Healthcare systems are one of the systems which usually operate under uncertainty. For example, incoming number of patients of a hospital’s outpatient clinics varies according to the time of the day. Patient’s length of stay at the hospital services differs depending upon the age of patient, gender of patient, having another disease or not. In healthcare systems; in order to be able to do plannings such as capacity or personnel planning; parameters, like number of incoming patients, length of hospital stay, operation time of surgery should be estimated. Some methods to estimate these parameters are forecasting , distribution fitting and machine learning. From these methods, distribution fitting tests whether the data which is collected in a specific time period fits to a theoretical distribution or not. Nevertheless, in the circumstances which has much uncertainty, estimating the aforementioned parameters may always not be possible by a pure statistical distribution. In this study, it has been shown that the use of mixed distributions in health systems, which are used in modeling of heterogeneous data and formed by merging or overlapping multiple distributions, better model uncertainities depending on the data set.
Primary Language | Turkish |
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Journal Section | Articles |
Authors | |
Publication Date | September 30, 2019 |
Published in Issue | Year 2019 Volume: 34 Issue: 3 |