针对 CTSA 附属电子健康记录数据网络的全国性未满足需求评估:客户发现方法。

IF 2.1 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Journal of Clinical and Translational Science Pub Date : 2024-10-03 eCollection Date: 2024-01-01 DOI:10.1017/cts.2024.609
Nallely Mora, Madeline Mehall, Lindsay A Lennox, Harold A Pincus, David Charron, Elaine H Morrato
{"title":"针对 CTSA 附属电子健康记录数据网络的全国性未满足需求评估:客户发现方法。","authors":"Nallely Mora, Madeline Mehall, Lindsay A Lennox, Harold A Pincus, David Charron, Elaine H Morrato","doi":"10.1017/cts.2024.609","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE).</p><p><strong>Methods: </strong>We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group.</p><p><strong>Results: </strong>Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers' unmet needs.</p><p><strong>Conclusions: </strong>Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science.</p>","PeriodicalId":15529,"journal":{"name":"Journal of Clinical and Translational Science","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523010/pdf/","citationCount":"0","resultStr":"{\"title\":\"A national unmet needs assessment for CTSA-affiliated electronic health record data networks: A customer discovery approach.\",\"authors\":\"Nallely Mora, Madeline Mehall, Lindsay A Lennox, Harold A Pincus, David Charron, Elaine H Morrato\",\"doi\":\"10.1017/cts.2024.609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE).</p><p><strong>Methods: </strong>We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group.</p><p><strong>Results: </strong>Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers' unmet needs.</p><p><strong>Conclusions: </strong>Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science.</p>\",\"PeriodicalId\":15529,\"journal\":{\"name\":\"Journal of Clinical and Translational Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523010/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical and Translational Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/cts.2024.609\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical and Translational Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/cts.2024.609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

导言:过去二十年来,电子健康记录(EHR)数据网络的扩展大大改善了数据共享的可及性和流程。然而,在满足临床与转化科学奖(CTSA)中心的需求方面还存在差距,尤其是与真实世界数据(RWD)和真实世界证据(RWE)相关的需求:我们采用了一种混合方法来构建全面的需求评估,其中包括(方法:我们采用混合方法构建了全面的需求评估,其中包括:(1)景观背景分析,以了解竞争环境;(2)客户发现,以确定利益相关者和与电子病历数据网络相关的价值主张。方法包括调查、访谈和焦点小组:32 家 CTSA 机构为分析提供了数据。共进行了 54 次访谈和一次焦点小组讨论。我们围绕五个新出现的主题对研究结果进行了综合:(1)CTSA 的细分需求因资源而异;(2)团队科学是成功的关键;(3)数据质量可产生对网络的信任;(4)能力建设因研究人员的职业阶段和 CTSA 的现有资源而异;以及(5)研究人员的需求未得到满足:根据研究结果,像 ENACT 这样的电子病历数据网络要想满足 CTSA 联盟内学术研究中心的期望,就需要考虑填补我们的研究发现的空白:促进团队科学、提高劳动力能力、实现数据治理信任和运行效率,并帮助学习型医疗系统验证、应用和推广证据,以支持质量改进和高价值护理。这些研究结果与美国国立卫生研究院 NCATS 的数据科学战略计划相一致。
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
A national unmet needs assessment for CTSA-affiliated electronic health record data networks: A customer discovery approach.

Introduction: The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE).

Methods: We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group.

Results: Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers' unmet needs.

Conclusions: Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science.

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
Journal of Clinical and Translational Science
Journal of Clinical and Translational Science MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
2.80
自引率
26.90%
发文量
437
审稿时长
18 weeks
期刊最新文献
Overview of ACTIV trial-specific lessons learned. Preparing better: Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) therapeutics trials lessons learned: A call to the future. The future is now: Using the lessons learned from the ACTIV COVID-19 therapeutics trials to create an inclusive and efficient clinical trials enterprise. ACTIV trials: Lessons learned in trial design in the setting of an emergent pandemic. Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.
×
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