Applied Clinical Informatics, Table of Contents Appl Clin Inform 2016; 07(02): 275-289DOI: 10.4338/ACI-2015-09-RA-0127 Research Article Schattauer GmbH Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit Muge Capan 1 Christiana Care Health System, Value Institute, Newark, DE , Stephen Hoover 1 Christiana Care Health System, Value Institute, Newark, DE , Eric V. Jackson 1 Christiana Care Health System, Value Institute, Newark, DE , David Paul 2 Christiana Care Health System, Division of Neonatology, Newark, DE , Robert Locke 2 Christiana Care Health System, Division of Neonatology, Newark, DE › Author Affiliations Recommend Article Abstract Full Text PDF Download Keywords KeywordsForecasting - Neonatal Intensive Care Units - time series analysis References References 1 Rogowski JA, Staiger D, Patrick T, Horbar J, Kenny M, Lake ET. Nurse Staffing and NICU Infection Rates. JAMA Pediatr 2013; 167 (05) 444-450. 2 Cho SH, Ketefian S, Barkauskas VH, Smith DG. The Effects of Nurse Staffing on Adverse Events, Morbidity, Mortality and Medical Costs. Nurs Res 2003; 52 (02) 71-79. 3 Penoyer DA. Nurse staffing and patient outcomes in critical care: A concise review. Crit Care Med 2010; 38 (07) 1521-1528. 4 Cescutti-Butler L, Galvin K. Parents’ perceptions of staff competency in a neonatal intensive care unit. J Clin Nurs 2003; 12: 752-761. 5 Braithwaite M. Nurse Burnout and Stress in the NICU. Adv in Neonatal Care 2008; 08 (06) 343-347. 6 Scheward L, Hunt J, Hagen S, Macleod M, Ball J. The relationship between UK hospital nurse staffing and emotional exhaustion and job dissatisfaction. J Nurs Manag 2005; 13: 51-60. 7 Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH. Hospital Nurse Staffing and Patient Mortality, Nurse Burnout, and Job Dissatisfaction. JAMA 2002; 288 (16) 1987-1993. 8 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). 9 American Academy of Pediatrics. Levels of Neonatal Care. Pediatrics. 2004 114. 05 Available from: http://dx.doi.org/10.1542/peds.2004-1697 10 Hamilton KE, Redshaw ME, Tarnow-Mordi W. Nurse staffing in relation to risk-adjusted mortality in neonatal care. Arch Dis Child Fetal Neonatal Ed 2007; 92: F99-F103. 11 Kilbride HW, Powers R, Wirtschafter DD, Sheehan MB, Charsha DS, LaCorte M, Finer N, Goldmann DA. Evaluation and development of potentially better practices to prevent neonatal nosocomial bacteremia. Pediatrics 2003; 111: e504-e518. 12 Reis BY, Mandl KD. Time Series Modeling for syndromic surveillance. BMC Med Inform Decis Mak. 2003 03. (2): Available from: http://doi.org/10.1186/1472-6947-3-2. 13 Brillman JC, Burr T, Forslund D, Joyce E, Picard R, Umland E. Modeling emergency department visit patterns for infectious disease complaints: Results and application to disease surveillance. BMC Med Inform Decis Mak 2005; 05 (01) 4. 14 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.: 15 Jones SS, Thomas A, Evans RS, Welch SJ, Haug PJ, Snow GL. Forecasting Daily Patient Volumes in the Emergency Department. Acad Emerg Med 2008; 15 (02) 159-170. 16 Kam H, Sung J, Par R. Prediction of Daily Patient Numbers for a Regional Emergency Medical Center Using Time Series Analysis. Healthc Inform Res 2010; 16 (03) 158-165. 17 Schweigler LM, Desmond JS, McCarthy ML, Bukowski KJ, Ionides EL, Younger JG. Forecasting Models of Emergency Department Crowding. Acad Emerg Med 2009; 16: 1-8. 18 Champion R, Kinsman LD, Lee GA, Masman KA, May EA, Mills TM, Taylor MD, Thomas PR, Williams RJ. Forecasting emergency department presentations. Aust Health Rev 2007; 31 (01) 83-90. 19 Tandberg D, Qualls C. Time Series Forecasts of Emergency Department Patient Volume, Length of Stay and Acuity. Ann Emerg Med 1994; 23 (02) 299-305. 20 Yu Y, Lin H, Cheng B. The application of Prediction Modeling of the Optimal Emergency Nurse Scheduling. Advances in Information Sciences and Service Sciences 2013; 05 (12) 38-45. 21 Marcilio I, Hajat S, Gouveia N. Forecasting Daily Emergent Department Visits using Calendar Variables and Ambient Temperature Readings. Acad Emerg Med 2013; 20 (08) 179-177. 22 Sun Y, Heng BH, Seow YT, Seow E. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009 09. (1): Available from: http://doi.org/10.1186/1471-227X-9-1 23 Temple MW, Lehmann CU, Fabbri D. Predicting Discharge Dates From the NICU Using Progress Note Data. Pediatrics 2015; 136 (02) e395-e405. 24 Levin SR, Harley ET, Fackler JC, Lehmann CU, Custer JW, France D, Zeger SL. Real-time forecasting of pediatric intensive care unit length of stay using computerized provider orders. Crit Care Med 2012; 40 (11) 3058-3064. 25 Koestler DC, Ombao H, Bender J. Ensemble-based methods for forecasting census in hospital units. BMC Med Res Methodol 2013; 13: 67. 26 Shumway R, Stoffer D. Time Series Analysis and Its Applications: With R Examples. 3rd ed.. New York: Springer Texts in Statistics; 2011 27 Lewis CD. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. London: Butterworth Scientific; 1982 28 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: 379-399
