Creating a SNOMED CT reference set for common endocrine disorders based on routine clinic correspondence.

Standardized coding of outpatient clinical outcomes lags behind inpatient care. While inpatient episodes are well-documented, outpatient consultations lack consistent terminology systems. Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) provides a framework for standardized clinical coding. Prior research has shown that SNOMED CT can improve data interoperability and patient record accuracy. However, no prior work had resolved the specific challenge of applying SNOMED CT to endocrinology outpatient settings. That uncertainty drove the need for a focused reference set. No existing tools fully address the need for a curated endocrine-specific SNOMED CT set. This gap motivated the development of a tailored reference set for common endocrine disorders. The lack of such a resource limits the ability to standardize outpatient endocrine care coding. The absence of a validated set for this domain hinders data-driven decision-making in endocrinology.

The aim of the study was to develop a reference set for common endocrine disorders using SNOMED CT. This effort addresses the challenge of inconsistent coding in outpatient endocrinology consultations. The specific problem is the lack of standardized terminology for endocrine outcomes in outpatient settings. The motivation comes from the need to improve data accuracy and interoperability in endocrinology. The study focuses on creating a practical, validated set of SNOMED CT terms for routine use. The goal is to facilitate better data collection for service planning and funding decisions. The study targets a specific audience of endocrinologists and clinical coders. The reference set aims to bridge the gap between clinical practice and standardized coding systems.

The study used a semi-automated extraction approach from clinical correspondence. Data were collected from an adult tertiary outpatient endocrine clinic between 2018 and 2019. A total of 1870 patient records from two regional areas were analyzed. An automated script extracted problem statements from clinical notes. These statements were then manually coded using SNOMED CT disorder concepts. The process identified 298 endocrine diagnoses, findings, and procedures. Consultant endocrinologists validated the most common conditions. A survey assessed agreement on the frequency of each condition's occurrence.

The study identified 298 relevant endocrine diagnoses, findings, and procedures. Eighty-eight (29.5%) of these were commonly seen conditions like Graves' disease. Two hundred ten (70.5%) were less frequently encountered disorders. Consultant endocrinologists validated 28 conditions with 100% agreement. Twenty-five conditions received 90%-99% agreement from experts. Thirty-one had 50%-89% agreement, and four had less than 59%. These low-agreement conditions were excluded from the final reference set. The final set includes disorders with high consensus among specialists.

The study successfully created a SNOMED CT reference set for common endocrine disorders. This set is based on real-world outpatient data from a tertiary clinic. The reference set includes disorders with high agreement among endocrinologists. The authors propose that this tool will improve coding accuracy in endocrinology. They suggest it will support better data collection for service planning and funding. The study does not claim that this set is universally applicable to all endocrine clinics. The authors emphasize the importance of expert validation in the process. They propose that future work may expand the reference set to other specialties.