Senior Clinical Data AI Reviewer
J
JobgetherHealthcare Technology
CanadaPart-TimeSenior
Salary$60.87 to $67.64 CAD per hour
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Job Details
- Experience
- 7+ years
- Required Skills
- Artificial IntelligenceMachine LearningNLPLLMEHR
Requirements
- Registered Nurse (RN) or Nurse Practitioner (NP) degree with a valid current license.
- 7+ years of nursing experience, with preference for long-term post-acute care and/or healthcare management experience.
- Strong understanding of healthcare workflows, clinical documentation, and healthcare informatics concepts.
- Familiarity with artificial intelligence concepts, including large language models (LLMs), natural language processing (NLP), machine learning (ML), hallucinations, and model limitations.
- Experience using healthcare technology platforms and electronic health record systems.
- Experience with data labeling tools or clinical annotation processes.
- Ability to perform detailed data review and manipulation tasks using computer-based tools for extended periods.
- Understanding of statistical concepts used to measure annotation consistency and inter-reviewer agreement.
- Ability to quickly learn new software, technologies, and technical concepts.
- Strong analytical skills with the ability to categorize nuanced clinical information accurately.
Responsibilities
- Review and label clinical data to ensure accuracy, consistency, and alignment with defined quality standards.
- Support subject matter expert evaluations of clinical annotations and assess AI model performance and results.
- Provide clinical expertise related to healthcare workflows and electronic health record documentation practices.
- Help resolve disagreements and discrepancies between clinical reviewers to improve annotation quality and consistency.
- Collaborate with research, product, and technical teams to define requirements and communicate complex clinical concepts clearly.
- Advocate for clinical quality and accuracy by partnering with AI researchers, product leaders, and user experience teams.
- Evaluate AI-generated outputs and identify issues such as omissions, inaccuracies, or other model limitations.
- Contribute insights that help improve AI solutions and their practical application in healthcare settings.
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