The Role of SNOMED CT Concept Specificity in Healthcare Analytics

Francis June 13, 2023

Our understanding of healthcare analytics has evolved rapidly in recent years, especially as we leverage the potential of big data and artificial intelligence. One critical aspect of healthcare data is the specificity of diagnoses, which can significantly impact analytics outcomes. A study published in the Journal of Health Information Management delves into this fascinating subject.

What is SNOMED CT?

SNOMED CT is a globally recognised, structured clinical vocabulary used in electronic health records to ensure consistent terminology. It has different ‘concepts’ or categories that range in specificity, which essentially means how detailed or general the categorisation is. For instance, when considering the disease pneumonia, there are multiple SNOMED CT concepts that might apply, each with varying degrees of specificity.

The Study

In a research study conducted by Roberts et al. (2022), the authors aimed to quantify the importance of SNOMED CT concept specificity for forecasting the length of hospital stay for pneumonia patients. The investigation analysed pneumonia admissions to a tertiary hospital between 2011 and 2021, with inclusion criteria specifying a primary diagnosis of a subtype of bacterial or viral pneumonia identified by SNOMED CT concepts.

Three linear mixed models were constructed:

  1. Model One included known predictors of length of stay.
  2. Model Two included the predictors in Model One and lower specificity SNOMED CT concepts.
  3. Model Three incorporated the Model Two predictors and higher specificity SNOMED CT concepts.

Findings and Implications

The study found that sex, ethnicity, deprivation rank, and Charlson Comorbidity Index scores (age-adjusted) were significant predictors of the length of stay in all models. However, the addition of lower specificity SNOMED CT concepts did not significantly improve performance.

On the other hand, SNOMED CT concepts with higher specificity explained more variance in the length of stay than each of the individual predictors. This result emphasises the critical role that accurate and specific clinical documentation can play in improving predictive modelling and generating actionable insights.

For healthcare organisations and professionals, the message is clear: resources should be dedicated to optimising and assuring clinical documentation quality at the point of recording.

By recognising the value of highly specific SNOMED CT concepts in predicting outcomes, we can improve the quality of healthcare data and, in turn, the efficacy of our predictive analytics. This advancement may allow healthcare providers to optimise resources, like bed management, ultimately leading to improved patient care.

Reference: Roberts, L., Lanes, S., Peatman, O., & Assheton, P. (2022). The importance of SNOMED CT concept specificity in healthcare analytics. Journal of Health Information Management. DOI: 10.1177/18333583221144662

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Francis
Francis is an accredited clinical coding educator with over a decade of clinical coding experience in the UK. Passionate about the transformative power of e-learning, Francis blends this expertise with an innovative approach to education as a TAP Learning Practitioner. The mission is not just to educate but to inspire, fostering a community of lifelong learners in the dynamic world of clinical coding. With a commitment to improving the industry through education, Francis’s work is marked by an engaging, interactive, and inclusive approach, making every day an opportunity to learn, grow, and redefine what’s possible.