@article{
 title = {Waiting-time estimation in walk-in clinics},
 type = {article},
 year = {2016},
 identifiers = {[object Object]},
 keywords = {[Applied probability, Forecasting, Health services},
 id = {48c4f8b5-64ef-32ec-b30b-1ce1c4a1d41b},
 created = {2017-01-30T23:35:09.000Z},
 file_attached = {false},
 profile_id = {9696f304-db07-31d9-a8cd-d208939f9889},
 last_modified = {2017-03-15T19:51:05.557Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Montecinos2016},
 private_publication = {false},
 abstract = {© 2016 The Authors.Medical assistance is offered by walk-in clinics (WC). These clinics must keep track of patients' turn in line. Some private companies offer an extra follow-up service to WC patients, which notifies them when their consultation approaches, so patients can use their free time elsewhere than in the waiting room. This paper aims to develop an applied forecasting approach for consultation service time estimation and waiting-time estimation. A model based on particle filters and mixture models helps to estimate the waiting time for each consultation, using historical and new incoming data from patient consultations. The system considers two types of patients, namely, regular and follow-up. Our method gives an estimate of the waiting time for consultation better than simple statistics.},
 bibtype = {article},
 author = {Montecinos, J. and Ouhimmou, M. and Chauhan, S.},
 journal = {International Transactions in Operational Research}
}

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