Inpatient Medical Documentation AI Training Specialist

You will review adult inpatient records and clinical documentation used to improve AI systems. The work requires medical judgment when checking whether clinical claims are supported by the underlying record.

• Review FHIR data, clinical notes, and other medical-record information for AI training.
• Evaluate HPI, past medical history, medication reconciliation, social history, assessments, and plans.
• Identify missing, incorrect, or inconsistent details in inpatient documentation.
• Check medication interactions, renal- and hepatic-adjusted dosing, and age-appropriate clinical considerations.
• Apply review guidelines consistently and provide structured feedback on model behavior and data quality.
• Assess documentation quality and classify or rate clinical information according to project standards.

What It Pays and Takes

This is part-time contractor work for medical professionals contributing expertise to an AI training project.

• $80 to $125 per hour equivalent, paid per task that meets the project specifications.
• Open to candidates in the United States.
• Professional fluency in English.
• MD or DO with board certification in Internal Medicine.
• Internal Medicine physicians with a Hospital Medicine focused practice designation may qualify.
• At least five years of hospitalist experience.
• Current or recent inpatient hospitalist experience within the last two years.
• Strong knowledge of admission, daily progress notes, transitions of care, and discharge workflows.
• Comfort reviewing clinical notes and raw medical-record data.
• Close attention to detail and the ability to follow defined standards consistently.
• Experience with chart review, clinical documentation improvement, utilization review, medical coding, or clinical quality assurance is helpful.
• Listed experience level: entry level.

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 medical records and rating model responses. Experienced professionals are needed for specialist projects because accurate feedback helps models handle complex information more reliably.