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Development of an HL7 interface engine, based on tree structure and streaming algorithm, for large-size messages

Ki Sung Um1, Yun Sik Kwak, Hune Cho

  • 1Center of Bioinformatics, National Cancer Institute, Notional Institute of Health, 6116 Executive Blvd. Suite 403, Rockville, MD 20852, USA. umkis@mail.nih.gov

Computer Methods and Programs in Biomedicine
|September 27, 2005
PubMed
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This study introduces a new streaming algorithm for Health Level Seven (HL7) message parsing, overcoming message length limitations. This method efficiently handles large medical data, including images, improving healthcare data exchange.

Area of Science:

  • Health Informatics
  • Computer Science
  • Medical Data Exchange

Background:

  • Existing Health Level Seven (HL7) interface engines limit message length due to memory-intensive string array parsing.
  • This limitation prevents the effective handling of large data, such as images and multimedia, crucial for modern electronic health records.
  • Current systems struggle with critical errors when processing lengthy HL7 messages containing rich media.

Purpose of the Study:

  • To address the limitations of traditional HL7 message parsing methods.
  • To develop a novel HL7 parsing engine capable of handling unlimited message lengths, including large medical data.
  • To enhance the exchange of comprehensive patient information, incorporating multimedia elements.

Main Methods:

  • Implementation of a 'streaming algorithm' for HL7 message parsing.

Related Experiment Videos

  • Utilizing a character-stream object to process data character-by-character between main memory and hard disk.
  • Developing an HL7 engine with functionalities for message generation, parsing, validation, browsing, sending, and receiving, including XML format support.

    • Main Results:

    • The new streaming algorithm effectively alleviates the processing load on main memory.
    • The developed HL7 engine successfully parsed and generated XML-formatted HL7 messages.
    • The engine facilitated the exchange of HL7 messages containing 10MB images and discharge summaries between two university hospitals.

    Conclusions:

    • The streaming algorithm offers a robust solution for overcoming HL7 message length limitations.
    • This approach enables the efficient handling of large medical data, including images and multimedia, in healthcare systems.
    • The developed HL7 engine significantly improves the capability for comprehensive medical data exchange.