The work
You will review inpatient medical records for AI training. This includes FHIR data, a structured format for health information, along with clinical notes and other record details.
You will use clinical judgment and defined review standards to assess documentation quality, identify unsupported claims, and give clear feedback that helps improve AI systems and data quality.
- Review FHIR data, clinical notes, and other inpatient medical record information.
- Check whether clinical claims are supported by the underlying record.
- Evaluate histories of present illness, past medical histories, assessments, and plans.
- Identify missing, incorrect, or inconsistent information in inpatient documentation.
- Apply review guidelines consistently across records and findings.
- Communicate review conclusions clearly to the project team.
What it pays and takes
The work requires substantial clinical experience and careful, consistent review of inpatient documentation.
- Pay: $70 to $115 USD per hour.
- Work type: Part-time contract work.
- Location: Open to candidates in the countries specified for this role.
- Language: Professional fluency in English.
- Education: MD or DO degree.
- Certification: Board certification in Internal Medicine, Pediatrics, or Hospital Medicine.
- Experience: Current or recent inpatient or hospitalist experience within the last two years.
- Skills: Strong knowledge of inpatient documentation and hospital workflows; comfort reviewing clinical notes, FHIR data, and raw medical records; and strong attention to detail.
- Helpful background: Chart review, clinical documentation improvement, utilization review, medical coding, quality assurance, or work across multiple inpatient services or specialties.
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 it 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. People review records, label information, and evaluate model outputs so AI systems can learn from accurate examples; experienced professionals are needed when the work depends on specialized medical judgment.