AI-Driven Medical Records Reviewer (Inpatient)

Crossing Hurdles; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Crossing Hurdles in Singapore seeks an experienced MD/DO board-certified in Internal Medicine to review medical records for AI training, focusing on accuracy and data quality.

You will assess inpatient documentation, including HPI, PMH, and medication reconciliation, and ensure information aligns with clinical notes and claims. This role requires strong chart review skills and comfort working with raw medical record data to support model improvements.

Qualifications

• MD or DO, board certified in Internal Medicine.
• Strong understanding of inpatient documentation and hospital workflows.
• Comfortable reviewing clinical notes and raw medical record data.
• 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, including HPI, PMH, medication reconciliation, social history, assessments, and plans.
• Identify missing, incorrect, or inconsistent information in medical records, including renal- and hepatic-adjusted dosing, medication interactions, and age-appropriate clinical details.
• Share feedback to help improve AI models and data quality.

Skills

Internal Medicine

Inpatient documentation

Clinical notes review

Chart review

Medical coding

Education

MD/DO, Internal Medicine

Job description

Crossing Hurdles in Singapore seeks an experienced MD/DO board-certified in Internal Medicine to review medical records for AI training, focusing on accuracy and data quality.

You will assess inpatient documentation, including HPI, PMH, and medication reconciliation, and ensure information aligns with clinical notes and claims. This role requires strong chart review skills and comfort working with raw medical record data to support model improvements.