The Work You will review adult inpatient records and clinical documentation used to improve AI systems. The work requires hospitalist-level judgment to assess documentation quality, verify claims against the medical record, and give structured feedback on model reasoning.

• Review FHIR data, clinical notes, and other medical-record information.
• Check whether clinical information and claims are supported by the underlying record.
• Evaluate history of present illness, past medical history, medication reconciliation, social history, assessments, and care plans.
• Find missing, incorrect, or inconsistent details in inpatient documentation.
• Review medication interactions, kidney- and liver-adjusted dosing, and age-appropriate clinical considerations.
• Apply review guidelines consistently and provide feedback that improves model behavior and data quality.

What It Pays and Takes This is a part-time contractor role for a medical professional contributing expertise to an AI training project.

• Pay: $80 to $125 per hour.
• Location: Open to candidates in the United States.
• Language: Professional fluency in English.
• Education and certification: MD or DO with board certification in Internal Medicine.
• Practice background: Internal Medicine physicians with a Hospital Medicine focused practice designation are eligible.
• Experience required: At least five years of hospitalist experience and current or recent inpatient hospitalist experience within the last two years.
• Clinical knowledge: Strong understanding of admissions, daily progress notes, transitions of care, and discharge workflows.
• Work skills: Comfort reviewing clinical notes and raw medical-record data, close attention to detail, and consistent use of defined standards.
• Helpful experience: Chart review, clinical documentation improvement, utilization review, medical coding, or clinical quality assurance.

How It Works Apply on OpenTrain with your resume, then complete the application on the hiring site. About AI Training Work AI training is the human work behind systems that learn from examples, including reviewing records, rating outputs, and giving feedback. Medical specialists help improve these systems by applying careful judgment to complex clinical information.