Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/11402
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dc.date.accessioned2016-07-12T10:22:17Z
dc.date.available2016-07-12T10:22:17Z
dc.date.issued2015
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/11402
dc.descriptionB.SC.IT(HONS)en_GB
dc.description.abstractThrough the development of policies, it is possible for healthcare professionals and resource planners to ensure that the rare healthcare resources are used optimally at an acceptable cost of care. The daily resource requirements are affected by a patients' Length of Stay (LOS) and the number of patients admitted. These may depend on many factors, such as those representing the characteristics of a patient like; gender, age and locality, or the changes in the weather. This study uses Coxian phase-type distribution (C-PHD) to obtain results and generate Phase-Type Survival (PTS) trees to analyse groups of patients which a ffect the admission and LOS distribution, through the use of covariates such as admission date, gender, age, district and source of admissions. PTS trees for the same distributions were carried out to find the effect that the weather might have on these, using covariates such as date, maximum temperature, minimum temperature, average temperature and maximum variability in temperature. This study uses a two year data set (2011-2012) of patients admitted to the Emergency Department at Mater Dei Hospital to generate the models and an independent one year data set (2013) of patients admitted to the Emergency Department at Mater Dei Hospital to evaluate. The PTS tree eff ectively clusters patients based on their LOS considering prognostic signifi cance of di fferent covariates, those related to patient characteristics and those related to weather. Characterising the covariates related to patient characteristics and weather both provided successful results in relation to LOS. Similarly, the PTS tree was used to effectively cluster patients based on the admission rate considering prognostic significance of different covariates, those related to patient characteristics and those related to weather. Characterising admissions to patient characteristic covariates provided the most successful prognostic significance.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectHospital utilization -- Length of stay -- Maltaen_GB
dc.subjectDistribution (Probability theory)en_GB
dc.subjectHospitals -- Emergency services -- Maltaen_GB
dc.titleCharacterising hospital admission patterns and length of stay in the emergency department at Mater Dei hospitalen_GB
dc.typebachelorThesisen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Information and Communication Technology. Department of Computer Scienceen_GB
dc.description.reviewedN/Aen_GB
dc.contributor.creatorAttard, Natasha
Appears in Collections:Dissertations - FacICT - 2015
Dissertations - FacICTCS - 2010-2015

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