支持病历审查的下一代电子病历搜索引擎:系统用户研究与未来研究方向

Cheng Ye, Daniel Fabbri
{"title":"支持病历审查的下一代电子病历搜索引擎:系统用户研究与未来研究方向","authors":"Cheng Ye,&nbsp;Daniel Fabbri","doi":"10.1016/j.ject.2024.03.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Little research has been done on the user-centered document ranking approach, especially in a crowdsourcing chart review environment. As the starting point of designing and implementing the next generation of Electronic Medical Record (EMR) search engines, a systematic user study is needed to better understand the users' needs, challenges, and future research directions of EMR search engines.</p></div><div><h3>Materials and methods</h3><p>One primary observation during the user study is the need for a ranking method to better support the so-called \"early stopping\" reviewing strategy (i.e., reviewing only a subset of EMRs of one patient to make the final decision) during the clinical chart reviews. The authors proposed two novel user-centered ranking metrics: \"critical documents\" and \"negative guarantee ratio,\" to better measure the power of a ranking method in supporting the “early stopping” requirements during clinical chart reviews.</p></div><div><h3>Results</h3><p>The evaluation results show that i) traditional information retrieval metrics, such as the precision-at-K, have limitations in guiding the design and development of EMR search engines to better support clinical chart reviews; ii) there is not a global optimal ranking method that fits the needs of different chart reviews and different users; iii) a learning-to-rank approach cannot guarantee a stable and optimal ranking for different chart reviews and different users; and iv) A user-centered ranking metric, such as the negative guarantee ratio (NGR) metric is able to measure the “early-stopping” performance of ranking methods.</p></div><div><h3>Conclusions</h3><p>User-centered ranking metrics can better measure the power of ranking methods in supporting clinical chart reviews. Future research should explore more user-centered ranking metrics and evaluate their impact on real-world EMR search engines.</p></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"2 ","pages":"Pages 22-30"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949948824000179/pdfft?md5=36f2dbd89d4f348572f9e914feadf69c&pid=1-s2.0-S2949948824000179-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Next generation of electronic medical record search engines to support chart reviews: A systematic user study and future research direction\",\"authors\":\"Cheng Ye,&nbsp;Daniel Fabbri\",\"doi\":\"10.1016/j.ject.2024.03.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>Little research has been done on the user-centered document ranking approach, especially in a crowdsourcing chart review environment. As the starting point of designing and implementing the next generation of Electronic Medical Record (EMR) search engines, a systematic user study is needed to better understand the users' needs, challenges, and future research directions of EMR search engines.</p></div><div><h3>Materials and methods</h3><p>One primary observation during the user study is the need for a ranking method to better support the so-called \\\"early stopping\\\" reviewing strategy (i.e., reviewing only a subset of EMRs of one patient to make the final decision) during the clinical chart reviews. The authors proposed two novel user-centered ranking metrics: \\\"critical documents\\\" and \\\"negative guarantee ratio,\\\" to better measure the power of a ranking method in supporting the “early stopping” requirements during clinical chart reviews.</p></div><div><h3>Results</h3><p>The evaluation results show that i) traditional information retrieval metrics, such as the precision-at-K, have limitations in guiding the design and development of EMR search engines to better support clinical chart reviews; ii) there is not a global optimal ranking method that fits the needs of different chart reviews and different users; iii) a learning-to-rank approach cannot guarantee a stable and optimal ranking for different chart reviews and different users; and iv) A user-centered ranking metric, such as the negative guarantee ratio (NGR) metric is able to measure the “early-stopping” performance of ranking methods.</p></div><div><h3>Conclusions</h3><p>User-centered ranking metrics can better measure the power of ranking methods in supporting clinical chart reviews. Future research should explore more user-centered ranking metrics and evaluate their impact on real-world EMR search engines.</p></div>\",\"PeriodicalId\":100776,\"journal\":{\"name\":\"Journal of Economy and Technology\",\"volume\":\"2 \",\"pages\":\"Pages 22-30\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2949948824000179/pdfft?md5=36f2dbd89d4f348572f9e914feadf69c&pid=1-s2.0-S2949948824000179-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Economy and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949948824000179\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economy and Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949948824000179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

目的以用户为中心的文档排序方法研究甚少,尤其是在众包病历审查环境中。作为设计和实施下一代电子病历(EMR)搜索引擎的起点,需要进行系统的用户研究,以更好地了解用户的需求、挑战和 EMR 搜索引擎的未来研究方向。材料和方法用户研究中的一个主要发现是,在临床病历审阅过程中,需要一种排序方法来更好地支持所谓的 "提前停止 "审阅策略(即只审阅一个病人的 EMR 子集以做出最终决定)。作者提出了两个以用户为中心的新排序指标:"关键文档 "和 "负保证比率",以更好地衡量排序方法在支持临床病历审阅过程中的 "早期停止 "要求方面的能力。结果评估结果表明:i) 传统的信息检索指标,如精确度-at-K,在指导设计和开发 EMR 搜索引擎以更好地支持临床病历审查方面存在局限性;ii) 没有一种适合不同病历审查和不同用户需求的全局最优排序方法;iii) 学习排序方法不能保证为不同病历审查和不同用户提供稳定的最优排序;iv) 以用户为中心的排序指标,如负保证率(NGR)指标,能够衡量排序方法的 "提前停止 "性能。结论以用户为中心的排序指标可以更好地衡量排序方法在支持临床病历审查方面的能力。未来的研究应该探索更多的以用户为中心的排名指标,并评估它们对现实世界中 EMR 搜索引擎的影响。
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
Next generation of electronic medical record search engines to support chart reviews: A systematic user study and future research direction

Objective

Little research has been done on the user-centered document ranking approach, especially in a crowdsourcing chart review environment. As the starting point of designing and implementing the next generation of Electronic Medical Record (EMR) search engines, a systematic user study is needed to better understand the users' needs, challenges, and future research directions of EMR search engines.

Materials and methods

One primary observation during the user study is the need for a ranking method to better support the so-called "early stopping" reviewing strategy (i.e., reviewing only a subset of EMRs of one patient to make the final decision) during the clinical chart reviews. The authors proposed two novel user-centered ranking metrics: "critical documents" and "negative guarantee ratio," to better measure the power of a ranking method in supporting the “early stopping” requirements during clinical chart reviews.

Results

The evaluation results show that i) traditional information retrieval metrics, such as the precision-at-K, have limitations in guiding the design and development of EMR search engines to better support clinical chart reviews; ii) there is not a global optimal ranking method that fits the needs of different chart reviews and different users; iii) a learning-to-rank approach cannot guarantee a stable and optimal ranking for different chart reviews and different users; and iv) A user-centered ranking metric, such as the negative guarantee ratio (NGR) metric is able to measure the “early-stopping” performance of ranking methods.

Conclusions

User-centered ranking metrics can better measure the power of ranking methods in supporting clinical chart reviews. Future research should explore more user-centered ranking metrics and evaluate their impact on real-world EMR search engines.

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
自引率
0.00%
发文量
0
期刊最新文献
Creative destruction and artificial intelligence: The transformation of industries during the sixth wave Leveraging the digital sustainable growth model (DSGM) to drive economic growth: Transforming innovation uncertainty into scalable technology Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability LLM technologies and information search Women's economic empowerment and COVID-19 pandemic: A study on women entrepreneurs in Bangladesh
×
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