Querying Healthcare Data in Knowledge-Based Systems

In the ever-evolving healthcare landscape, integrating knowledge-based systems into data querying processes is becoming imperative. The existing challenges in querying healthcare data lie in the complexity of extracting meaningful insights from vast and heterogeneous datasets. EHRs store different forms of data, and query systems’ scalability and performance, especially considering the increasing volume of EHR data, are the main challenges faced. To overcome these challenges, the paper proposes a system with a user-friendly graphical interface for creating Archetype Query Language (AQL) queries in openEHR systems. It consists of three components: User Interface, which allows the user to specify query parameters, modify EHRs paths, filter data, and customize query results; Query builder, which creates the AQL query based on input from the User Interface and Repository of Documents where the compositions are stored and the query result obtained from this component is sent back to User Interface. It stands out with its innovative approach, systematically extracting openEHR schemas and simplifying the creation of complex AQL queries. The system’s effectiveness and user satisfaction make learning, using, and developing queries for graph-driven healthcare data knowledge easy. The system enhances the overall functionality and usability of the query builder within the system. It offers a pathway to improved clinical decision-making and patient care outcomes.