oa Linked Data Based Semantically Enabled Electronic Medical Record Systems

Electronic Medical Record (EMR) systems are information systems keeping electronic versions of patients€' medical records. The use of EMR systems has been steadily increasing in recent years, due to many potential benefits. A fully functional EMR system can record patient demographic and chart data, keep track of vital signs, current medications, drug allergies and many other important facets of the patient's medical record. In an ideal scenario, such a system would also be able to handle and implement complex decision support tasks, such as clinical guideline implementations, drug interaction checking and critical alerts. One large issue with existing EMR system implementations is that the semantics of the information elements are not made explicit. Internal identifiers are often used as a placeholder for clinical concepts. This is a large problem when aiming to interact with the patient record, whether it is by physicians, new decision support implementations or by other health information systems. Without explicit semantics, both understanding and accessing the right information can require additional effort to adapt to these identifiers. We propose a Linked Data based approach to implement an EMR system to solve these issues. Linked Data, and in particular the Resource Description Framework (RDF) form the basis of the Semantic Web, which is designed to make the semantics of information both human and machine accessible. With RDF knowledge is represented as a set of triples, where each element of the triple can be an explict Uniform Resource Identifier (URI) with which internal and external resources can be linked. By linking to well defined medical terminologies, such as SNOMED CT, an RDF based approach can explicitly refer to a formalized set of concepts. Using databases for RDF documents, called triple stores, multiple large records can be stored as triple sets. With query languages that make use of triple based patterns, such as the SPARQL query language, the necessary clinical guidelines and other decision support can be implemented in a scalable way. We have evaluated this approach by implementing a Linked Data based version of an EMR system for Atrial Fibrillation (AF) patients. We have populated this system with automatically generated data that takes into account clinically feasible parameters. In addition a number of AF specific queries and decision support tasks were implemented to evaluate the scalability of the whole approach. Our results show that such a system has adequate performance for EMR systems deployed for a single small scale clinic, even on desktop level hardware. A key limiting factor is that an EMR can theoretically hold multiple years worth of very fine grained patient data, which can slow down the execution of various decision support tasks. However we have found that the portion of the dataset that is relevant to the decision support queries is often a very small subset of the overall record. A system that is dynamically able to divide the dataset over multiple data stores is needed to keep the system scalable for larger records with a higher number of patients.