Selected aspects of electronic health record analysis from the big data perspective

The electronic health record (EHR) groups all digital documents related to a given patient as anamnesis, results of the laboratory tests, prescriptions, recorded medical signals as ECG or images etc. Dealing with such data representation we face with plethora of problems as different form of data, unstructured data (as doctor´s notes), huge and fast growing volume, etc. It causes that EHR should be considered as the complex data representation. Accordingly, taking into consideration its complexity, hetorogenousity, fast growing and size we need special tools to analyse such medical big data. Such tools should be able to analyse datasets characterized by so-called 4Vs (volume, velocity, variety, and veracity). Notwithstandingly, we should also add the fifth V-value, because the only analytics tool deployment makes sense if it leads to healthcare improvement (as personalised patient´s care, unnecessary hospitalization decreasing or reducing the patient´s readmissions). In this paper we focus on the selected aspects EHR analysis from the big data perspective.