It’s About Time to Improve Health Mapping Quality and Reduce Manual Mapping Time

It’s About Time to Improve Health Mapping Quality and Reduce Manual Mapping Time

Manual mapping has had its era and most healthcare organizations testify to it becoming a burden to maintain. It is about time for all healthcare organizations to acquire an information technology (IT) system that supports mapping activities and improves the mapping quality. Why and how? Through semantic interoperability, we will shape the future of healthcare, and mapping is a key part of it. Let us explain more!

What is mapping? 

Semantic interoperability is the ability for an information technology (IT) system to receive information from another IT system and reliably apply its business rules to the information received. To achieve semantic interoperability, the health system must adopt and implement consistent clinical messaging and data standards that provide a framework and language for communicating a shared meaning. This is where mapping comes into place.

Standard vocabularies for representing clinical data have gone mature over the last many years and have been internationally adopted in many areas. For example, Logical Observation Identifiers Names and Codes (LOINC; Regenstrief Institute, Inc., Indianapolis, IN), provide universal identifiers for laboratory tests and other clinical observations, and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) provides universal identifiers for organisms, substances, diseases, and other findings that may be recorded in the medical records or identified in test results. 

Although clinical data standards are available most hospitals, laboratories, and physicians continue to rely on local and idiosyncratic ways of identifying clinical observations (eg. laboratory tests and clinical measurements). Therefore, in order to understand the local clinical data from a hospital, health departments must often translate data into standardized LOINC and SNOMED CT codes. This transformation process is known as mapping. 


Challenges of Mapping Local Codes to International Standardized Codes 

Mapping local terms to standard vocabularies is complex and resource-intensive. Identifying the correct concept from the standard vocabulary requires specific domain knowledge and knowledge of the target vocabulary standards. In addition, local laboratory test names often lack the information needed to appropriately identify the correct standard concept. For example, test names may lack an indication of the specimen type or whether the result returned is quantitative or ordinal. Similarly, the units of measure associated with a result may not be available during mapping. This missing information can increase the risk of having ambiguous test names, which makes the mapping process more challenging. Furthermore, data representation of the local clinical observations can vary as different people typically have been a part of developing the local vocabulary list. For example, the granularity and descriptional information for each entity in the same hospital can vary. This variation can make the concept identification more complex than normal as information needs to be gathered at different places.


Tools to Optimize the Mapping Process

CareCom has many years of experience with different data structures both local code systems and international standards. Based on our research and client experience, the mapping should leverage as much source or local code system information that can be collected. The additional properties/values/fields are needed because the local code descriptions are typically less granular compared to the international standards. Utilizing the full scope of the local content will increase the accuracy of the mapping. A lot of different tools are available to automatically map your local codes into a standard, both using simple algorithms or AI-based algorithms. However, each local code system is unique, and using one algorithm or a domain-optimized algorithm sometimes isn’t always enough to ensure high clinical quality mappings. The clinical domain is not so simple that algorithms used for auto-mapping can be one-size-fits-all. Incorporating the SME’s knowledge of both the local and standard code system is of paramount importance to achieve a successful automatic mapping. HealthTerm provides a mapping tool to configure your own algorithm(s) to ensure the needed clinical domain knowledge is included when mapping match suggestions are generated.

Using tools to improve the mapping process does not (and perhaps should not) necessarily replace the expert human reviews but it can reduce the time spent on the mapping process. We still suggest that expert reviews are needed to resolve the computer-generated suggested mappings as local and standard vocabularies evolve and the clinical understanding of ambiguity and use are difficult to replace. Furthermore, the burden of maintaining the manual mappings is significant, ongoing, and easily underestimated. Therefore, all healthcare organizations – whether data senders, receivers, or both – require people, processes, and tools to support mapping activities and improve the mapping quality. 


Camilla Frejlev Bæk

Product Owner at CareCom A/S


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Blog Resources

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  • Bodenreider O . Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform. 2008:67–79.
  • Lin MC Vreeman DJ McDonald CJ Huff SM . A characterization of local LOINC mapping for laboratory tests in three large institutions. Methods Inf Med. 2011;50(2):105–114.
  • Baorto DM Cimino JJ Parvin CA Kahn MG . Combining laboratory data sets from multiple institutions using the logical observation identifier names and codes (LOINC). Int J Med Inform. 1998;51(1):29–37.
  • Kim H El-Kareh R Goel A Vineet FNU Chapman WW . An approach to improve LOINC mapping through augmentation of local test names. J Biomed Inform. 2012;45(4):651–657.
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