Middleware to Integrate Patient Data from Heterogeneous Distributed Databases and Its Efficacy

The health organizations store the patient data in different repositories and scattered in diverse locations. In the healthcare domain, the problem is that each hospital or even each department under a hospital maintains its own database having various data models (SQL, NoSQL, etc.). In this situation, existing or new applications require to grant healthcare actors to locate and share patient data from those pre-existing distributed databases (DDBs) remotely for the needs of patient quality treatment, daily operations of the health centers. However, data integration from distributed data sources is raising concern for data model variability. Therefore, it is significant to identify that how much an application like middleware is efficient to reconstruct and share patient data remotely from heterogeneous DDBs over the networks. The health organizations could also require to ensure whether their existing database model performs well or should replace by another one. So, this paper aims to design a system using different databases consisting of distinct data structures and an algorithm for middleware to integrate data from them with testing the system performance. The experimental results of this research work show that the patient data could be shared from various distributed data sources efficiently. Therefore the study could direct the healthcare organizations for sharing patient data from heterogeneous distributed databases without replacing the existing data model.