Efficient metadata loading algorithm for generation and parsing of health level 7 version 3 messages

Information technology has started focusing on the healthcare enterprises, for providing better medical care. There exist different healthcare enterprise standards that are used for the communication of medical information across health enterprises providing swift and reliable results. HL7 is one of those standards that are used for the exchange of medical information between healthcare systems. The main focus of this research work is to make metadata processing efficient in HL7 v3. HL7 v3 is an emerging standard to achieve semantic interoperability with its well defined information models like reference information model (RIM), domain message information model (D-MIM), and refined message information model (R-MIM). These models are converted to some technology specific format for implementation such as model interchange format (MIF). This format includes metadata information in the form of XML. MIFs are required to load into memory for generation and parsing of messages. HL7 Java SIG API utilizes these files in a non-efficient manner, as it loads all associations (no matter these are required or not) present in a particular MIF file. This is not only the performance issue but also memory wastage. In this paper, we propose an algorithm to improve the process of message generation and parsing by avoiding unnecessary associations during MIF loading. This technique is based on proxy design pattern. The proposed technique removes the performance bottleneck of the API and makes it space efficient.