Novelty interoperable software systems use valuable sources of terminology in order to provide operational transparency and clinical proficiency. They breach the terminology gaps by integrating various medical vocabularies on a code-level.
By synchronizing immensely populated medical databases in semanticity and syntax, interoperable systems provide more accurate assessment of clinical data. The core information needed for operating large-scale trials are the ones that concern patient-care data and clinical laboratory results.
In order for a multi-centered clinical trial to be accurately developed and run, the operating software needs to relate to SNOMED CT and LOINC.
What Is SNOMED CT?
SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) is a multilingual dictionary of standardized clinical terms that facilitates the exchange of relevant information between healthcare professionals. It is considered as the most comprehensive dictionary of clinical terminology in the world.
SNOMED encodes the meanings of terms often used in healthcare settings, such as symptoms, signs, diagnoses, procedures, clinical findings, and body structures. Its database is also able to distinguish between etiologies, pharmaceuticals, specimens, and medical devices.
Today, this system is the core of information systems used for keeping electronic healthcare records and is fundamental for interoperable health-related software.
History of SNOMED CT
Throughout its long history, the system has been updated multiple times and meticulously perfected.
The system originated in 1965, from Systematized Nomenclature of Pathology (SNOP), which was used to describe morphology and pathology. To satisfy the growing need for medical research, SNOP was expanded to SNOMED in 1975. Later on, in 2002, after the merger with other clinical terminology standards, such as CTV3, the final version of SNOMED CT was created.
Today, SNOMED CT is developed, maintained, and distributed by the International Health Terminology Standards Development Organisation (IHTSDO), established in 2007. Since 2013, SNOMED has been a standard mandatory in the US for keeping EHRs.
How Does SNOMED CT Work?
SNOMED is used for indexing, storing, and retrieving medical data. Furthermore, it groups information and facilitates sharing between distant healthcare specialists and through various healthcare settings.
Within the system, data can be stored in various healthcare settings simultaneously, and at the same time, organized, queried, and analyzed.
Three criteria define each term in the base:
- Concepts – Each one is a unique clinical meaning,
- Descriptions – Every concept relates to two descriptive categories – Fully Specified Name (FSN) and Synonym,
- Relationships – Series of cross-functions that define how concepts relate to each other.
Concepts are defined within a nine-system hierarchy, and these are:
- Clinical finding concepts,
- Procedure concepts,
- Evaluation procedure concepts,
- Specimen concepts,
- Body structure concepts,
- Pharmaceutical/biologic product concepts,
- The situation with explicit context concepts,
- Event concepts,
- Physical object concepts.
Each concept is numerically represented, and as of January 31st, 2020, there were officially 352,567 different concepts within SNOMED CT.
Who Uses SNOMED and Why?
Its features are used by healthcare professionals to capture data, and by researchers to link relevant information across settings. The streamlined data reassures for evidence-based patient care across different healthcare contexts.
SNOMED CT can be cross-mapped to other international standards, such as ICD-10, which helps facilitate semantic interoperability. For clinicians, it is an essential resource with extensive medical content.
The numerical reference system assists the exchange of clinical information among disparate health care providers and electronic medical records systems.
What Is LOINC?
LOINC (Logical Observation Identifiers Names and Codes) is a unified database that assists the exchange and gathering of clinical results, such as laboratory tests, clinical observations, and measurement-related patient outcomes. It is also used for laboratory testing devices and healthcare management.
LOINC facilitates both laboratory expertise and research by providing a unified terminology for different results. The LOINC database and its operating software are created and maintained by the Regenstrief Institute, Inc.
History of LOINC
LOINC was introduced in 1994 to assist the exchange of results obtained by different laboratories. Before that, no universal standard for laboratory tests existed, which presented a problem for cooperating laboratories.
Even today, LOINC is not considered as a necessity for performing laboratory and observational operations. Many systems use different computable coding systems, such as IHE or HL7. However, LOINC is considered as a more interoperable system and is generally preferred.
How Does LOINC Work?
LOINC database uses more than 71,000 terms to describe observations. Its terminology has two main parts:
- Laboratory LOINC, which covers lab tests and microbiology values;
- Clinical LOINC, which describes non-laboratory observational values, such as obstetric ultrasound, ECG values, etc.
Each six-figured term in the database represents a single test, observation, or measurement. Every figure represents a distinctive value of the test or measurement: Component, Kind of property, Time aspect, Specimen type, Type of scale, and the Type of method. Furthermore, the database has other descriptive fields, such as synonyms, status, translations, and substance information.
Other standards, such as ASTM, E1238, IHE, HL7, or CEN/TC251, use LOINC to electronically transfer results from different reporting settings to a single network.
How Is LOINC Used in Clinical Trials?
LOINC is used with REMLA (Regenstrief LOINC Mapping Assistant) – a program designed for searching through LOINC databases and mapping local files to it.
New versions of LOINC and REMLA are released twice every year, in June and December.
LOINC codes allow a researcher to merge clinical results from multiple trials into a single database. Using corresponding universal codes is considered as a time-valuable investment.
The downside of LOINC is the interpretation of its hierarchy. The computable hierarchy exists; however, it is not exposed in a semantic format, that would be easy for a terminology server to interpret.