Research Assistant - Clinical Data & Patient Engagement

About the role

The Research Assistant supports the SIMFONI programme by extracting, validating and managing clinical data from electronic medical records, working with clinicians, researchers, data scientists and project teams to prepare high-quality, accurate and compliant data for AI and machine learning projects. The role also involves significant patient-facing work, acting as a liaison between patients and the research team.

Key responsibilities

• Extract structured and unstructured clinical data from EMRs, clinical notes, medication records and discharge summaries, following data dictionaries and project protocols.
• Validate and check data quality, resolve discrepancies and keep audit trails of extracted and corrected data.
• Prepare datasets for AI and ML models: label and annotate data, categorize and code it, and document metadata.
• Coordinate data collection, track timelines and deliverables, prepare progress reports and schedule meetings between clinical and technical teams.
• Support patient recruitment, screening, enrolment and follow-up, and coordinate appointments and study visits.
• Ensure compliance with patient confidentiality, hospital data governance and regulatory requirements, and support ethics submissions and approvals.
• Maintain documentation of methods and workflows, create SOPs, generate quality reports and assist with stakeholder presentations.
• Liaise with clinicians, nurses, researchers and IT teams, and help train team members on data abstraction.

Required profile

• Degree in Health Informatics, Data Science, Biomedical Informatics or a related field.
• 2 to 5 years of healthcare, clinical research, medical records or health informatics experience.
• Experience reviewing and interpreting medical records, EMR/EHR systems, and healthcare data collection or clinical documentation review.
• Preferred: experience with AI, machine learning, digital health or analytics projects; clinical databases or registries; clinical research or outcomes research; data annotation and labeling.

Required skills

• Medical record abstraction, data extraction and validation.
• Proficiency in Microsoft Excel; familiarity with EMR systems such as Epic or equivalent.
• Knowledge of ICD, SNOMED CT, CPT or other clinical coding systems; basic SQL is advantageous.
• Familiarity with REDCap, SPSS, Power BI, Tableau or similar tools; understanding of AI and machine learning concepts is desirable.
• Understanding of clinical terminology, disease processes, laboratory tests, medications and clinical workflows.