Nursing Terminologies, EHR & SNOMED CT

This paper focuses on nursing terminologies as an organized categorization, the importance of coding data in EHR systems, details of SNOMED CT as a standard nursing language and it's the difference in comparison to other interventions. EHR's allows for effective monitoring of individual health records through structured data elements. SNOMED CT contains an interdisciplinary aspect that provides reliable and consistent clinical information in both EHR's and more medical IT solutions. The aim of this assignment, therefore, is to discuss standard nursing languages and how the patient's individual data from Electronic Health Records assist in monitoring health.

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Codifying data in EHR systems entails assigning a standard code to each result to ensure consistency within clinical records and efficient communication with other systems (Fengab, Tsenga, Yana, Huanga, & Chang, 2013). Data present in EHR are a potential pool for researchers within the medical field; this is possible through secondary use of health care data whereby the patient's information gathered during treatment is taken into further use which is research (Fengab et al., 2013). Each clinical code set correlates with a particular medical concept and they can be used to build executable queries based on a database that will extract patient's data for more analysis.

Electronic Health Record (EHR) contains patients records that are accessible real-time by an authorized user such as medical providers. An EHR system has a vast pool of functions such as keeping a patient's medical history, dates of immunization, laboratory, x-ray and radiology tests, allergies and treatment plans (Wisner, Lyndon, & Chesla, 2019). Providers have access to tools that allow them to offer prescriptions to a patient based on evidence. EHR allows for automation and efficient workflow for nurses. It is evident that EHR is more effective than book records regarding retrieval of patient data.

EHR coding provides accurate patient information per system. Clinical providers can minimize errors and properly diagnose patients when using an EHR system. The work of providers like nurses eases and they're able to balance work and rest time through the use of EHR, leading to increase in productivity (Wisner et al., 2019). Nurses can attain their business goals through the incorporation of EHR systems. EHR coding makes it easier for nurses to complete their medical duties.

Coding in EHR leads to reduced costs in health care activities. EHR systems boost preventative remedies, better health care services coordination, and less unnecessary tests; all these activities lead to low costs (Wisner et al., 2019). Reduced paperwork is another aspect that results in lower expenses when using EHR systems. The use of EHR saves expenses that would otherwise be accrued if coding isn't implemented.

Code sets of information within the EHR system mean the patient's data is more secure in comparison to records on books. Providers can effectively and securely share information with their colleagues in different medical institutions. In general, EHR coding enhances security and privacy of patient data.

EHR coding sets allows for collection and analysis of patient data which can easily reach individuals remotely. Improved medical status of individuals through recommending better nutrition, increased physical activity more use of protective measures (Fengab et al., 2013). Medical providers can safely and more reliably give prescriptions. Through code sets in EHR, more patients can be addressed within a shorter time.

Limited medical errors makes for good support for decision-making within clinics. Due to lack of of-of paper storage records, access to patient records is easier, there is more efficient and coordinated medical care. EHR provides health care convenience through provider and patient communication and interaction. Provision of real-time, complete and accurate patient information at the clinic is essential for effective patient services.

Systematized Nomenclature of Medicine (SNOMED CT) is a clinical term that contains standard multilingual terminology used by different health care providers for the interchange of electronic health records. The standard language, SNOMED CT came to life in 1965 as Systematized Nomenclature of Pathology (SNOP) when the College of American Pathologists (CAP) published it to define anatomy and morphology (Wisner et al., 2019). SNOMED CT can be used with Electronic Health Records (EHR) systems to depict applicable clinical data extensively and comprehensively to produce electronic health information.

SNOMED CT is a standard language with the ability to cross-map to other international classifications. Mapping offers a link to a single international classification and another to achieve different benefits (Fengab et al., 2013). Clinical data based on SNOMED CT are re-usable in reporting management and statistical data through other classifications and code systems. SNOMED CT allows retention of data value when crossing to recent formats of databases. With SNOMED CT redundant data entry is minimized as well as a reduction in errors and cost. SNOMED CT provides compatibility between international code systems, terminologies, and classifications.

