The importance of SNOMED CT concept specificity in healthcare analytics

Abstract

Background Healthcare data frequently lack the specificity level needed to achieve clinical and operational objectives such as optimising bed management. Pneumonia is a disease of importance as it accounts for more bed days than any other lung disease and has a varied aetiology. The condition has a range of SNOMED CT concepts with different levels of specificity. Objective This study aimed to quantify the importance of the specificity of an SNOMED CT concept, against well-established predictors, for forecasting length of stay for pneumonia patients. Method A retrospective data analysis was conducted of pneumonia admissions to a tertiary hospital between 2011 and 2021. For inclusion, the primary diagnosis was a subtype of bacterial or viral pneumonia, as identified by SNOMED CT concepts. Three linear mixed models were constructed. Model One included known predictors of length of stay. Model Two included the predictors in Model One and SNOMED CT concepts of lower specificity. Model Three included the Model Two predictors and the concepts with higher specificity. Model performances were compared. Results Sex, ethnicity, deprivation rank and Charlson Comorbidity Index scores (age-adjusted) were meaningful predictors of length of stay in all models. Inclusion of lower specificity SNOMED CT concepts did not significantly improve performance (ΔR2 = 0.41%, p = .058). SNOMED CT concepts with higher specificity explained more variance than each of the individual predictors (ΔR2 = 4.31%, p < .001). Conclusion SNOMED CT concepts with higher specificity explained more variance in length of stay than a range of well-studied predictors. Implications Accurate and specific clinical documentation using SNOMED CT can improve predictive modelling and the generation of actionable insights. Resources should be dedicated to optimising and assuring clinical documentation quality at the point of recording.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

“…Result labels Data standardization. Its primary function is to convert data from varying ranges and scales into a uniform standard format, such as FHIR HL7 [38], SNOMED CT [39] and DICOM [40], thus making data more suitable for machine learning and statistical analysis.…”

Clinical documentation integrity: Its role in health data integrity, patient safety and quality outcomes and its impact on clinical coding and health information management

“…ICD-10 and its Australian derivative, ICD-10-AM). The concept of specificity refers to the level of detail observable within data and is considered an “essential characteristic of high-quality data” (Roberts et al, 2023: 8). While specificity is recognised as important, it does not commonly feature in administrative data, often necessitating use of multiple data sources or data linkage for research and analytics (Duke et al, 2022; Kilkenny et al, 2022; Palamuthusingam and Pascoe, 2023; Roberts et al, 2023).…”