{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:09:45Z","timestamp":1772122185964,"version":"3.50.1"},"reference-count":28,"publisher":"Georg Thieme Verlag KG","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2016,4]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations.<\/jats:p><jats:p>Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach.<\/jats:p><jats:p>We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics.<\/jats:p><jats:p>The census showed a slightly increasing linear trend. Best fitting models included a nonseasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)\u00d7(1,1,2)7 and ARIMA(2,1,4)\u00d7(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach.<\/jats:p><jats:p>Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support shortand long-term census forecasting, and inform staff resource planning.<\/jats:p>","DOI":"10.4338\/aci-2015-09-ra-0127","type":"journal-article","created":{"date-parts":[[2016,5,4]],"date-time":"2016-05-04T08:35:41Z","timestamp":1462350941000},"page":"275-289","source":"Crossref","is-referenced-by-count":26,"title":["Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit"],"prefix":"10.4338","volume":"07","author":[{"given":"Stephen","family":"Hoover","sequence":"first","affiliation":[]},{"given":"Eric","family":"Jackson","sequence":"first","affiliation":[]},{"given":"David","family":"Paul","sequence":"first","affiliation":[]},{"given":"Robert","family":"Locke","sequence":"first","affiliation":[]},{"given":"Muge","family":"Capan","sequence":"additional","affiliation":[]}],"member":"194","published-online":{"date-parts":[[2017,12,16]]},"reference":[{"key":"10.4338\/ACI-2015-09-RA-0127-1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapediatrics.2013.18"},{"key":"10.4338\/ACI-2015-09-RA-0127-2","doi-asserted-by":"publisher","DOI":"10.1097\/00006199-200303000-00003"},{"key":"10.4338\/ACI-2015-09-RA-0127-3","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e3181e47888"},{"key":"10.4338\/ACI-2015-09-RA-0127-4","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-2702.2003.00783.x"},{"key":"10.4338\/ACI-2015-09-RA-0127-5","doi-asserted-by":"publisher","DOI":"10.1097\/01.ANC.0000342767.17606.d1"},{"key":"10.4338\/ACI-2015-09-RA-0127-6","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2834.2004.00460.x"},{"key":"10.4338\/ACI-2015-09-RA-0127-7","doi-asserted-by":"publisher","DOI":"10.1001\/jama.288.16.1987"},{"key":"10.4338\/ACI-2015-09-RA-0127-8","unstructured":"Neonatal Intensive Care, A History of Excellence, NIH Publication No. 92-2786 1992. Available from: http:\/\/www.neonatology.org\/classics\/nic.nih1985.pdf (accessed September 28, 2015)"},{"key":"10.4338\/ACI-2015-09-RA-0127-9","doi-asserted-by":"crossref","unstructured":"American Academy of Pediatrics. Levels of Neonatal Care. Pediatrics 2004; 114(5). Available from: http:\/\/dx.doi.org\/10.1542\/peds.2004-1697","DOI":"10.1542\/peds.2004-1697"},{"key":"10.4338\/ACI-2015-09-RA-0127-10","doi-asserted-by":"publisher","DOI":"10.1136\/adc.2006.102988"},{"key":"10.4338\/ACI-2015-09-RA-0127-11","doi-asserted-by":"crossref","first-page":"e504","DOI":"10.1542\/peds.111.SE1.e504","volume":"111","author":"Kilbride","year":"2003","journal-title":"Pediatrics"},{"key":"10.4338\/ACI-2015-09-RA-0127-12","doi-asserted-by":"crossref","unstructured":"Reis BY, Mandl KD. Time Series Modeling for syndromic surveillance. BMC Med Inform Decis Mak 2003; 3(2). Available from: http:\/\/doi.org\/10.1186\/1472-6947-3-2","DOI":"10.1186\/1472-6947-3-2"},{"key":"10.4338\/ACI-2015-09-RA-0127-13","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-5-4"},{"key":"10.4338\/ACI-2015-09-RA-0127-14","doi-asserted-by":"crossref","unstructured":"Guo H, Tang J, Qu G. Historical Data Driven Nurse Flexible Scheduling Problem. 