SNOMED CT has top-level concepts subdivided into different branches, with different concepts that have specific content. Biologic or Pharmaceutical product is a concept that stands for medical products such as paracetamol 100mg capsule. The observable entity is that which symbolizes a result or an answer for example height, age, hair color, etc. It implies a procedure that depicts clinical activities such as injection, radiography, and therapies. The clinical finding is a concept that stands for the result of the assessment, clinical observation and other complications such as acute muscle cramps, asthma, sinuses. The specimen is a concept that symbolizes samples from a patient during analysis or examination e. stool or urine

SNOMED CT is a terminology that enables providers to access multiple terms within rules through computer processing. There is a possibility of creating infinite coded terms that are known as post-coordinated terms. A medical professional can be expressive as they want when using SNOMED CT, as opposed to paraphrasing their notes. Through the multiple encoded electronic data, there will be a boost in decision support and intercommunication becomes more secure and clear.

Logical Observation Identifiers Names and Codes (LOINC)

Logical Observation Identifiers Names and Codes (LOINC) is an extensive clinical terminology with a set of codes and names for representing health records, observations, and measurements. The first publishing of LOINC was in 1994 as a solution to the rising demand for health care providers for a unifying electronic database to offer free service to extensive medical information (Wisner et al., 2019). LOINC contains terms for lab tests and results, clinical tests like blood pressure, standard survey tools and extra patient tests.

Similarities between LOINC and SNOMED CT Terminologies

Both terminologies are available for free for both use and distribution, all you need to do is sign up at their official websites. Both LOINC and SNOMED CT are coding standards widely-used in the Health and IT field, particularly within the medical examination. Both coding terminologies provide a standard language for use by medical providers to avoid redundant data entry and error when accessing patient information. Both these codes play a role in advancement within the health care sector through the use of Electronic Health Records (EHR). A challenge for both of the terminologies is the exhaustive setup of defining all the terminologies to ensure accuracy before incorporating data into the database.

Difference between LOINC and SNOMED CT Terminologies

The codes in LOINC represent the measurement or a question for a test. The codes in SNOMED CT, however, have virtually an infinite representation which allows providers to make clinical notes without restrictions. The coding in LOINC is split into two sections, Clinical and Laboratory. SNOMED CT is split into four parts which are relationships, concept codes, reference sets, and descriptions. LONC is an extensive electronic medical encyclopedia that allows access to patient's data by medical professionals. SNOMED CT is a real-time terminology that provides accurate information when a patient is being handled. SNOMED CT likewise has automated reminders and alerts.

A Case Study of Application of SNOMED CT

SNOMED CT terminology was picked by Sanitas Hospital, a hospital in Spain to switch from free text records of patients with allergy to a computerized system. The medical institution picked SNOMED CT because of its extensive, multilingual capability. The primary objective of the project was to transfer all the traditionally recorded patient data to a real-time search widget with SNOMED CT as the coding language. The search widget was to allow clinical providers to clean and normalize the data through the software ITServer and then map it to SNOMED CT through the amapset editor of the IT Server

A Case Example of LOINCLogical Observation Identifiers Names and Codes (LOINC) is a coding language for clinical records and it can assist with a large number of records that go through the Hospital Information Systems (HIS). Due to the increased demand for networking among health systems, it is necessary for, interexchange of documents between varying H.I.S. For effective compatibility of varying H.I.S systems, local documents should be cross-linked with the international standard code.

References

Fengab, R.-C., Tsenga, K.-J., Yana, H.-F., Huanga, H.-Y., & Chang, P. (2013). A preliminary study on the use of clinical care classification in nursing documentation data sets. Computer Methods and Programs in Biomedicine, 112(3), 713-719. doi.org/10.1016/j.cmpb.2013.07.019

Wisner, K., Lyndon, A., & Chesla, C. (2019). The electronic health record's impact on nurses' cognitive work: An integrative review. International Journal of Nursing Studies, 94(1), 74-84. doi.org/10.1016/j.ijnurstu.2019.03.003