Developing a SNOMED CT-based value set to document symptoms and diagnoses for adverse drug events (Preprint)

BACKGROUND Adverse drug events (ADE) lead to over two million emergency department visits in Canada annually, resulting in significant patient harm and over $1 billion in healthcare costs. Effective documentation and sharing of ADE information through electronic medical records (EMRs) is essential to inform subsequent care and improve safety when culprit medications can be replaced and re-exposures avoided. Yet current systems often lack standardized comprehensive ADE value sets. OBJECTIVE This study aimed to develop a SNOMED CT value set for symptoms and diagnoses to standardize ADE documentation and improve ADE data integration into EMRs. METHODS We used ADE data from ActionADE, a prospective reporting system implemented in nine hospitals in British Columbia. We extracted 5,792 reports that yielded 827 unique ADE symptom and diagnosis terms based on MedDRA preferred terms. Two independent mappers employed both automated and manual mapping approaches to match these terms to SNOMED CT concepts. Two clinical experts conducted validation, followed by a quality assurance review by a separate clinical team. Discrepancies were resolved through consensus discussions. Interrater reliability was assessed using Cohen’s kappa. RESULTS The automated mapping process identified 63.8% (528/827) semantically equivalent matches from SNOMED CT’s Clinical Finding hierarchy. Two mappers manually reviewed the automatically mapped terms and identified appropriate target concepts for the unmapped terms. After the manual mapping process, 95.3% (788/827) of the source terms were successfully mapped to SNOMED CT concepts, with 4.7% (39/827) remaining unmapped. Interrater reliability between the mappers was strong (κ = 0.87, 95% CI: 0.85-0.89). The validation phase identified and removed one irrelevant term, resulting in 98.9% (778/826) terms mapped, with 1.6% (9/826) unmapped, and a high interrater reliability (κ = 0.88, 95% CI: 0.80-0.95). During quality assurance, six terms were flagged for concerns regarding clinical relevance or safety risks and were resolved through discussions. The final value set comprised 813 SNOMED CT concepts, with 95.0% of terms classified as semantically equivalent. Thirteen additional terms remained unmapped and will be reviewed as new SNOMED CT codes are added. CONCLUSIONS This study developed a SNOMED CT-based value set to document symptoms and diagnoses for adverse drug events observed in adults in EMRs. Adopting this value set can improve the consistency, accuracy, and interoperability of ADE documentation in EMRs, helping to reduce repeat ADEs and enhance patient safety. Ongoing refinement and improved clinical usability are essential for its widespread adoption. Future research should assess the impact of integrating this value set into EMRs on ADE reporting, pharmacovigilance, and patient safety outcomes.