# Decision Support Software for Forecasting Patient's Length of Stay
## 1 Introduction  
Nowadays, every healthcare system faces constant pressures to lower operating costs by improving the use of resources while maintaining and even enhancing the quality of service. Successful healthcare resource management is especially indispensable for addressing these seemingly contradictory pressures. The main objective of hospital managers is the administration of facilities, equipment, and labor resources for the establishment of an appropriate planning and organizational structure while at the same time they anticipate expenditure reduction. To this end, several methodologies and techniques have been presented and developed. The major component in these techniques is the accurate prediction of patients' hospitalization time and the identification of the factors which influence it. Length of Stay (LoS) is usually defined as the duration of a patient hospitalization and is calculated as the difference between the timestamp of a patient discharge and the timestamp of its admission. It is generally acknowledged as an indicative marker of inpatient hospitalization costs and resource use [1]. Since bed supply in a hospital is limited, their use is considered economically crucial for most hospitals and any administration policy, related to bed use, has profound impacts on the perception of quality in the provided healthcare and satisfaction of patients and physicians. Moreover, with the growing number of hospitalized patients, the prediction of the average LoS has become increasingly significant for effective admission scheduling and resource planning. Currently, clinicians and hospitalmanagers rely on only aggregated data and generally assume that the prediction of patients' discharge date and LoS is heavily depended on experience. It is worth noticing that many hospitals have no ability to predict and measure future admission requests [2]. During the last decades, hospitals have managed to accumulate a large volume of data which enable researchers to measure and compare clinical performance and use these results to support or critique policy decisions. Machine learning techniques can be considered a helpful tool, offering a first step in extracting useful and valuable information from healthcare data and gaining insights into the prediction of patient's LoS and on the major factors and elements which affect it. To this end, research focused on the application of machine learning on patients' data for the development of accurate and intelligent Decision Support Systems (DSS) [3; 4; 5; 6; 7; 8; 9]. Specifically, an academic DSS is a knowledge-based information system which captures, handles, and analyzes information which affects or is intended to affect decision making performed by people in the scope of a professional task appointed by a user [10]. Through the use of a predictive DSS, it is possible to forecast patients' LoS in a hospital and assist healthcare management plan, policy, and resources. Moreover, LoS prediction is critical for diseases or injuries which necessitate long treatments or involve scarce patient care resource. Therefore, the development of an efficient DSS is considered essential and valuable not only for hospital administrators but also for the patients. More comprehensively, \"knowledge discovery\" can assist hospital administrators in their decision-making process for promoting health services, rehabilitation planning, resource allocation and healthcare unit administration (e.g., patient admission/treatment/discharge, bed management, staff scheduling). Any bottlenecks in bed and resource availability could be foreseen in time to avoid thereby unnecessary patients' transfer between wards during their admission. Thus, healthcare policies could be properly prioritized and the appropriate allocation of healthcare resources could be comprised according to differences in patients' LoS, health status and social-demographic features.