Sets in clinical measurements and observations

This post will focus on two code sets which are used in relation to clinical measurements and observations. The first of these two code sets is known asLogical Observation Identifiers Names and Codes (LOINC). LOINC was initially designed to focus on the exchange of laboratory data, although the set has since been expanded to include clinical data as well (McQueen, 2019). LOINC has been utilized in 177 countries to [facilitate] exchange, pooling, and processing of data(Benson & Grieve, 2021a). The codes themselves “represent [a hypothetical]question for a test or measurement” (Regenstrief Institute, n.d.). One example of a LOINC code is one whichrepresents data pertaining to the hemoglobin A1c laboratory test (McQueen, 2019). The answer to this hypothetical questionis the result of this test, which can berepresented in a fairly straightforward manner in the form of a numeric value as well as associated units of measurement (Regenstrief Institute, n.d.). However, in another example, a LOINC code can represent an observation regarding a patient’s smoking status (McQueen, 2019). The answer to this hypothetical question is not nearly as simple, as there are many variables which come into play when describing a patients smoking status. For instance, one variable would need to account for whether the patient is a current smoker, a prior smoker, or a lifelong nonsmoker. For patients who are smokers, other variables that are essential toconsider include the patients pack-year history, which depends on both the number of cigarettes smoked daily as well as the number of years as an active smoker. In this situation, it is clear that a numerical value would not be able to accuratelyrepresent a patients smoking status. The answer to this hypothetical question is contained within other standards, such as the Systemized Nomenclature of Medicine – Clinical Terms (SNOMED CT) (Regenstrief Institute, n.d.). Similar to LOINC, SNOMED CT is used in many countries around the world to facilitate clinical documentation and reporting and to retrieve and analyze clinical data. In contrast with LOINC, though, SNOMED CT incorporates clinical findings and their underlying contexts, including, but not limited to, involved anatomical site of the finding, causative agent of the finding, severity of the finding, and several other variables (Benson & Grieve, 2021b). Continuing with the aforementioned example regarding a patients smoking status, there are different SNOMED CT codes that exist based on the smoking habits of an individual, including whether the patient is a smoker or not, if it is known if an individual is a current or prior smoker, as well as other characteristics pertaining to smoking status (McQueen, 2019). This is one of many potential examples of the granular detail that is capable with the use of SNOMED CT.
These two code sets represent a microcosm of the various standards which exist in health care. However, the mere presence of numerous redundant terminologies in itself makes it quite challenging to achieve optimal semantic interoperability, which, by definition, relies on common and standardized information models and terminologies. Further adoption of SNOMED CT may be a step in the right direction to improve interoperability, considering that this tool is fairly comprehensive in nature relative to other terminologies (Hovenga & Grain, 2022).

References:

Benson, T., & Grieve, G. (2021a). LOINC. In T. Benson & G. Grieve (Eds.),Principles of health interoperability: FHIR, HL7 and SNOMED CT(4th ed., pp. 325-338). Springer.
Benson, T., & Grieve, G. (2021b). SNOMED CT. In T. Benson & G. Grieve (Eds.),Principles of health interoperability: FHIR, HL7 and SNOMED CT(4th ed., pp. 293-324). Springer.
Hovenga, E. & Grain, H. (2022). Health data standards limitations. In E. Hovenga & H. Grain (Eds.),Roadmap to successful digital health ecosystems(1st ed., pp. 169-207). Academic Press.
McQueen, D. (2019, December 10).LOINC and SNOMED CT – Why they’re better together.https://www.draegan.com/codes-2/
Regenstrief Institute. (n.d.).What LOINC is.https://loinc.is/

Discussion Reply 2

Ellie Spindle
DQ3
COLLAPSE
Standards are important when it comes to health technology to create seamless interoperability and simple usage for providers and patients. One dominating standard is fast healthcare interoperability resources (FHIR), developed by Health Level Seven (HL7). FHIR has grown in popularity and is widely used today because of its modular nature (Saripalle et al., 2019). These modules are composed of resources, or basic healthcare terms and needs, such as patient, diagnosis, or encounters (Ayaz et al., 2021). Every resource holds a narrative in relation to patients (Benson & Grieve, 2016). FHIR strives to follow representable state transfer (REST) concepts, including layered softwares and standard interfaces. This is crucial because REST inspires increased usability and dependable software designs, creating a more fluent user experience. With FHIRs modular architecture, old standards are being replaced because of their document-focused approach (Saripalle et al., 2019). These old standards lacked flexibility for users and were not focused on information integrity. With FHIR, clients dictate their requests and the server responds (Benson & Grieve, 2016). Clients also have the flexibility to update and alter records appropriately.
There are many benefits to the FHIR standards because they promote usability and interoperability. Through FHIR, standardized software is created so that the interface can be accessed from anywhere, including across various healthcare organizations, providers, and patients (Ayaz et al., 2021). There are a plethora of tools through FHIR to capture all of the data needed for high quality care. Healthcare providers can access a through resources including insurance, payment, visits, and tasks (Saripalle et al., 2019). This huge variety cultivates individuality and specification for a patients unique health story. FHIR is also the only standard to support REST practices, which is a huge benefit for successful interoperability (Saripalle et al., 2019). Application interface programming (API) is utilized by FHIR to promote interoperability between softwares, and enacts REST standards by striving for reliable and user-friendly services. Usability is promoted through FHIR because users are able to build patient profiles using collected data. Appropriate restraints are already implemented so that healthcare professionals do not need to worry about additional inputs, such as units or excess labeling (Saripalle et al., 2019).
On top of that, FHIR has cultivated a large amount of support because of its unique interface. Cerner has vocalized support for FHIR, along with other academic groups due to its blatant usability and future potential (Saripalle et al., 2019). FHIR allows for simplistic interfaces, where patient profiles can be created on one page, allowing for a positive user experience (Ayaz et al., 2021). While FHIR is highly beneficial, there are a few drawbacks to its implementation. Creating ideal interoperability can be challenging to achieve, due to old systems that have not adapted new standards and technologies. Keeping FHIR updated and functioning through required maintenance can bring new problems for information technology experts (Ayaz et al., 2021). There can also be issues when updating an already existing system to FHIR standards. Legacy softwares may require additional support when making the update to combine old systems with new standards (Saripalle et al., 2019). Data already created with old systems may struggle to update to FHIR, and may need to be altered or deleted (Shivers et al., 2021). This could be problematic for smaller organizations with less technical support, or facilities with specific focuses and needs. While there can be difficulties utilizing FHIR, the promise of increased interoperability and usability bring crucial benefits.

References
Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & Stiawan, D. (2021). The fast health
interoperability resources (FHIR) standard: Systematic literature review of
implementations, applications, challenges and opportunities.JMIR Medical Informatics,
9(7), e21929e21929. https://doi.org/10.2196/21929
Benson, B. & Grieve, G. (2016).Principles of health interoperability(B. Benson, Ed.). Springer
Verlag London.
Saripalle, R., Runyan, C., & Russell, M. (2019). Using HL7 FHIR to achieve interoperability in
patient health record.Journal of Biomedical Informatics,94, 103188103188.
https://doi.org/10.1016/j.jbi.2019.103188
Shivers, J., Amlung, J., Ratanaprayul, N., Rhodes, B., & Biondich, P. (2021). Enhancing
narrative clinical guidance with computer-readable artifacts: Authoring FHIR
implementation guides based on WHO recommendations.Journal of Biomedical
Informatics,122, 103891103891. https://doi.org/10.1016/j.jbi.2021.103891

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