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Top1. Introduction
In any nation, the emergency treatment unit (also known as emergency department) in hospital plays a critical and progressively significant role in offering services to the patient every year. The count of high risk victims are increasing day–by-day over the previous years and thus congestion at ETU has become global problem. Most of the crowding is a consequence of old patients, nurse care, professionals treating patients, public paying a visit to victim, which in turn influences medical outcome of patient’s health, and patient’s comfort (Bernstein & Onofrio, 2009). It also creates disturbance among medical staff causing violence in the department. Various studies have exhibited that volume of patient flow in ETU could be an important tool for evaluating the impact of overcrowding (Robert et al., 2006). Overcrowding in ETU was also spotted as a nationwide crises (Eitel, Rudkin, Malvehy, Killeen & Pines, 2010) in countries like United States and Canada. A similar problem can be observed in hospitals of Northern Ireland for the increase in demand of treatment. The causes can be less capacity of wards, increase in longer waiting times and less number of staff available at hospitals. The greatest challenge of ETU is its capacity. In current days, as the population is growing in large number, many individuals are encountered with long-lasting diseases. These complicated diseases are successfully treated by ETUs, indicating more time is allocated for single patient. Thus, increase in appointments for emergency units through ambulance and management care systems results in longer delays for the treatment, hence causing overcrowding at ETU. Additionally, most of the time doctors do not have patients’ background information and medical history. Therefore, gathering patient’s information at post discharge is practically unfeasible and noted as a vulnerable point.
One similar problem can be observed at IT sector where there are possibilities of facing natural disasters, fire accidents or other accidents causing injury to the employees at work. Health issues, work stress (Shivani & Vinky, 2016), falling sick and sudden heart or brain strokes are the other reasons for causing emergency rush to the hospital. Although, every IT work place has an emergency plan of giving first-aid to control medical crisis, the severity in patient’s condition requires employees to be rushed to the hospital through IT enterprise transportation or by hospital ambulance. The study conducted by Asbah and Asif (2019) shows that the above conditions of IT professionals are not due to the effects caused by social media. Enhance communication is essential to treat IT professionals with care in Emergency Treatment Unit (ETU). ETU serves as a vital line of care for injured and critical-care needs. It has a systematic step wise process to treat IT employees and gather their information. The first process is called a triage stage where the status of patient’s severity is determined to give immediate treatment. Here, employee faces unfriendly environment due to crowding at ETUs, as every patient wants an immediate treatment. Based on employee’s condition, he/she is sent for treatment unit or for registration. Registration process is required mainly for two reasons- it lets the ETU staff to collect the IT worker’s detailed information and to obtain authorize access for further treatment. The test results give physicians an additional information for type of treatment given to a patient. The final step follows the discharge process where an employee are sent home with written home-care instructions.
Keeping in view the circumstances of IT professionals, more effects are expected from the doctors, hospital management, visitors and modern techniques to identify the causes and plan accordingly in advance. Medical evidences are being improved regularly and yet have not met enhanced effectiveness. Whereas, in reality, the time to consult a doctor was increased. Thus, the current study uses machine learning methods to predict the future outcomes of patent’s admissions based on the past inpatient and emergency administrative data. The objective of the present study is to design and develop a model that can provide early treatment to IT professionals in their emergency conditions. This could be achieved by implementing a LGBM model which may perform better than other ML algorithms. The rest of the paper is formulated as follows- the second phase describes the related work to the current research; the third phase comprises of the methods for predicting admission rates; whereas, the fourth phase includes results conducted by the study and the fifth phase consists of conclusion and future scope.