Unit 2 Discussion Reply

Respond to the discussion prompts and questions by the due dates outlined in the forum.

Discussion will include the following 4 components:

– Add new information or viewpoints

– Provide context by example, inference, explanation, or comparison

– Critically evaluate discussion content

– Challenge, question, or refute discussion content or accepted knowledge

Reply to both discussion:
Discussion Reply 1
Steven Bo
DQ1
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 as Logical 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 which represents data pertaining to
the hemoglobin A1c laboratory test (McQueen, 2019). The answer to this hypothetical question is the
result of this test, which can be represented 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 patient’s 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 to consider include the
patient’s 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 accurately represent a patient’s 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 patient’s 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.org/get-started/what-loinc-is/
1
Discussion Reply 2
Ellie Spindle
DQ3
C OLLA PSE
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 FHIR’s 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 complete patient
history through resources including insurance, payment, visits, and tasks (Saripalle et al.,
2019). This huge variety cultivates individuality and specification for a patient’s 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
2
interoperability resources (FHIR) standard: Systematic literature review of
implementations, applications, challenges and opportunities. JMIR Medical
Informatics,
9(7), e21929–e21929. 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, 103188–103188.
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, 103891–103891. https://doi.org/10.1016/j.jbi.2021.103891