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Sourced from Aptura's careers page

Medical Doctor with SNOMED Coding Experience (UK)

Aptura

United KingdomRemoteContract

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We are seeking qualified medical doctors to annotate and code clinical documents with SNOMED CT, with a focus on accuracy and consistency against detailed coding guidelines. You will work asynchronously on a dedicated annotation platform, coding de-identified clinical notes and documentation including discharge summaries, lab results and progress notes against SNOMED CT concepts to support the development of clinical AI platforms. This is a remote, flexible and short-term project. KEY RESPONSIBILITIES • Structured Annotation: Identify clinically relevant concepts across discharge summaries, lab results and prognosis notes, and map them to the correct SNOMED CT codes on a dedicated platform. • Guideline Application: Work to a coding guideline covering hierarchy scope, negation, medication scope, section-level inclusions and compound coding, applied consistently across a large volume of documents. • Edge Case Handling: Flag ambiguous or contested cases with a short rationale so guidance can be refined. • Quality Review: Participate in adjudication rounds to resolve disagreements and reach a consensus code set. QUALIFICATIONS: License: GMC Registered Doctor based in the UK Experience: Minimum 1–2 years of clinical practice, with SNOMED CT coding or clinical terminology experience. Knowledge: Strong grounding in clinical documentation, terminology and coding conventions. Attention to detail: Proven accuracy and consistency in coding or clinical review work. Technical proficiency: Comfortable using structured annotation tools and digital platforms. Language: Native or near-native written English. LEGAL STATUS You will have the right to work in your country of residence. You will work as an independent contractor. WHY JOIN US? • Flexible Work Arrangements: Part-time, remote and fully asynchronous. • Competitive Compensation: Hourly rate, paid weekly • Professional Development: Hands-on experience in clinical AI development and terminology work, shaping how AI systems interpret clinical records and assign medical codes.

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