Medical Inter-Specialty Referral Networks

Tatiana Ermakova, Benjamin Fabian, M. Erhart, T. Czihal, D. Stillfried
{"title":"Medical Inter-Specialty Referral Networks","authors":"Tatiana Ermakova, Benjamin Fabian, M. Erhart, T. Czihal, D. Stillfried","doi":"10.1145/3498851.3498930","DOIUrl":null,"url":null,"abstract":"Motivated by their increasing popularity and usefulness in the English-speaking world, we model and analyze patient referral networks for medical specialties based on Germany-wide claims data from the fourth quarter of 2015. Based on the average values of local graph measures, different groups of medical specialties could be distinguished. Family physicians have almost perfect average out-degree and closeness centrality. Based on the principal components applied to local graph measures, four clusters of local medical specialties could be identified, that are characteristic for (1) orthopedists, surgeons, otolaryngologists, dermatologists and internists; (2) non-medical psychotherapists, pediatricians, gynecologists, and ophthalmologists; (3) only family physicians in Bremen and internists in Hamburg and Saarland; (4) the remaining family physicians. This study can serve as a basis for further network simulations and monitoring to achieve the desired health care outcomes, optimal resource allocation and protection against infections.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"53 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3498851.3498930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Motivated by their increasing popularity and usefulness in the English-speaking world, we model and analyze patient referral networks for medical specialties based on Germany-wide claims data from the fourth quarter of 2015. Based on the average values of local graph measures, different groups of medical specialties could be distinguished. Family physicians have almost perfect average out-degree and closeness centrality. Based on the principal components applied to local graph measures, four clusters of local medical specialties could be identified, that are characteristic for (1) orthopedists, surgeons, otolaryngologists, dermatologists and internists; (2) non-medical psychotherapists, pediatricians, gynecologists, and ophthalmologists; (3) only family physicians in Bremen and internists in Hamburg and Saarland; (4) the remaining family physicians. This study can serve as a basis for further network simulations and monitoring to achieve the desired health care outcomes, optimal resource allocation and protection against infections.
医学专科间转诊网络
由于它们在英语世界越来越受欢迎和有用,我们基于2015年第四季度德国范围内的索赔数据,建模并分析了医学专业的患者转诊网络。根据局部图测度的平均值,可以区分不同的医学专科组。家庭医生的平均外向度和亲近度中心性接近完美。基于应用于局部图测度的主成分,可以识别出4个地方医学专科集群,它们的特征为:(1)骨科、外科、耳鼻喉科、皮肤科和内科;(2)非医学心理治疗师、儿科医生、妇科医生、眼科医生;(3)只有不来梅的家庭医生和汉堡和萨尔州的内科医生;(4)剩余的家庭医生。该研究可作为进一步网络模拟和监测的基础,以实现理想的卫生保健结果,优化资源分配和预防感染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信