{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:35:32Z","timestamp":1740202532213,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:p>Background: Crowding in emergency departments (ED) has a negative impact on quality of care and can be averted by allocating additional resources based on predictive crowding models. However, there is a lack in effective external overall predictors, particularly those representing public activity.<\/jats:p>","DOI":"10.3233\/978-1-61499-971-3-57","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T15:26:30Z","timestamp":1740151590000},"source":"Crossref","is-referenced-by-count":0,"title":["Improving the Prediction of Emergency Department Crowding: A Time Series Analysis Including Road Traffic Flow"],"prefix":"10.3233","author":[{"family":"Rauch Jens","sequence":"additional","affiliation":[]},{"family":"H&uuml;bner Ursula","sequence":"additional","affiliation":[]},{"family":"Denter Mathias","sequence":"additional","affiliation":[]},{"family":"Babitsch Birgit","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","dHealth 2019 &amp;ndash; From eHealth to dHealth"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:14:23Z","timestamp":1740154463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-970-6&spage=57&doi=10.3233\/978-1-61499-971-3-57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-971-3-57","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2019]]}}}