Data Interoperability Enhancement of Electronic Health Record data using a hybrid model

An IoT based healthcare system promises the implementation of high-quality healthcare services in a time bound and accurate manner. But the varieties of data coming from various sources will make the system more heterogeneous and hence it is challenging to process them further. These data coming from sensors are usually collected from the sensor's web and stored in Electronic Health Records (EHR). Data in EHR consists of each patients' details with respect to his hospital visits, previous treatment history, medication used, medical history etc. An error free and understandable data handling process enhances data interoperability among various EHRs, which use different ways of representing data. To handle these multiple types of data stored in different EHRs, data interoperability enhancement techniques such as semantic and syntactic methods play major roles. But, Syntactic method fails in tapping the meaning of the data while semantic method does not consider the format of the data. These shortcomings are overcome by the proposed hybrid method which can tap the meaning of data from heterogeneous sources while bringing uniformity for the data format as well. The proposed technique is analyzed in healthcare domain and is proven to be more efficient than using each method separately.