2013 25th Chinese Control and Decision Conference (CCDC). 25-27 May 2013, Guiyang City Guiyang, China","DOI":"10.1109\/CCDC.2013.6561121"},{"key":"10.4338\/ACI-2015-09-RA-0127-15","doi-asserted-by":"publisher","DOI":"10.1111\/j.1553-2712.2007.00032.x"},{"key":"10.4338\/ACI-2015-09-RA-0127-16","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2010.16.3.158"},{"key":"10.4338\/ACI-2015-09-RA-0127-17","doi-asserted-by":"publisher","DOI":"10.1111\/j.1553-2712.2009.00356.x"},{"key":"10.4338\/ACI-2015-09-RA-0127-18","doi-asserted-by":"publisher","DOI":"10.1071\/AH070083"},{"key":"10.4338\/ACI-2015-09-RA-0127-19","doi-asserted-by":"crossref","unstructured":"Tandberg D, Qualls C. Time Series Forecasts of Emergency Department Patient Volume, Length of Stay and Acuity. Ann Emerg Med 1994: 23(2): 299-305","DOI":"10.1016\/S0196-0644(94)70044-3"},{"issue":"12","key":"10.4338\/ACI-2015-09-RA-0127-20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.4156\/aiss.vol5.issue12.5","volume":"5","author":"Yu","year":"2013","journal-title":"Advances in Information Sciences and Service Sciences"},{"key":"10.4338\/ACI-2015-09-RA-0127-21","doi-asserted-by":"publisher","DOI":"10.1111\/acem.12182"},{"key":"10.4338\/ACI-2015-09-RA-0127-22","doi-asserted-by":"crossref","unstructured":"Sun Y, Heng BH, Seow YT, Seow, E. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med 2009; 9(1). Available from: http:\/\/doi.org\/10.1186\/1471-227X-9-1","DOI":"10.1186\/1471-227X-9-1"},{"key":"10.4338\/ACI-2015-09-RA-0127-23","doi-asserted-by":"crossref","unstructured":"Temple MW, Lehmann CU, Fabbri D. Predicting Discharge Dates From the NICU Using Progress Note Data. Pediatrics 2015: 136(2): e395-e405","DOI":"10.1542\/peds.2015-0456"},{"key":"10.4338\/ACI-2015-09-RA-0127-24","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e31825bc399"},{"key":"10.4338\/ACI-2015-09-RA-0127-25","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-13-67"},{"key":"10.4338\/ACI-2015-09-RA-0127-26","doi-asserted-by":"crossref","unstructured":"Shumway R, Stoffer D. Time Series Analysis and Its Applications: With R Examples. 3rd ed. New York: Springer Texts in Statistics; 2011","DOI":"10.1007\/978-1-4419-7865-3"},{"key":"10.4338\/ACI-2015-09-RA-0127-27","unstructured":"Lewis CD. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. London: Butterworth Scientific; 1982"},{"key":"10.4338\/ACI-2015-09-RA-0127-28","doi-asserted-by":"crossref","unstructured":"Ellison PT, Valeggia CR, Sherry DS. Human birth seasonality. In: Brockman DK, van Schaik CP, editors. Seasonality in Primates: Studies of Living and Extinct Human and Non-Human Primates. Cambridge University Press; 2005. p. 379-399","DOI":"10.1017\/CBO9780511542343.014"}],"container-title":["Applied Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.4338\/ACI-2015-09-RA-0127.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T19:48:51Z","timestamp":1655668131000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.4338\/ACI-2015-09-RA-0127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4]]},"references-count":28,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2017,12,16]]},"published-print":{"date-parts":[[2016]]}},"URL":"https:\/\/doi.org\/10.4338\/aci-2015-09-ra-0127","relation":{},"ISSN":["1869-0327"],"issn-type":[{"value":"1869-0327","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4]]}}}