Pediatric Inpatient Nurses (RN)
Great Value Hiring · United States · 1 wk ago
RemoteRemoteHealthcare$55–$65/hrContract
About the role
Experienced Pediatric Inpatient Nurses (RNs) to apply their frontline pediatric bedside experience to improve the accuracy, safety, and reliability of medical AI tools.
Responsibilities
- Review and evaluate AI-generated clinical outputs based on nursing flowsheet documentation
- Validate accuracy, completeness, and adherence to current documentation standards
- Annotate and structure inpatient pediatric nursing assessment data for AI training datasets
- Provide expert feedback on nursing assessments and documentation practices
- Identify gaps, inconsistencies, or risks in AI-generated responses
- Ask clarifying questions when annotation guidance is ambiguous, and contribute to guideline refinement
- Collaborate with technical teams to improve model performance
- Contribute to the development of high-quality clinical benchmarks
Qualifications
- Active RN license (U.S., outside California)
- Recent pediatric acute care inpatient bedside experience, ideally in a non-procedural inpatient unit
- Worked in an inpatient bedside role within the past 10 years, with current or more recent documentation experience prioritized over total years of nursing experience
- Comfortable performing and documenting comprehensive pediatric nursing assessments
- Comfortable following detailed annotation guidelines consistently
- Excellent written communication skills and responsiveness to feedback
- Ability to work independently and meet deadlines
Preferred Qualifications
- Epic EHR experience (most common platform, closely aligns with many workflows)
- Comfortable learning new annotation tools and web-based platforms
- Able to navigate transcripts efficiently and use AI-assisted tools appropriately while still verifying outputs
- Experience reviewing charts for quality improvement, utilization review, CDI, or clinical informatics
- Previous annotation, chart abstraction, or healthcare AI experience
- Comfortable discussing edge cases and contributing to guideline refinement