4 дня назад
Internal Medicine Specialist (AI)
80 - 125INR
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Internal Medicine Specialist (AI): Reviewing adult inpatient medical records, FHIR data, and clinical documentation for an AI training project with an accent on clinical accuracy, documentation consistency, and medication safety. Focus on identifying unsupported or inconsistent information, evaluating renal- and hepatic-adjusted dosing and medication interactions, and applying review guidelines to improve AI models and data quality.
Location: United States, Remote
Salary: ₹80–₹125 per hour
Company
is the hiring organization for a contractor role supporting an AI training project.
What you will do
- Review inpatient medical records, FHIR data, and clinical notes.
- Verify that clinical information and claims are supported by medical documentation.
- Evaluate adult inpatient records, including medical histories, medication reconciliation, assessments, and care plans.
- Identify missing, inaccurate, or inconsistent information, including renal- and hepatic-adjusted dosing, medication interactions, and age-appropriate clinical details.
- Apply review guidelines consistently and provide feedback to improve AI models and data quality.
Requirements
- At least 5 years of hospitalist experience.
- MD or DO with board certification in Internal Medicine, or an Internal Medicine Hospital Medicine focused practice designation.
- Current or recent inpatient hospitalist experience within the past 2 years.
- Strong understanding of inpatient documentation and hospital workflows, including admissions, progress notes, care transitions, and discharge.
- Experience reviewing clinical notes and medical record data, with excellent attention to detail and consistent guideline adherence.
Nice to have
- Experience in chart review, clinical documentation improvement, utilization review, medical coding, or quality assurance.
Culture & Benefits
- Remote contractor engagement.
- Work focused on improving AI model accuracy and clinical data quality.
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