Find An Article That Covers The Content Coverage Of SNOME

Find an article that addresses the content coverage of SNOMED CT Af

Find an article that addresses the content coverage of SNOMED CT. Af

Evaluate the sufficiency of SNOMED Clinical Terms (SNOMED CT) in representing comprehensive patient health records. SNOMED CT is a rich, multilingual clinical healthcare terminology that facilitates the recording and sharing of medical data across different health information systems. Analyzing recent scholarly articles, such as those by Chute et al. (2013), reveals that SNOMED CT covers a broad spectrum of clinical concepts, including diseases, procedures, and findings, ensuring detailed clinical documentation. However, despite its extensive coverage, some limitations persist, such as gaps in capturing emerging medical conditions or nuanced clinical details specific to certain specialties (Stevenson et al., 2019). Additionally, integration challenges with other terminologies like LOINC or ICD can hinder comprehensive interoperability. Overall, SNOMED CT provides a robust backbone for health data representation, but its sufficiency depends on ongoing updates and integration efforts to encompass all information within a patient’s health record. Continuous enhancements and harmonization with other standards remain essential for full coverage.

Paper For Above instruction

SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms) plays a critical role in standardizing clinical terminology to improve the accuracy, efficiency, and interoperability of electronic health records (EHRs). Its goal is to represent all necessary clinical information comprehensively; however, whether it can entirely encapsulate a person's complete health record remains a nuanced question. Recent literature, including analyses by Chute et al. (2013), emphasizes SNOMED CT's extensive content coverage, which includes over 300,000 concepts covering diseases, procedures, findings, and social contexts. This extensive repository enables detailed documentation of a patient's health status, diagnostics, treatments, and outcomes, fostering effective clinical communication and data sharing.

Nonetheless, despite its comprehensiveness, SNOMED CT does not fully cover every aspect of a person’s health record. For example, specialized medical fields like genetics or emerging infectious diseases may require the addition of new concepts, leading to temporal gaps in coverage. Furthermore, capturing the contextual nuances and individual patient preferences remains challenging within the structured terminology. The integration of SNOMED CT with other terminologies such as LOINC (Logical Observation Identifiers Names and Codes), which is focused on laboratory and clinical observations, enhances the overall representation capacity but also introduces complexity. To truly represent all health information, SNOMED CT must be continually updated and synchronized with other coding standards, with efforts to improve semantic interoperability.

In conclusion, SNOMED CT provides a solid foundation for comprehensive clinical documentation, but its sufficiency is context-dependent and requires ongoing development and integration with complementary terminologies. Realizing fully comprehensive health records necessitates dynamic, evolving terminologies that adapt to medical advancements, support detailed clinical narratives, and facilitate interoperability across health information systems (Stevenson et al., 2019).

References

  • Chute, C. G., Kush, R., Maly, R., & Campbell, T. (2013). SNOMED CT and the future of clinical documentation. Journal of the American Medical Informatics Association, 20(4), 742–747.
  • Stevenson, M., Lane, S., & Brown, D. (2019). The role of SNOMED CT in advancing health data interoperability. International Journal of Medical Informatics, 128, 89–94.
  • De Lusignan, S., et al. (2014). SNOMED CT in primary care: A systematic review. BMJ Open, 4(9), e005358.
  • Huser, A., et al. (2020). Challenges and opportunities in semantic interoperability with SNOMED CT. Journal of Biomedical Informatics, 102, 103359.
  • Feldman, B. J., & Kohn, M. (2019). Mapping SNOMED CT concepts to clinical data repositories. Applied Clinical Informatics, 10(3), 462–469.
  • Rossi, A., et al. (2018). Knowledge representation and SNOMED CT: A review. Journal of Medical Systems, 42(11), 204.
  • Benson, T., et al. (2017). Using SNOMED CT for clinical decision support. Journal of Health Informatics Research, 1(2), 113–122.
  • Hollingworth, J., et al. (2021). Enhancing health record completeness with SNOMED CT. Health Data Management, 36(4), 229–236.
  • Brandt, C., et al. (2019). The impact of SNOMED CT updates on clinical workflows. BMC Medical Informatics and Decision Making, 19, 57.
  • Walker, S., & Kearns, P. (2020). Integrating SNOMED CT with other healthcare standards: Strategies and challenges. Journal of Healthcare Engineering, 2020, 1–12.

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