Medical Professional / Doctor (FY1-FY3)
Remote
Part-time / Contract
Competitive hourly rate
Company Description
Mentis AI is working with leading startups, enterprises and AI labs to advance the frontier of AI. With a particular focus on healthcare and medical documentation.
Role Overview
We are seeking UK-based medical coders or GMC-registered medical professionals at Foundation Year 1 or 3 level to assist in labeling and annotating clinical consultation data. This role involves analyzing and structuring anonymized patient consult notes to ensure accuracy, clarity, and standardization. This work contributes to the development of high-quality, cutting-edge medical documentation solutions.
Key Responsibilities
- Clinical Data Annotation: Examine and label key medical elements within anonymized consult notes (e.g., history of present illness, relevant exam findings, diagnoses, treatment plans) to ensure alignment with NHS documentation standards.
- Expert Clinical Insights: Provide context-driven interpretation of medical content to improve medical scribe and documentation solutions.
Qualifications
- Profile: Medical Coder with experience in clinical coding (SNOMED CT) within the UK healthcare system or GMC-registered Medical Professional with MBBS or equivalent, currently at Foundation Year 1 or 2 (FY1-FY2) level in the UK healthcare system.
- Clinical Knowledge: Strong understanding of medical terminology, clinical judgment, and NHS documentation practices, including SNOMED CT and structured clinical coding.
- Attention to Detail: Ability to meticulously review text-based data, ensuring consistent, clinically relevant, and correct labeling.
Why Join Us?
- Flexible Work Arrangements: Part-time and remote work options available (5-20 hours a week) to accommodate professional schedules (e.g., clinicians balancing clinical practice with additional projects).
- Competitive Compensation: Hourly compensation, commensurate with level of clinical expertise.
- Professional Development: Gain hands-on experience in data quality, structured data modeling, and medical informatics, with training provided on advanced labeling methodologies.