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Purpose

To review, annotate and classify AI generated data for improving precision.

Responsibilities

1. Review, interpret and verify

   1. accurately classify data generated by AI agents from medical documents such as physician notes, prescriptions, laboratory orders, clinical charts, etc.

   2. digitally isolated and labelled medical data (e.g., distinguishing between patient history, symptoms, diagnoses, and dosage instructions)

   3. specific clinical entities within the text, labelling variables such as [Medication Name], [Dosage], [Frequency], [ICD-Code], [Symptom], etc

   4. scanned medical documents into standardized asset types such as discharge summaries, referral letters, insurance authorization forms, lab reports, etc

   5. context tags, structural and clinical, assigned to documents to improve accuracy of the AI Agent.

2. Ground Truth Validation: identify errors, correct misinterpretations and log feedback to improve model accuracy.

3. Data Privacy Compliance: Strictly adhere to healthcare privacy regulations ensuring all data handling processes maintain zero exposure of Protected Health Information (PHI).

Qualifications and Skills

1. A degree or certification in nursing, pharmacology or medical transcription

2. Excellent written and verbal communication skills in English

3. Strong foundational knowledge of medical terminology, pharmacology, abbreviations, and clinical documentation structures

4. Ability to read and interpret varied, complex, and poorly legible cursive or handwritten medical text.

5. Should be able to use web-based data annotation software and office tools

6. Experience in data annotation, medical transcription, medical billing, or a clinical administrative role. shall be an added advantage

7. Quality Driven: Should have an eye for details and should be able to catch even minor differences and discrepancies in data

8. Sustained high focus: Capable of maintaining high level of focus and accuracy during repetitive, high-volume tasks

9. Data Privacy: Highly disciplined regarding data privacy/security boundaries