Interoperability standards in Medical Imaging – DICOM and HL7

According to Eurostat, in 2022, ~15000 CT and ~10000 MRI scans per 10000 inhabitants were performed in the EU. That is a 28% and 43% increase since 2012. Managing and fully utilizing this huge amount of data requires organisation and cooperation of many public institutions as well as medical facilities. In other words, it requires interoperability, that is allowing different systems and applications to securely and conveniently exchange medical data.

Therefore, fundamental part of this medical image interoperability is utilization of various standards of storing, reading and exchanging said data. In this article I’ll be discussing in some detail two important examples of such standards DICOM and HL7. I’ll  focus more on DICOM as it specifically refers to imaging data and HL7 is more general in its approach.

In 1993, a committee consisting of members of both American College of Radiology (ACR) and National Association of Electronic Manufacturers (NEMA) presented the Digital Imaging and Communications in Medicine (DICOM) technical standard.

Their goal was to facilitate interoperability of medical imaging equipment by specifying standards for:

• network protocols for communication between DICOM-conforming entities;
• syntax and semantics of commands that could be used with these protocols;
• file format and directory structure for medical images (and other media).

It’s important to note that DICOM provides a set of recommendations and not details of their implementation or any accompanying software. However, since the standard’s conception, many tools and software libraries, including open-source, were made available to healthcare professionals, researchers, and engineers alike—many of which are used in medical image analysis.

Novelty of DICOM standard came from its three core assumptions:

• ability to share image-related information in standardized format over the network is and will only become more essential;
• metadata, i.e. image acquisition and patient information, is as important as pixel data;
• any sustainable standard should be general enough to cover almost any modality and flexible enough to adapt to their changes over time.

Prior to DICOM’s widespread adoption biomedical were mostly either printed to film or stored on removable media in proprietary format. Usability of data in such system was therefore limited by availability of specific software and possibility of sharing removable media.

Above all, DICOM standard puts a lot of emphasis on importance of metadata describing how given image was acquired. It frames metadata as essential to fully utilize image data in diagnosis, as well as in research or data management. Each attribute in metadata is identified by its unique tag that consists of two numbers: group id and element id. Attributes with the same group id tend to describe the same entity such as patient information or image orientation. For instance, group 0018 contains elements describing details of CT image acquisition such as gantry tilt (0018, 1120), convolution kernel/algorithm used for image reconstruction (1018, 1210), type of filter inserted into a x-ray beam (1018, 1160) etc.

Beside using public tags i.e. tags predefined in the standard, DICOM allows users to define their own private tags.  In summary, this feature was designed as a way to conveniently extend the standard along with evolution of biomedical imaging. For example, private tags may describe some novel imaging tool used for this study or response of an ai classification model. Although it’s a good practice to firstly make sure that the new attribute couldn’t already be described using public tags.

HL7 stands for Health Level 7 and comes from number of layers in  Open Systems Interconnection model that HL7 implements. It was created by Health Level Seven International, a non-profit organization aiming to provide standards enabling interoperability in healthcare. HL7 standardizes format, syntax and content of messages exchanged between different systems within the same or between different facilities. In other words, it enables interoperability between electronic healthcare records, laboratories, other hospitals or even billing and HR systems.

For example, performing a scheduled CT scan in the hospital would require data exchange between clinical, radiology and picture archiving systems (CIS, RIS, PACS). Firstly, RIS would receive patient information via HL7-compliant message from CIS, to populate metadata of the new DICOM file. Secondly, that newly created DICOM file would be archived in PACS via data exchange protocol defined in DICOM standard. Thirdly, image would be retrieved by  RIS from PACS via DICOM protocol, then radiologist would create report based on it. And finally, both report and DICOM file converted to appropriate format would be sent back to CIS via HL7-compliant message.

In conclusion, DICOM and HL7 play a crucial role in enabling interoperability in healthcare by providing standards of data communication. That comes from the fact that today’s healthcare IT could be understood as information exchange between many interdependent systems. Consequently, any efforts to bolster interoperability that would reduce the need to translate this communication are worthy of consideration. In addition, increased interoperability has benefits not only for patient care, but also encourages and enables data exchange between researchers.

One of our previous blog posts, Interoperability Standards in Medical Imaging – DICOM and HL7, explores this topic in more detail and is available for further reading.

Reference:

Healthcare resource statistics – technical resources and medical technology – Statistics Explained

Larobina M. Thirty Years of the DICOM Standard. Tomography. 2023; 9(5):1829-1838. https://doi.org/10.3390/tomography9050145

1 Scope and Field of Application

Shivshankar, S., Makhija, N., Mathusudhanan, P. (2024). Digital Imaging and Communication in Medicine (DICOM): Biomedical and Health Informatics: Imaging and Interoperability Using HL7 and DICOM. In: Chaurasia, M.A., Balaji, P., Frery, A.C. (eds) Smart Healthcare and Machine Learning. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-97-3312-5_20