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Transformation of Arden Syntax's medical logic modules into ArdenML for a business rules management system

Chai-Young Jung,Jongmin Choi,S. Jeong,Kyunghee Cho,Yong Duk Koo,Jin Hee Bae,Sukil Kim

Published 2016 in Artif. Intell. Medicine

ABSTRACT

INTRODUCTION Arden Syntax is a Health Level Seven International (HL7) standard language that is used for representing medical knowledge as logic statements. Arden Syntax Markup Language (ArdenML) is a new representation of Arden Syntax based on XML. Compilers are required to execute medical logic modules (MLMs) in the hospital environment. However, ArdenML may also replace the compiler. The purpose of this study is to demonstrate that MLMs, encoded in ArdenML, can be transformed into a commercial rule engine format through an XSLT stylesheet and made executable in a target system. METHODS The target rule engine selected was Blaze Advisor. We developed an XSLT stylesheet to transform MLMs in ArdenML into Structured Rules Language (SRL) in Blaze Advisor, through a comparison of syntax between the two languages. The stylesheet was then refined recursively, by building and applying rules collected from the billing and coding guidelines of the Korean health insurance service. Two nurse coders collected and verified the rules and two information technology (IT) specialists encoded the MLMs and built the XSLT stylesheet. Finally, the stylesheet was validated by importing the MLMs into Blaze Advisor and applying them to claims data. RESULTS The language comparison revealed that Blaze Advisor requires the declaration of variables with explicit types. We used both integer and real numbers for numeric types in ArdenML. "IF∼THEN" statements and assignment statements in ArdenML become rules in Blaze Advisor. We designed an XSLT stylesheet to solve this issue. In addition, we maintained the order of rule execution in the transformed rules, and added two small programs to support variable declarations and action statements. A total of 1489 rules were reviewed during this study, of which 324 rules were collected. We removed duplicate rules and encoded 241 unique MLMs in ArdenML, which were successfully transformed into SRL and imported to Blaze Advisor via the XSLT stylesheet. When applied to 73,841 outpatients' insurance claims data, the review result was the same as that of the legacy system. CONCLUSION We have demonstrated that ArdenML can replace a compiler for transforming MLMs into commercial rule engine format. While the proposed XSLT stylesheet requires refinement for general use, we anticipate that the development of further XSLT stylesheets will support various rule engines.

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