Appl Clin Inform 2016; 07(02): 275-289
DOI: 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
Further Information

Publication History

received: 29 September 2015

accepted: 14 February 2016

Publication Date:
16 December 2017 (online)

Summary

Background

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.

Objective

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.

Methods

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.

Results

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.

Conclusions

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.