Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit
| Autor: | David A. Paul, Robert Locke, Stephen Hoover, Eric V. Jackson, Muge Capan |
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| Rok vydání: | 2016 |
| Předmět: |
Male
Time Factors Neonatal intensive care unit Resource planning Health Informatics 03 medical and health sciences Patient safety 0302 clinical medicine Health Information Management Intensive Care Units Neonatal 030225 pediatrics Health care Linear regression Statistics Humans Medicine Autoregressive integrated moving average Time series business.industry Infant Newborn Censuses 030208 emergency & critical care medicine Census Computer Science Applications Female business Forecasting Research Article |
| Zdroj: | Applied Clinical Informatics. :275-289 |
| ISSN: | 1869-0327 |
| DOI: | 10.4338/aci-2015-09-ra-0127 |
| Popis: | SummaryAccurate 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.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.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.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)×(1,1,2)7 and ARIMA(2,1,4)×(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.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. |
| Databáze: | OpenAIRE |
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