衡量 HL7® FHIR® 标准在支持 3 个公共卫生登记数据采集方面的覆盖范围。

IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Systems Pub Date : 2024-02-08 DOI:10.1007/s10916-023-02033-z
Manju Bikkanuri, Taiquitha T Robins, Lori Wong, Emel Seker, Melody L Greer, Tremaine B Williams, Maryam Y Garza
{"title":"衡量 HL7® FHIR® 标准在支持 3 个公共卫生登记数据采集方面的覆盖范围。","authors":"Manju Bikkanuri, Taiquitha T Robins, Lori Wong, Emel Seker, Melody L Greer, Tremaine B Williams, Maryam Y Garza","doi":"10.1007/s10916-023-02033-z","DOIUrl":null,"url":null,"abstract":"<p><p>With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7<sup>®</sup>) Fast Healthcare Interoperability Resources (FHIR<sup>®</sup>) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR<sup>®</sup> ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries. That said, in order to understand the current utility of FHIR<sup>®</sup> for supporting the public health use case, we must first measure the extent to which the standard resources map to the required registry data elements. Thus, using a systematic mapping approach, we evaluated the level of completeness of the FHIR<sup>®</sup> standard to support data collection for three public health registries (Trauma, Stroke, and National Surgical Quality Improvement Program). On average, approximately 80% of data elements were available in FHIR<sup>®</sup> (71%, 77%, and 92%, respectively; inter-annotator agreement rates: 82%, 78%, and 72%, respectively). This tells us that there is the potential for significant automation to support EHR-to-Registry data exchange, which will reduce the amount of manual, error-prone processes and ensure higher data quality. Further, identification of the remaining 20% of data elements that are \"not mapped\" will enable us to improve the standard and develop profiles that will better fit the registry data model.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"18"},"PeriodicalIF":3.5000,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10853080/pdf/","citationCount":"0","resultStr":"{\"title\":\"Measuring the Coverage of the HL7® FHIR® Standard in Supporting Data Acquisition for 3 Public Health Registries.\",\"authors\":\"Manju Bikkanuri, Taiquitha T Robins, Lori Wong, Emel Seker, Melody L Greer, Tremaine B Williams, Maryam Y Garza\",\"doi\":\"10.1007/s10916-023-02033-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7<sup>®</sup>) Fast Healthcare Interoperability Resources (FHIR<sup>®</sup>) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR<sup>®</sup> ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries. That said, in order to understand the current utility of FHIR<sup>®</sup> for supporting the public health use case, we must first measure the extent to which the standard resources map to the required registry data elements. Thus, using a systematic mapping approach, we evaluated the level of completeness of the FHIR<sup>®</sup> standard to support data collection for three public health registries (Trauma, Stroke, and National Surgical Quality Improvement Program). On average, approximately 80% of data elements were available in FHIR<sup>®</sup> (71%, 77%, and 92%, respectively; inter-annotator agreement rates: 82%, 78%, and 72%, respectively). This tells us that there is the potential for significant automation to support EHR-to-Registry data exchange, which will reduce the amount of manual, error-prone processes and ensure higher data quality. Further, identification of the remaining 20% of data elements that are \\\"not mapped\\\" will enable us to improve the standard and develop profiles that will better fit the registry data model.</p>\",\"PeriodicalId\":16338,\"journal\":{\"name\":\"Journal of Medical Systems\",\"volume\":\"48 1\",\"pages\":\"18\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-02-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10853080/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Systems\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10916-023-02033-z\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Systems","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10916-023-02033-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

摘要

随着向州和国家公共卫生登记处及时提交数据的需求日益增加,目前手动获取和提交数据的方法已不能满足需要。在临床实践中,联邦法规现在强制要求使用数据报文标准,即健康七级(HL7®)快速医疗互操作性资源(FHIR®)标准,以促进临床(患者)数据的电子交换。在研究和公共卫生实践中,我们还可以利用 FHIR® 和支持临床实践数据交换的现有基础设施,实现电子病历和公共卫生登记之间的无缝交换。也就是说,为了了解 FHIR® 目前在支持公共卫生用例方面的效用,我们必须首先衡量标准资源与所需登记数据元素的映射程度。因此,我们使用系统映射法评估了 FHIR® 标准支持三个公共卫生登记处(创伤、中风和国家外科质量改进计划)数据收集的完整程度。平均而言,约 80% 的数据元素在 FHIR® 中可用(分别为 71%、77% 和 92%;标注者之间的一致率分别为 82%、78% 和 72%):注释者之间的一致率分别为 82%、78% 和 72%)。这告诉我们,在支持电子病历到注册表的数据交换方面,有很大的自动化潜力,这将减少手工操作和容易出错的流程,并确保更高的数据质量。此外,识别剩余 20% 的 "未映射 "数据元素将使我们能够改进标准并开发更适合登记处数据模型的配置文件。
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
Measuring the Coverage of the HL7® FHIR® Standard in Supporting Data Acquisition for 3 Public Health Registries.

With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR® ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries. That said, in order to understand the current utility of FHIR® for supporting the public health use case, we must first measure the extent to which the standard resources map to the required registry data elements. Thus, using a systematic mapping approach, we evaluated the level of completeness of the FHIR® standard to support data collection for three public health registries (Trauma, Stroke, and National Surgical Quality Improvement Program). On average, approximately 80% of data elements were available in FHIR® (71%, 77%, and 92%, respectively; inter-annotator agreement rates: 82%, 78%, and 72%, respectively). This tells us that there is the potential for significant automation to support EHR-to-Registry data exchange, which will reduce the amount of manual, error-prone processes and ensure higher data quality. Further, identification of the remaining 20% of data elements that are "not mapped" will enable us to improve the standard and develop profiles that will better fit the registry data model.

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 Medical Systems
Journal of Medical Systems 医学-卫生保健
CiteScore
11.60
自引率
1.90%
发文量
83
审稿时长
4.8 months
期刊介绍: Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician''s office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.
期刊最新文献
Self-Supervised Learning for Near-Wild Cognitive Workload Estimation. Electronic Health Records Sharing Based on Consortium Blockchain. Large Language Models in Healthcare: An Urgent Call for Ongoing, Rigorous Validation. Why Clinicians should Care about YouCare and Other Wearable Health Devices. A Joint Message from the Outgoing and Incoming Editors-in-Chief.
×
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