Comparison between Related Work and The Project

As discussed above, the related work described in Section 4.1 (i.e., web based HL7 message generation and validation) tries to create and validate the HL7 XML messages.

It parses the HL7 EDI message and gets the message components to check them against the database having HL7 components definitions for message validation and also

generates the XML form of the message from the components. The related work in Section 4.2 (i.e., HL7 Java SIG Project) makes use of the HL7 V3 RIM data model, HL7 data types and the meta files to create the XML message from the RMIM instance present in the memory. It can also generate the RMIM instance from the XML message, thus giving two ways of transformation of XML messages and RMIM instances. The third related work in Section 4.3, which is the thesis titles interoperability in healthcare, defines a way to tackle the semantic, technical and process interoperability of HL7 and openEHR messages. It uses the ontology mapping and XQuery and XSLT to develop a system to achieve the above mentioned interoperability. This project introduces the combination of SPLE with FODA and MDE with GME in order to generate the interoperable HL7 and openEHR messages. The FODA is used to classify the tags in the messages as mandatory, optional, alternative or ‘or’. This helps to get all the possible combination of tags. Each combination represents an XML message to be generated.

Also, the transformation rules introduced in the project using GME based on MDE helps to specify all the transformations that should be specified in order to get the messages in HL7 and openEHR standards simultaneously. The FODA classification and the transformation rules can be specified simultaneously in a single model using this project, which helps in generating all the possible XML messages with the unique tags combinations in HL7 and openEHR standards.

CHAPTER 5

CONCLUSION

This project helps to generate a family of different EMR messages (XML) with the help of features classification based on SPLE and FODA principles and also by using transformation rules. As such, the messages can be generated by specifying the features (tags) as either mandatory, optional, alternative or more-of. It uses concepts of software product line, analyzed by using FODA, in order to build the common parts (tags) in all the messages and then separately write the variant tags to their corresponding output messages. Also, the project is designed using MDE concepts, which facilitates simple design and better communication. A significant aspect of this project is that users have the flexibility to define the interoperable relations between the tags other than simply applying the FODA classification to them. The tags can be related such that one can become a parent of another, or it can be added or removed form the other standard message(s). Also, attributes can become tags in the other standard if specified so by users. Once users give all such specifications, the model can be interpreted using the code generation program written in Java to generate the desired XML messages. Since XML is used in many other domains, this application can be used to generate the messages in that domain too, thus making it a more generalized tool.

References

[1] S. N. Di Lima, L. T. Johns, and J. G. Liebler, “A Practical Introduction to Health Information Management,” Aspen Reference Group (Aspen Publishers), 1998.

[2] J. S. Luo, “Electronic Medical Records,” Primary Psychiatry, 13(2):20-23, 2006.

[3] Health Level 7, http://www.hl-7.org/HL7-messages.asp.

[4] L. Heather, “openEHR - The World's Record,” PulseIT, (6): 50-55, 2007.

[5] K. Pohl, G. Bockle, and F. V. D. Linden, “Software Product Line Engineering:

Foundations, Principles and Techniques,” Springer, 2005.

[6] S. Trujillo, D. Batory, and O. Diaz, “Feature Oriented Model Driven Development: A Case Study for Portlets,” the 29th International Conference on Software Engineering, pp.

44-53, 2007.

[7] K. C. Kang , S. G. Cohen, J.A. Hess, W. E. Novak, and A. S. Peterson, “Feature-Oriented Domain Analysis (FODA) Feasibility Study,” Technical Report CMU/SEI-90-TR-021, SEI, Carnegie Mellon University, November 1990.

[8] D. Gaševic, D. Djuric, and V. Devedžic, “Model Driven Engineering and Ontology Development,” Springer, 2009.

[9] Vanderbilt University,“GME Manual and User Guide,”

http://www.isis.vanderbilt.edu.

[10] T. Kosar, P. Martinez-Lopez, P. Barrientos, and M. Mernik, “A Preliminary Studyon Various Implementation Approaches of Domain-Specific Language,” Information &

Software Technology, 50(5): 390-405, 2008.

[11] A. van Deursen and P. Klint, “Domain-Specific Language Design Requires Feature Descriptions,” Journal of Computing and Information Technology, 10(1): 1-17, 2002.

[12] W. Stallings, “Cryptography and Network Security: Principles and Practice,” The 5^th Edition, Prentice Hall, 2010.

[13] A. J. Ducrou, “Complete Interoperability in Healthcare: Technical, Semantic and Process Interoperability through Ontology Mapping and Distributed Enterprise

Integration Techniques,” Doctor of Philosophy Thesis, University of Wollongong, http://ro.uow.edu.au/theses/3048, 2009

[14] H. Stevens, “Introduction to HL7 Version 3 Advanced Tutorial,” Halifax, 2003.

[15] M. Regio and J. Greenfield, and B, Thuman, “A Software Factory Approach to HL7 Version 3 Solutions,” MSDN Library, http://msdn.microsoft.com/en-us/library/ms954602.aspx, 2005.

[16] M. Mezini and K. Ostermann, “Variability Management with Feature Oriented Programming and Aspects,” ACM SIGSOFT Software Engineering Notes, 29(6): 127-136, 2004.

[17] S. Segura., R. M. Hierons, D. Benavides and A. Ruiz-Cortés, “Automated Test Data Generation on the Analyses of Feature Models: A Metamorphic Testing,” the 3rd International Conference on Software Testing, Verification and Validation, pp. 35-44, 2010.

[18] J Bezevin, “Model Engineering for Software Modernization,” The 11^th IEEE Working Conference on Reverse Engineering, pp. 4, 2004.

[19] Object Management Group, “Meta Object Facility (MOF) 2.0 Core Specification version 2.0 Available Specification” ,ptc/04-10-15

[20] I Berges , J Bermudez, A Goñi, A Illarramendi, “Semantic Interoperability of Clinical Data,” the 1st International Workshop on Model-Driven Interoperability, p.10-14, 2010.

[21] A. Ducrou and P. Eklund, “Ontology Mapping between HL7 Versions 2 and 3 and OpenEHR for Observations Messages,” Health Informatics Conference, pp. 40-46, 2009.

[22] E. W. Huang, S. H. Hsiao, and D. M. Liou, “Design and Implementation of a Web-based HL7 Message Generation and Validation System,” International Journal of Medical Informatics, 70(1): 49-58, 2003.

[23] HL7 Java SIG Project, http://aurora.regenstrief.org/javasig/wiki/HL7v3Overview [24] V. Vaishnavi, V. K. Vaishnavi, and W. Kuechler, “Design Science Research Methods and Patterns: Innovating Information and Communication Technology,” CRC Press, 2007.

[25] College of American Pathologists, “SNOMED CT User Guide,” 2007.

[26] eXist open source native XML database, http://exist.sourceforge.net/quickstart.html [27] M. Brundage, “XQuery: The XML Query Language,” Addison-Wesley, 2004.

[29] MuleSource Inc., “Mule enterprise service bus (ESB),”

http://www.mulesoft.org/what-mule-esb