Emergency Department Online Patient-Caregiver Scheduling
release_kaz3sdhf5rf2npxy44fifhl2gu
by
Hanan Rosemarin, Ariel Rosenfeld, Sarit Kraus
2019 Volume 33, p695-701
Abstract
Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED's objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospital-specific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED's performance metrics.
In application/xml+jats
format
Archived Files and Locations
application/pdf 317.3 kB
file_w2ha2dkpyna3hnzts5q4sfpcoq
|
aaai.org (web) web.archive.org (webarchive) |
article-journal
Stage
published
Date 2019-07-21
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar