4 дня назад
Pediatric Hospitalist (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Pediatric Hospitalist (AI): Reviewing inpatient pediatric medical records, FHIR data, and clinical notes to train and evaluate AI systems with an accent on clinical accuracy, documentation completeness, and age-appropriate care details. Focus on validating clinical claims, identifying documentation gaps and inconsistencies, and assessing weight-based medication dosing.
Location: Remote from Brazil, Argentina, Mexico, Colombia, Peru, or Chile
Company
Boutique consultancy providing IT professional services and engineering talent solutions.
What you will do
- Review medical records, structured FHIR data, clinical information, and narrative notes for AI training and evaluation.
- Verify clinical claims and extracted information against source medical records.
- Evaluate pediatric inpatient documentation, including HPI, PMH, birth and developmental history, immunization status, assessments, and treatment plans.
- Identify missing, inaccurate, or inconsistent clinical details, including age-appropriate considerations and weight-based medication dosing.
- Apply review guidelines and documentation standards consistently.
- Provide evidence-based feedback and document review outcomes for the project team.
Requirements
- MD or DO degree.
- Board certification in Pediatrics, with Pediatric Hospital Medicine subspecialty certification where applicable.
- At least five years of hospitalist experience.
- Current or recent inpatient pediatric hospitalist experience within the past two years.
- Strong understanding of pediatric inpatient workflows, including admissions, daily progress notes, transitions of care, and discharge documentation.
- Ability to interpret clinical notes and structured medical record data, exercise sound clinical judgment, and communicate findings clearly.
Culture & Benefits
- Remote contract engagement.
- No prior AI experience is required.
- Work contributes to improving AI model performance and clinical data quality.
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