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Medical/Clinical Coding Specialists (SNOMED) Required for AI Project
About the job Medical/Clinical Coding Specialists (SNOMED) Required for AI Project
Job Title: Medical/Clinical Coding Specialists (SNOMED) Required for AI Project
Job Location: London, UK
Job Location Type: Remote
Job Contract Type: Contract
Job Seniority Level: Entry level
We are seeking medical or clinical coders to assist in coding medical questionnaires to SNOMED codes on a dedicated labeling platform.
You will work asynchronously to code the de-identified data from patient questionnaires, helping train and refine AI models for automated clinical coding.
Key Responsibilities:
Questionnaire Coding & Mapping: Map each patient questionnaire item (5–15+ per questionnaire) to SNOMED codes, including medications, conditions, allergies, reactions, side effects, and severity indicators.
Quality Assurance & Data Structuring: Ensure consistent and accurate application of medical codes following provided guidelines and project standards.
FHIR Data Integration: Support mapping of coded data to FHIR-compliant structures (training and guidance provided).
Platform-Based Labeling: Use a dedicated labeling platform to review, code, and validate data efficiently.
Feedback & Collaboration: Provide feedback to improve coding guidelines, identify ambiguities in source data, and improve AI-assisted mappings.
Qualifications:
Medical coding experience: Minimum 1 year of professional experience in medical coding.
Coding knowledge: Demonstrated experience in SNOMED coding; if not, a strong grasp of standard medical coding systems (ICD, CPT, LOINC, etc.) and principles is required.
Attention to detail: Proven track record of accuracy and consistency in clinical data coding.
Technical proficiency: Comfortable using structured data labeling tools and digital platforms.
Language: Professional working English proficiency for written deliverables.
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: Short-term, fully remote project (estimated duration 1–2 months).
Competitive Compensation: Hourly rate paid out weekly
Collaborative Environment: Be part of a multidisciplinary initiative advancing automation in clinical coding.
You will work asynchronously to code the de-identified data from patient questionnaires, helping train and refine AI models for automated clinical coding.
Key Responsibilities:
Questionnaire Coding & Mapping: Map each patient questionnaire item (5–15+ per questionnaire) to SNOMED codes, including medications, conditions, allergies, reactions, side effects, and severity indicators.
Quality Assurance & Data Structuring: Ensure consistent and accurate application of medical codes following provided guidelines and project standards.
FHIR Data Integration: Support mapping of coded data to FHIR-compliant structures (training and guidance provided).
Platform-Based Labeling: Use a dedicated labeling platform to review, code, and validate data efficiently.
Feedback & Collaboration: Provide feedback to improve coding guidelines, identify ambiguities in source data, and improve AI-assisted mappings.
Qualifications:
Medical coding experience: Minimum 1 year of professional experience in medical coding.
Coding knowledge: Demonstrated experience in SNOMED coding; if not, a strong grasp of standard medical coding systems (ICD, CPT, LOINC, etc.) and principles is required.
Attention to detail: Proven track record of accuracy and consistency in clinical data coding.
Technical proficiency: Comfortable using structured data labeling tools and digital platforms.
Language: Professional working English proficiency for written deliverables.
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: Short-term, fully remote project (estimated duration 1–2 months).
Competitive Compensation: Hourly rate paid out weekly
Collaborative Environment: Be part of a multidisciplinary initiative advancing automation in clinical coding.
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