Research Assistant

The Research Assistant will support SIMFONI programme initiatives by extracting, validating, and managing clinical data from electronic medical records. The role involves working closely with clinicians, researchers, data scientists, and project teams to ensure high-quality, accurate, and compliant healthcare data is prepared for artificial intelligence and machine learning projects.

The role also involves significant patient-facing responsibilities, requiring regular interaction with patients and research participants to support various research activities and study operations. The incumbent will serve as a key liaison between patients and the research team, fostering a positive participant experience through professional, empathetic, and effective communication while ensuring adherence to research protocols, ethical standards, and confidentiality requirements.

The incumbent will maintain data integrity, patient confidentiality, and compliance with healthcare regulations while contributing to the development of AI solutions that improve patient care, operational efficiency, and clinical research.

Key Responsibilities

1. Medical Record Data Extraction

• Review electronic medical records (EMR), clinical notes, medication records, and discharge summaries.
• Extract structured and unstructured clinical data according to predefined data dictionaries and project protocols.
• Identify relevant clinical variables, diagnoses, procedures, medications, laboratory results, and patient outcomes.
• Perform manual and semi-automated chart abstraction for AI model development.
• Ensure completeness and accuracy of extracted information.

2. Data Quality Management

• Conduct data validation and quality checks to ensure accuracy and consistency.
• Identify missing, incomplete, or conflicting information and escalate issues as required.
• Perform discrepancy resolution through record verification.
• Maintain audit trails of extracted and corrected data.

3. AI Dataset Preparation

• Assist in preparing datasets for machine learning and AI model training.
• Label and annotate clinical data according to project requirements.
• Support data categorization, classification, and coding activities.
• Work with data scientists to clarify clinical context and variable definitions.
• Contribute to dataset documentation and metadata management.

4. Project Coordination

• Coordinate data collection activities across stakeholders.
• Track project timelines, milestones, and deliverables.
• Prepare progress reports and update project documentation.
• Schedule meetings and facilitate communication between clinical and technical teams.
• Follow up on data requests and project action items.

5. Patient and Research Participant Engagement

• Serve as a key point of contact for patients and research participants throughout their study journey.
• Support patient recruitment, screening, enrolment, and follow-up activities in accordance with approved research protocols.
• Provide study-related information and respond to participant enquiries in a professional, empathetic, and timely manner.
• Coordinate participant appointments, assessments, and study visits to ensure a positive participant experience.
• Foster strong relationships with patients and research participants while maintaining confidentiality and professionalism.

6. Compliance, Privacy & Governance

• Ensure strict compliance with patient confidentiality requirements.
• Adhere to hospital data governance policies and regulatory requirements.
• Support ethics submissions, approvals, and project documentation where applicable.
• Handle patient information securely and responsibly.
• Follow institutional policies on data access and usage.

7. Documentation & Reporting

• Maintain comprehensive documentation of extraction methodologies and workflows.
• Create standard operating procedures (SOPs) and work instructions.
• Generate reports on extraction progress, data completeness, and quality metrics.
• Assist with presentations and project updates for stakeholders.

8. Stakeholder Collaboration

• Liaise with clinicians, nurses, allied health professionals, researchers, and IT teams.
• Provide clarification on clinical documentation and coding requirements.
• Support training of team members involved in data abstraction activities.
• Participate in AI project discussions and workflow improvement initiatives.

Requirements

Education

• Degree in Health Informatics, Data Science, Biomedical Informatics, or related field.

Experience

Essential

• 2 to 5 years of healthcare, clinical research, medical records, or health informatics experience.
• Experience reviewing and interpreting medical records.
• Experience with Electronic Medical Record (EMR/EHR) systems.
• Experience in healthcare data collection, abstraction, or clinical documentation review.

Preferred

• Experience supporting AI, machine learning, digital health, or analytics projects.
• Experience with clinical databases or registries.
• Experience in clinical research or outcomes research.
• Familiarity with data annotation and labeling activities.

Technical Skills

• Medical record abstraction and chart review.
• Data extraction and validation.
• Proficiency in Microsoft Excel.
• Knowledge of healthcare databases.
• Familiarity with EMR systems such as Epic or equivalent.
• Understanding of ICD, SNOMED CT, CPT, or other clinical coding systems.
• Basic SQL knowledge is advantageous.
• Familiarity with REDCap, SPSS, Power BI, Tableau, or similar tools.
• Understanding of AI and machine learning concepts is desirable.

Clinical Knowledge

• Ability to understand clinical terminology.
• Knowledge of disease processes and treatment pathways.
• Understanding of laboratory tests, medications, and diagnostic reports.
• Familiarity with clinical workflows and healthcare operations.