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Electronic Health Records (EHR) embody a large volume of measured values and records of clinical encounters. Data is produced in healthcare settings at a large rate. Medical researchers find themselves facing massive volumes of data that should be reviewed and analysed before making clinical decisions that affect the lives of patients. The aim of this study is to apply the performance measurement approach used in finance and engineering to the EHR systems and develop a new system that allows clinicians who are not computer experts to analyse and query the EHR for better clinical decisions. Sources of healthcare data are numerous: nurses, doctors, technicians, patients, pharmaceutical companies, and third-party payers. Data is collected and stored from different sources such as computerised patient files, laboratory and diagnostic machinery, wired and wireless monitoring devices attached to patients across the various care-giver encounters, and many other electronic files and databases. Decision-support is critical in management of healthcare organizations. Data is collected for analysis, but requires organization of structure, design of systems to analyse the data, and technical knowledge from the management. This study aims at developing a novel system for the analysis of EHR data through the application of Performance Measurement and Management (PMM). This is achieved through investigation of the current situation and the state-of-the-art in clinical analytics, and then modifying the solutions to take advantage of PMM. |
