.2%);其中,54 / 118(45.8%)为行政目的,43 / 118(36.4%)为临床目的,20 / 118(16.9%)兼有两种目的。大多数仪表板将一线临床工作人员作为最终用户(97/118,82.2%)。为了确定影响仪表板使用的因素,一半的人在设计过程中涉及最终用户(59/ 118,50%);较少描述形成性可用性测试(26/ 118,22%)或使用任何理论或框架来指导开发、实现或评估(24/ 118,20.3%)。提高吸收率的最常见策略包括教育(60/ 118,50.8%);审核和反馈(59/ 118,50%);顾问委员会(54/118,45.8%)。对仪表板的评价以定量评价为主(60/118,50.8%),仅定性评价为主(6/118,5.1%)或定量与定性结合评价较少(18/118,15.2%)。结论:大多数仪表板在开发过程中放弃了一些步骤,以确保它们符合最终用户和临床环境的需求;定性评估——可以提供改进仪表板有效性的方法——是不常见的。经常使用教育、审计和反馈来增加吸收。这些发现说明了在仪表板开发中颁布最佳实践的必要性,并将对仪表板规划者有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Development, Implementation, and Evaluation Methods for Dashboards in Health Care: Scoping Review.

Background: Dashboards have become ubiquitous in health care settings, but to achieve their goals, they must be developed, implemented, and evaluated using methods that help ensure they meet the needs of end users and are suited to the barriers and facilitators of the local context.

Objective: This scoping review aimed to explore published literature on health care dashboards to characterize the methods used to identify factors affecting uptake, strategies used to increase dashboard uptake, and evaluation methods, as well as dashboard characteristics and context.

Methods: MEDLINE, Embase, Web of Science, and the Cochrane Library were searched from inception through July 2020. Studies were included if they described the development or evaluation of a health care dashboard with publication from 2018-2020. Clinical setting, purpose (categorized as clinical, administrative, or both), end user, design characteristics, methods used to identify factors affecting uptake, strategies to increase uptake, and evaluation methods were extracted.

Results: From 116 publications, we extracted data for 118 dashboards. Inpatient (45/118, 38.1%) and outpatient (42/118, 35.6%) settings were most common. Most dashboards had ≥2 stated purposes (84/118, 71.2%); of these, 54 of 118 (45.8%) were administrative, 43 of 118 (36.4%) were clinical, and 20 of 118 (16.9%) had both purposes. Most dashboards included frontline clinical staff as end users (97/118, 82.2%). To identify factors affecting dashboard uptake, half involved end users in the design process (59/118, 50%); fewer described formative usability testing (26/118, 22%) or use of any theory or framework to guide development, implementation, or evaluation (24/118, 20.3%). The most common strategies used to increase uptake included education (60/118, 50.8%); audit and feedback (59/118, 50%); and advisory boards (54/118, 45.8%). Evaluations of dashboards (84/118, 71.2%) were mostly quantitative (60/118, 50.8%), with fewer using only qualitative methods (6/118, 5.1%) or a combination of quantitative and qualitative methods (18/118, 15.2%).

Conclusions: Most dashboards forego steps during development to ensure they suit the needs of end users and the clinical context; qualitative evaluation-which can provide insight into ways to improve dashboard effectiveness-is uncommon. Education and audit and feedback are frequently used to increase uptake. These findings illustrate the need for promulgation of best practices in dashboard development and will be useful to dashboard planners.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
期刊最新文献
Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study. The Impact of Data Control and Delayed Discounting on the Public's Willingness to Share Different Types of Health Care Data: Empirical Study. Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study. A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study. Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1