An Evaluation of Two Methods for Generating Synthetic HL7 Segments Reflecting Real-World Health Information Exchange Transactions

Thomas S. Mwogi, Paul Biondich, Shaun Grannis

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Motivated by the need for readily available data for testing an open-source health information exchange platform, we developed and evaluated two methods for generating synthetic messages. The methods used HL7 version 2 messages obtained from the Indiana Network for Patient Care. Data from both methods were analyzed to assess how effectively the output reflected original 'real-world' data. The Markov Chain method (MCM) used an algorithm based on transitional probability matrix while the Music Box model (MBM) randomly selected messages of particular trigger type from the original data to generate new messages. The MBM was faster, generated shorter messages and exhibited less variation in message length. The MCM required more computational power, generated longer messages with more message length variability. Both methods exhibited adequate coverage, producing a high proportion of messages consistent with original messages. Both methods yielded similar rates of valid messages.

Original languageEnglish (US)
Pages (from-to)1855-1863
Number of pages9
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2014
StatePublished - 2014

ASJC Scopus subject areas

  • Medicine(all)

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