Inpatient Clinical Documentation Reviewer, Registered Nurse
OpenTrain AIThe work
You will review inpatient clinical materials and AI-generated outputs to help improve artificial intelligence systems. Your nursing judgment will guide checks for clinical accuracy, safety, appropriateness, completeness, and fit with real inpatient workflows.
The work includes medical documentation review, dataset enrichment, and clear feedback on guidelines and difficult cases.
- Annotate and review nursing notes, progress notes, care plans, orders, lab results, and discharge summaries.
- Evaluate AI-generated clinical outputs across inpatient populations and care scenarios.
- Use nursing standards of care and inpatient documentation knowledge to validate datasets.
- Identify unclear or ambiguous cases and communicate findings clearly to project stakeholders.
- Provide detailed feedback that improves clinical guidelines, workflows, and model understanding.
What it pays and takes
This is a remote contractor role for candidates based in the United States. The role is part-time, and the work requires professional nursing judgment and careful communication.
- Pay: $60 to $70 per hour.
- Location: Open to candidates based in the United States.
- License: Current Registered Nurse status with eligibility to practice in the United States.
- Experience: Recent inpatient bedside nursing experience.
- Clinical background: Broad exposure to varied inpatient populations and care scenarios.
- Skills: Clear written and spoken communication with close attention to detail.
- Helpful background: Experience in an academic or large health system, such as a teaching hospital.
- Helpful background: Previous annotation experience with positive performance reviews.
How it works
You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing is how you qualify for paid tasks, so take it soon after you are invited.
About AI training work
AI training is the human work behind artificial intelligence systems. People review documents, label data, and assess model responses so these systems can produce more accurate and useful results.