A Novel Approach to Handle Uncertainty in Health System Variables for Hospital Admissions
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.
[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.
[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.
@article{"International Journal of Business, Human and Social Sciences:64140", author = "Manisha Rathi and Thierry Chaussalet", title = "A Novel Approach to Handle Uncertainty in Health System Variables for Hospital Admissions", 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", volume = "6", number = "10", pages = "2699-4", }