Inpatient Physician for AI Training Data QA

Marketframe; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

Marketframe is seeking a physician with hospitalist experience to contribute to an AI training project focused on inpatient care and documentation standards. You will review medical records, including FHIR data and clinical notes, to ensure information is accurate and consistently applied across cases.

The role involves evaluating inpatient documentation such as HPI, PMH, medication reconciliation, social history, assessments, and plans, and sharing feedback to improve AI models and data

Qualifications

• Minimum 5 years of hospitalist experience.
• MD or DO with board certification in Internal Medicine.
• Recent inpatient hospitalist experience (within 2 years).
• Strong understanding of inpatient documentation and hospital workflows.
• Experience with chart review, clinical documentation improvement, utilization review, or medical coding/QA is a plus.

Responsibilities

• Review medical records, including FHIR data and clinical notes, for AI training.
• Check whether clinical information and claims are supported by the medical record.
• Review inpatient adult medicine documentation (HPI, PMH, meds reconciliation, social history, assessments, plans).
• Follow guidelines and apply the same standards consistently.
• Identify missing or inconsistent information in medical records and provide feedback to improve AI models.

Skills

Hospitalist experience

Inpatient documentation understanding

Attention to detail

Clinical notes review

Education

MD or DO, board certified in Internal Medicine

Job description

Marketframe is seeking a physician with hospitalist experience to contribute to an AI training project focused on inpatient care and documentation standards. You will review medical records, including FHIR data and clinical notes, to ensure information is accurate and consistently applied across cases.

The role involves evaluating inpatient documentation such as HPI, PMH, medication reconciliation, social history, assessments, and plans, and sharing feedback to improve AI models and data