Standardising the structure of routinely collected data for childhood ocular inflammation: a SNOMED-CT mapping project

Background and aims Multicentre, longitudinal research methods are usually necessary for rare disease research. SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), the comprehensive and standardized terminology system can be used to enhance the interoperability of data collected across different settings. Childhood uveitis is a rare, blinding disorder, with uncertainties around disease distribution and outcome. To enhance the interoperability of uveitis data, we created a SNOMED CT coded dataset derived from a core clinical dataset. Methods Data elements were selected from a published list developed through a consensus exercise undertaken by a national disease research group, the United Kingdom’s Paediatric Ocular Inflammatory Group (POIG). Items were organised using a three level priority score, based on the National Institute for Health (NIH) model for common data elements, and grouped using the Heath Level 7 (HL7) standard "Fast Healthcare Interoperability Resources" (FHIR) generic data structure, and then mapped across to the SNOMED CT codes. Results From the POIG consensus exercise, 160 elements were selected: 89 as high priority items, with 35 as medium and 29 as low priority items. These elements, and response items where appropriate, were grouped into Patient (n= 13 items), Observation (n= 63 items), Condition (n= 20 items), Procedure (n= 44 items), Medication (n= 18 items). There were four items for which a SNOMED CT ID could not be found. Conclusion Through this mapping activity, using international coding and terminologies, we have created a dataset for childhood onset uveitis care and research. This dataset provides a standardised vocabulary for describing clinical concepts, with a semantic interoperability which will support the exchange of data across different systems, organizations, and international or supranational groups. Future expansion of the dataset will be needed to ensure coverage of international concepts and care structures. Author summary Through a mapping activity, in which data items from a consensus developed core clinical dataset were mapped across to SNOMED CT terminologies, we have created a dataset for childhood onset uveitis care and research. This dataset provides a standardised vocabulary for describing clinical concepts, with a semantic interoperability which will support the exchange of data across different systems, organizations, and international or supranational groups.