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.