Poisson Regression models to reduce Waiting Time for Hospital Service. Case Study: Nakhonpathom Hospital Thailand.
The growing population has led to increase in waiting time and overcrowding in the hospital service, a Poisson regression model has been developed to analyze the time series of count data. In finding a Poisson regression model, parameters are estimated and goodness-of-fit is utilized to carefully extract the best model to fit the count data. The marginal effect is the basis function which can be used in the Poisson regression model. This study attempted to analyze actual operations of a hospital and proposed modifications in the system to reduce waiting times for the patients, which should lead to an improved view of the quality of service provided. To develop a Poisson regression analysis model for the above situation, we need to define a model for the expected number of patients for hospital services cases. Here, two underlying variables are of interest, “waiting time” and “hospital services”. Since “waiting time” have been categorized seven groups. The variable “hospital services” which contains four categorizes (No welfare (NW), Reimbursement to employer (RE), Social Security Service (SS) and 30 baht for welfare health service (Gold cards (30W)). As a result, significant levels of causal variables are not expected to be identical for each model. We find that 30 baht for welfare health service (Gold cards (30W)) category has a higher rate of increase in the average waiting time. The marginal effect is a basis function that can be used in the Poisson regression. It allows into arrive at better predictions of hospital service and rehabilitation decision making.
Broyles, J.R. and Cochran, J.K. (2007) Estimating business loss to a hospital emergency department from patient reneging by queuing-based regression, in Proceedings of the 2007 Industial Engineering Research Conference, 613-618.
Bruin, A.M., Koole, G.M. and Visser, M.C.(2005). Bottleneck analysis of emergency cardiac in-patient flow in a university setting: an application of queueing theory. Clinical and Investigative Medicine, 28, 316-317.
Bruin, A.M., Rossum, A.C., Visser, M.C. and Koole, G.M.(2007). Modeling the emergency cardiac in-patient flow: an application of queuing theory. Health Care Management Science, 10, 125-137.
Chin-Shang Li; Wanzhu Tu; (2005), A semiparametric Poisson regression model for the analysis of health
care utilization data, The National Institutes of Health and by the American Lebanese Syrian
Associated Charities, HD042404 (W. Tu) and CA21765 (C.S. Li)
Eldabi, T. and Paul, R.J., (2001), “A Proposed Approach for Modeling Healthcare Systems for
Understanding”, Proceedings of the 2001 Winter Simulation Conference, Institute of Electrical and
Electronics Engineers, Arlington, Virginia, USA, 9-12 December, 1412-1420;
Fiems, D., Koole, G. and Nain, P. (2007) Waiting times of scheduled patients in the presence of emergency
requests. http://www.math.vu.nl/~koole/articles/report05a/art.pdf, accessed August 6, 2007.
Giles, D. E. A. (2007), Modeling inflated count data. In L. Oxley, and D. Kulasiri, (eds.), MODSIM 2007
International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia
and New Zealand, Christchurch, N.Z., 919-925.
Giles, D. E. A. (2010), Hermite regression analysis of multi-modal count data, mimeo., Department of
Economics, University of Victoria.
Green, L.V. and Nguyen,V. (2001). Strategies for cutting hospital beds: the impact on patient service.
Health Services, pp.421-442.
Harper, R.R.,(2002). “A Framework for Operational Modeling of Hospital Resources”, Health Care
Management Science, 5: 165-173;
Herbert C. Heien, and William A. Baumann (2000).; Inference in Log-Rate Models.
Department of Mathematics and Statistics Minnesota State University,Mankato, USA, MN 56002.
Heien, H.C., Baumann, W.A., and Rahman, M. (2004). Inference in Log-Rate Models.
Journal of Undergraduate Research.2004; Available from:
Ivo Adan and Jacques Resing (2001), Queueing Theory, Department of Mathematics and Computing
Science Eindhoven University of Technology, The Netherlands.
Jacobson, S.H., Hall, S.N. and Swisher, J.R. (2006), “Discrete-event simulation of health care systems, in
Patient Flow: Reducing Delay in Healthcare Delivery”, Hall, R.W. Springer, New York, 211-252.
Jiahe Qian, (2003), Application of Logistic Regression in Analysis of e-rater Data Education Testing
Service, ETS MS 02-T, Princeton, NJ 08541
Koizumi N., Kuno, E.and Smith, T.E. (2005) Modeling patient flows using a queuing network with
blocking. Health Care Management Science, 8, 49-60.
McManus, M.L., Long, M.C., Cooper, A. and Litvak, E. (2004) Queuing theory accurately models the need
for critical care resources. Anesthesiology, 100, 1271-1276.
Megu Ohtaki, (2004), Extended Poisson Regression Model for Analyzing Ordered Categorical Response
Data, Department of Environmetrics and Biometrics, Hiroshima University, Japan,
sumitted to Biometrics. E-mail: email@example.com
Michael Kohler and Adam Krzyzak, (2005), Asymtotic confidence intervals for Poisson regression analysis,
Department of Economics Syracuse, NY 13244, (315) 443-3233
Preater, J. (2002). Queues in health. Health Care Management Science, 5, 283.
Rahman, Mezbuhar, Cortes, Judy and Pardis, Cyrus (2001), A Note on Logistic Regression, Journal of
Statistics & Management Systems, Vol. 4, No.2 p 175-187.
Robert Green (2001), Using Queueing Theory to Increase the Effectiveness of ED Provider. Academic
Emergency Medicine, 2001. 8: p. 151-155.
Roche, K.T., Cochran, J.K. and Fulton, I.A. (2007) Improving patient safety by maximizing fast-track
benefits in the emergency department – a queuing network approach, in Proceedings of the 2007
Industrial Engineering Research Conference, pp 619-624.
Sidorenko, Alexandra (2001): Stochastic Model of Demand for Medical Care with Endogenous Labor
Supply and Health Insurance, publishcation by Australian Nationnal University, Economics RSPAS
Tuija Lindqvist. (2001). Quality Management in Social Welfare and Health care for; National
Recommendation. Gabor Horvath; Approximate WaitingTime Analysis of Priority Queues,
Copyright (c) 2019 International Journal For Research In Mathematics And Statistics (ISSN: 2208-2662)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
In consideration of the journal, Green Publication taking action in reviewing and editing our manuscript, the authors undersigned hereby transfer, assign, or otherwise convey all copyright ownership to the Editorial Office of the Green Publication in the event that such work is published in the journal. Such conveyance covers any product that may derive from the published journal, whether print or electronic. Green Publication shall have the right to register copyright to the Article in its name as claimant, whether separately
or as part of the journal issue or other medium in which the Article is included.
By signing this Agreement, the author(s), and in the case of a Work Made For Hire, the employer, jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere, and is not being considered for publication elsewhere in any form, except as provided herein. Each author’s signature should appear below. The signing author(s) (and, in