Hospital Medicine Physician AI Trainer

The work

You will review inpatient medical records and clinical documentation used to train AI systems. The work involves checking whether clinical claims are supported by the record and giving clear feedback on accuracy, completeness, and consistency.

• Review FHIR data, clinical notes, and other inpatient record information.
• Assess claims against the underlying medical record using written review guidelines.
• Evaluate HPI, past medical history, medication reconciliation, social history, assessments, and plans.
• Identify gaps involving renal- and hepatic-adjusted dosing, medication interactions, and age-appropriate clinical details.
• Review documentation across admissions, daily progress notes, transitions of care, and discharge workflows.

What it pays and takes

• Pay: $80 to $125 per hour equivalent, paid per task that meets the project specifications.
• Work type: Part-time contract role.
• Location: Open to candidates in the United States.
• Language: Fluent in English.
• Education: MD or DO required.
• Certification: Board certification in Internal Medicine or an Internal Medicine hospital medicine focused practice designation required.
• Experience: At least five years of hospitalist experience and current or recent inpatient hospitalist practice within the last two years required.
• Skills: Strong adult inpatient documentation and hospital workflow knowledge, comfort reviewing clinical notes and raw medical record data, and careful use of written guidelines.
• Helpful background: Chart review, clinical documentation improvement, utilization review, medical coding, clinical quality assurance, medication safety, or evaluating support for clinical claims.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work uses human review to help artificial intelligence understand and produce better results. Medical experts are needed to check clinical information, identify errors, and improve the quality of systems used with specialized content.