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Jeong and Kim: A Critical Review of Nurse Demand Forecasting Methods in Empirical Studies 1991~2014
Original Article

Perspectives in Nursing Science 2016;13(2):81-87.

Published online: 19 October 2016

DOI: https://doi.org/10.16952/pns.2016.13.2.81

A Critical Review of Nurse Demand Forecasting Methods in Empirical Studies 1991~2014

Suyong Jeong1, Jinhyun Kim2,

1Graduate Student, College of Nursing, Seoul National University, Seoul

2Professor, College of Nursing, Seoul National University, Seoul, Korea

Corresponding author: Kim, Jinhyun College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. Tel: +82-2-740-8818, Fax: +82-2-766-1852, E-mail: jinhyun@snu.ac.kr

Received 11 August 2016       Revised 30 August 2016       Accepted 30 August 2016

Copyright © 2016 The Research Institute of Nursing Science, Seoul National University

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose:

The aim of this study is to review the nurse demand forecasting methods in empirical studies published during 1991~2014 and suggest ideas to improve the validity in nurse demand forecasting. Methods: Previous studies on nurse demand forecasting methodology were categorized into four groups: time series analysis, top-down approach of workforce requirement, bottom-up approach of workforce requirement, and labor market analysis. Major methodological properties of each group were summarized and compared. Results: Time series analysis and top-down approach were the most frequently used forecasting methodologies. Conclusion: To improve decision-making in nursing workforce planning, stakeholders should consider a variety of demand forecasting methods and appraise the validity of forecasting nurse demand.

Keywords: Nurse demand, Nursing workforce, Nurse demand forecasting, Demand for nurse

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Table 1.
Methodological Characteristics in Nurse Demand Forecasting Studies
Researcher Forecast horizon∗ Historic data† Demand forecasting model
Approach Model specification Nurse productivity‡ Working days§
Kim et al. (1991) 1995~2010 1984~1990 Top-down Time series Bottom-up Linear regression (log) Outpatient 30 Law 265
(=Inpatient 2.5) Current||  
Hospital 4.6 PCS  
Clinic 31.9    
Hospital 1.8    
Clinic 2.6    
Park et al. (1993) 1995~2010 1984~1991 Top-down Time series Bottom-up Linear regression (log) Outpatient 30 (=Inpatient 2.5) Law Current|| 265
Outpatient 60 PCS  
(=Inpatient 5.0)    
Outpatient 40    
(=Inpatient 1.5)    
Kim et al. (1999) 2002~2012 1990~1997 Top-down Time series Linear regression (square root) Outpatient 30 Law 255
(=Inpatient 2.5) Current|| 265
Outpatient 45    
(=Inpatient 4.0)    
Park et al. (2001) 2000~2015 1990~1997 Top-down Time series Bottom-up Linear regression (square root) Outpatient 30 Law || 265
(=Inpatient 2.5) Current||  
Outpatient 60 PCS  
(=Inpatient 5.0)    
Outpatient 40    
(=Inpatient 1.5)    
Park et al. (2002) 2005~2020 1989~1999 Top-down Time series Bottom-up Linear regression (square root) Outpatient 30 Law 265
(=Inpatient 2.5) Current||  
Outpatient 60 PCS  
(=Inpatient 5.0)    
Outpatient 40    
(=Inpatient 1.5)    
Jo et al. (2005) 2008~2018 1990~2003 Top-down Time series Curve estimation (logistic) Outpatient 30 (=Inpatient 2.5) Law 255
Current|| 265
Outpatient 45 (=Inpatient 4.0)    
   
Oh (2008) 2010~2020 2001~2004 Top-down Time series Average growth rate Outpatient 30 (=Inpatient 2.5) Law 255
Current|| 265
Outpatient 45 (=Inpatient 4.0)    
   
Oh (2010) 2010~2025 2003~2007 Top-down Time series Curve estimation (logit) ARIMA Outpatient 30 (=Inpatient 2.5) Law 255
Current|| 265
Outpatient 52.78 (80~120%)    
   
Oh (2014) 2010~2025 2003~2013 Top-down Time series Average growth rate Curve estimation (logistic) Curve estimation (logarithm) ARIMA Outpatient 30 (=Inpatient 2.5) Law Current|| 255 265
Outpatient 55.8    
(80~120%)    

∗ During the years that researcher forecasted nurse demand estimates;

† During the years of historic data that researcher used for forecasting;

‡ Scenario researcher considered as a nurse productivity, which means patient volumes covered by a nurse per day;

§ Days that a nurse works in a year;

|| Current workload indicators based on researcher's assumption; PCS=Patient classification system.

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