Commenced in January 2007
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A Novel Approach to Handle Uncertainty in Health System Variables for Hospital Admissions

Authors: Manisha Rathi, Thierry Chaussalet

Abstract:

Hospital staff and managers are under pressure and concerned for effective use and management of scarce resources. The hospital admissions require many decisions that have complex and uncertain consequences for hospital resource utilization and patient flow. It is challenging to predict risk of admissions and length of stay of a patient due to their vague nature. There is no method to capture the vague definition of admission of a patient. Also, current methods and tools used to predict patients at risk of admission fail to deal with uncertainty in unplanned admission, LOS, patients- characteristics. The main objective of this paper is to deal with uncertainty in health system variables, and handles uncertain relationship among variables. An introduction of machine learning techniques along with statistical methods like Regression methods can be a proposed solution approach to handle uncertainty in health system variables. A model that adapts fuzzy methods to handle uncertain data and uncertain relationships can be an efficient solution to capture the vague definition of admission of a patient.

Keywords: Admission, Fuzzy, Regression, Uncertainty

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084474

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References:


[1] A.F. Shapiro, "Fuzzy Regression Models", ARC 2005.
[2] O. Berman, F. Zahedi, & K.R. Pemble,, A decision model and support system for the optimal design of health information networks, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews, 2001), 31(2): 146-158.
[3] J.M. Corrigan, & J.B. Martin,, Identification of Factors Associated with Hospital Readmission and Development of a Predictive Model, Health Services Research (1992), 27(1): 81-101.
[4] E. Demir, T. Chaussalet, H. Xie, & P.H. Millard, Modelling risk of readmission with phase- type distribution and transition models, IMA Journal of Management Mathematics (2008), 20(4): 357-367.
[5] M. Hensher, N. Edwards, & R. Strokes, International trends in the provision and utilization of hospital care, Bio Medical Journal (1999), 319(7213): 845-848.
[6] King-s Fund Patients at risk of re-hospitalisation (PARR) case finding tool. Available at http://www.kingsfund.org.uk current projects/predictive risk/index.html, (2006).
[7] E.R. Marcantonio, S. McKean, M. Goldfinger, S. Kleefield, M. Yurkofsky, & T.A. Brennan, Factors Associated with Unplanned Hospital Readmission among Patients 65 Years of age and Older in a Medicare Managed Care Plan, The American Journal of Medicine (1999), 107(1): 13-17.
[8] E. Demir, T. Chaussalet, H. , Xie, & P.H. Millard, Modelling risk of readmission with phase- type distribution and transition models, IMA Journal of Management Mathematics (2008), 20(4): 357-367.
[9] P. Nagar, & S. Srivastava, Adaptive Fuzzy Regression Model for the Prediction of Dichotomous Response Variables using Cancer Data: A Case Study, Journal of Applied Mathematics and Informatics (JAMSI, 2008), 4(2): 183-191.