Overview

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This session contains a 40 minute panel.

We implemented a system that detected patients with COVID-19 who may be suitable for anti-viral therapy, by codifying federal government eligibility requirements and evidence-based clinical guidelines into algorithms in the NSW ‘patient flow portal’. We notified the patient’s General Practitioner (GP) by secure message, then notified the patient of this potential treatment, with instructions to make an appointment for GP assessment and self-care advice. GPs then undertook consultations following RACGP guidelines on prescribing oral treatments for COVID-19 – most often via telehealth – and issued prescriptions to 77% of patients. Almost 20,000 patients were connected with GPs over a 3.5 months in mid-2022. The program was effective in influencing desired help-seeking behaviours in consumers and increasing health literacy. Benefits of the program included directing consumers away from tertiary care to primary care or enabling consumers to manage their own care safely. This work has broader application for the use of data analytics to pinpoint patients who may benefit from treatment, connect them with a GP, and support the GP with evidence based guidelines to assist clinical judgement.

SNOMED CT is a powerful clinical terminology used to record coded data in electronic health records (EHRs) around the world. Accessing this power, however, requires an understanding of SNOMED CT’s logical design, its comprehensive clinical content, its concept model rules, and the mechanisms available to leverage SNOMED CT’s unique features. SNOMED CT’s query language – the expression constraint language (ECL) – has been designed to simplify access to SNOMED CT’s rich capabilities, including its ontological definitions, its flexible dialect representations, and its historical audit trails. This tutorial will provide a solid introduction to the key features of ECL, and how these powerful queries can be accessed through HL7 FHIR terminology services.

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