@inproceedings{DBLP:conf/mod/AringhieriDDS17,
  author    = {Roberto Aringhieri and
               Davide Dell'Anna and
               Davide Duma and
               Michele Sonnessa},
  title     = {Evaluating the Dispatching Policies for a Regional Network of Emergency
               Departments Exploiting Health Care Big Data},
  booktitle = {Proceedings of the Third International
               Conference on Machine Learning, Optimization, and Big Data, {MOD} 2017, Revised
               Selected Papers},
  volume    = {10710},
  pages     = {549--561},
  year      = {2017},
  url_Link       = {https://doi.org/10.1007/978-3-319-72926-8_46},
  url_Paper       = {2017_MOD/MOD17_Aringhieri.pdf},
  url_Slides= {2017_MOD/MOD17_Aringhieri_Slides.pdf},
  url_Poster = {https://iris.unito.it/bitstream/2318/1624657/1/AringhieriEtAl-poster.pdf},
  keywords  = {Big Data, Health Care, Online Optimization, Big Data Health Care},
  doi       = {10.1007/978-3-319-72926-8\_46},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/mod/AringhieriDDS17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  abstract = {The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time, ...) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the regional network of emergency departments: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. We present a simulation model based on the case study of the Piedmont in Italy. The model is powered by the knowledge provided by the analysis of the regional health care big data.}
}

{"_id":"iPkv24T5kD5iEiSFj","bibbaseid":"aringhieri-dellanna-duma-sonnessa-evaluatingthedispatchingpoliciesforaregionalnetworkofemergencydepartmentsexploitinghealthcarebigdata-2017","authorIDs":["btfox5LNCYjybS6FG"],"author_short":["Aringhieri, R.","Dell'Anna, D.","Duma, D.","Sonnessa, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Roberto"],"propositions":[],"lastnames":["Aringhieri"],"suffixes":[]},{"firstnames":["Davide"],"propositions":[],"lastnames":["Dell'Anna"],"suffixes":[]},{"firstnames":["Davide"],"propositions":[],"lastnames":["Duma"],"suffixes":[]},{"firstnames":["Michele"],"propositions":[],"lastnames":["Sonnessa"],"suffixes":[]}],"title":"Evaluating the Dispatching Policies for a Regional Network of Emergency Departments Exploiting Health Care Big Data","booktitle":"Proceedings of the Third International Conference on Machine Learning, Optimization, and Big Data, MOD 2017, Revised Selected Papers","volume":"10710","pages":"549–561","year":"2017","url_link":"https://doi.org/10.1007/978-3-319-72926-8_46","url_paper":"2017_MOD/MOD17_Aringhieri.pdf","url_slides":"2017_MOD/MOD17_Aringhieri_Slides.pdf","url_poster":"https://iris.unito.it/bitstream/2318/1624657/1/AringhieriEtAl-poster.pdf","keywords":"Big Data, Health Care, Online Optimization, Big Data Health Care","doi":"10.1007/978-3-319-72926-8_46","timestamp":"Tue, 29 Dec 2020 00:00:00 +0100","biburl":"https://dblp.org/rec/conf/mod/AringhieriDDS17.bib","bibsource":"dblp computer science bibliography, https://dblp.org","abstract":"The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time, ...) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the regional network of emergency departments: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. We present a simulation model based on the case study of the Piedmont in Italy. The model is powered by the knowledge provided by the analysis of the regional health care big data.","bibtex":"@inproceedings{DBLP:conf/mod/AringhieriDDS17,\n author = {Roberto Aringhieri and\n Davide Dell'Anna and\n Davide Duma and\n Michele Sonnessa},\n title = {Evaluating the Dispatching Policies for a Regional Network of Emergency\n Departments Exploiting Health Care Big Data},\n booktitle = {Proceedings of the Third International\n Conference on Machine Learning, Optimization, and Big Data, {MOD} 2017, Revised\n Selected Papers},\n volume = {10710},\n pages = {549--561},\n year = {2017},\n url_Link = {https://doi.org/10.1007/978-3-319-72926-8_46},\n url_Paper = {2017_MOD/MOD17_Aringhieri.pdf},\n url_Slides= {2017_MOD/MOD17_Aringhieri_Slides.pdf},\n url_Poster = {https://iris.unito.it/bitstream/2318/1624657/1/AringhieriEtAl-poster.pdf},\n keywords = {Big Data, Health Care, Online Optimization, Big Data Health Care},\n doi = {10.1007/978-3-319-72926-8\\_46},\n timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},\n biburl = {https://dblp.org/rec/conf/mod/AringhieriDDS17.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n abstract = {The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time, ...) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the regional network of emergency departments: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. We present a simulation model based on the case study of the Piedmont in Italy. The model is powered by the knowledge provided by the analysis of the regional health care big data.}\n}\n\n\n\n","author_short":["Aringhieri, R.","Dell'Anna, D.","Duma, D.","Sonnessa, M."],"key":"DBLP:conf/mod/AringhieriDDS17","id":"DBLP:conf/mod/AringhieriDDS17","bibbaseid":"aringhieri-dellanna-duma-sonnessa-evaluatingthedispatchingpoliciesforaregionalnetworkofemergencydepartmentsexploitinghealthcarebigdata-2017","role":"author","urls":{" link":"https://doi.org/10.1007/978-3-319-72926-8_46"," paper":"http://davidedellanna.com/publications/2017_MOD/MOD17_Aringhieri.pdf"," slides":"http://davidedellanna.com/publications/2017_MOD/MOD17_Aringhieri_Slides.pdf"," poster":"https://iris.unito.it/bitstream/2318/1624657/1/AringhieriEtAl-poster.pdf"},"keyword":["Big Data","Health Care","Online Optimization","Big Data Health Care"],"metadata":{"authorlinks":{"dell'anna, d":"https://bibbase.org/show?bib=https://dblp.org/pid/201/0398.bib"}},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"http://davidedellanna.com/publications/dellanna.bib","creationDate":"2021-02-08T09:40:40.447Z","downloads":0,"keywords":["big data","health care","online optimization","big data health care"],"search_terms":["evaluating","dispatching","policies","regional","network","emergency","departments","exploiting","health","care","big","data","aringhieri","dell'anna","duma","sonnessa"],"title":"Evaluating the Dispatching Policies for a Regional Network of Emergency Departments Exploiting Health Care Big Data","year":2017,"dataSources":["Rymgh7KJx3EhNfvG8","6mqXRucaPuwqWyQEd","2pnuieZznE6L96taK"]}