Oracle Health Data Intelligence (HDI) is at the forefront of transforming healthcare through innovative data and AI solutions. We’re seeking a highly skilled individual contributor to join our team in the USA. This role focuses on leveraging clinical data, machine learning (ML), and AI technologies to drive healthcare innovation. You’ll contribute to AI-driven projects by applying informatics techniques, statistical modeling, and annotation guidelines to improve healthcare delivery, patient outcomes, and operational efficiency. At HDI, we are committed to using cutting-edge technologies such as natural language processing (NLP) and predictive analytics to revolutionize patient care and optimize healthcare systems.
U.S. citizenship is required for this position, as the successful candidate will be required to obtain and maintain a U.S. government security clearance after hire.
Internal Responsibilities
Required Experience
- Clinical experience in roles such as a registered nurse, pharmacist, medical scribe, clinical laboratory technician, respiratory therapist, or other healthcare-related role. Relevant healthcare experience may be considered in lieu of formal clinical certification.
- 1-3 years of experience in clinical informatics, healthcare data analysis, clinical documentation review, medical coding, quality improvement, or related healthcare technology roles.
- Familiarity with electronic health record (EHR) systems and clinical workflows.
- Experience reviewing clinical data and applying clinical knowledge to support data quality, care management, quality measurement, or healthcare operations.
- Exposure to healthcare AI, machine learning, natural language processing (NLP), data annotation, or clinical data labeling projects is preferred.
- Familiarity with healthcare terminologies such as SNOMED CT, ICD, CPT, LOINC, or RxNorm.
- Experience working with healthcare data sources such as EHR, laboratory, billing, claims, or eligibility data.
- Understanding of healthcare data privacy, security, and regulatory requirements.
- Life sciences, clinical trials, or regulatory experience is a plus.
Preferred Experience
- Exposure to AI/ML model evaluation, annotation quality review, or healthcare data validation.
- Basic understanding of healthcare interoperability standards such as FHIR.
- Experience supporting cross-functional projects involving clinicians, data scientists, engineers, and business stakeholders.
Responsibilities
- Review and validate medical annotations to ensure clinical accuracy, consistency, and compliance with established annotation guidelines.
- Assist in the creation, maintenance, and refinement of clinical annotation guidelines and documentation.
- Perform quality assurance reviews of annotated datasets and provide feedback to annotation teams.
- Identify annotation discrepancies, edge cases, and data quality issues and escalate findings appropriately.
- Support the development and maintenance of clinical value sets using industry-standard terminologies such as SNOMED CT, LOINC, RxNorm, ICD, CPT, and other healthcare vocabularies.
- Collaborate with clinical experts, annotators, data scientists, and engineers to improve annotation quality and data consistency.
- Participate in AI/ML model evaluation activities by reviewing model outputs and identifying opportunities for improvement.
- Conduct basic error analysis and contribute clinical insights to improve annotation processes and model performance.
- Assist with data preparation, validation, and quality checks to support AI/ML initiatives.
- Document findings, recommendations, and quality metrics related to annotation and model evaluation activities.
- Support ongoing process improvement efforts related to clinical data quality, annotation workflows, and AI-enabled healthcare solutions.
- Stay current with healthcare standards, terminology updates, and industry best practices.
Skills
Required Skills
- Strong clinical knowledge and ability to interpret healthcare documentation and clinical workflows.
- Attention to detail and commitment to data quality and accuracy.
- Knowledge of healthcare terminologies including SNOMED CT, ICD, CPT, LOINC, or RxNorm.
- Familiarity with healthcare data sources such as EHR, billing, laboratory, claims, or eligibility data.
- Strong analytical and problem-solving skills.
- Ability to identify inconsistencies and quality issues within annotated clinical data.
- Effective written and verbal communication skills.
- Ability to collaborate effectively with cross-functional teams.
Preferred Skills
- Basic understanding of AI/ML concepts, natural language processing (NLP), and healthcare data annotation workflows.
- Familiarity with model evaluation metrics and quality measurement concepts.
- Experience using SQL, Excel, or other data analysis tools.
- Exposure to healthcare interoperability standards such as FHIR.
- Knowledge of care management programs, quality measures, or population health initiatives.
External Responsibilities
Required Experience
- Clinical experience in roles such as a registered nurse, pharmacist, medical scribe, clinical laboratory technician, respiratory therapist, or other healthcare-related role. Relevant healthcare experience may be considered in lieu of formal clinical certification.
- 1-3 years of experience in clinical informatics, healthcare data analysis, clinical documentation review, medical coding, quality improvement, or related healthcare technology roles.
- Familiarity with electronic health record (EHR) systems and clinical workflows.
- Experience reviewing clinical data and applying clinical knowledge to support data quality, care management, quality measurement, or healthcare operations.
- Exposure to healthcare AI, machine learning, natural language processing (NLP), data annotation, or clinical data labeling projects is preferred.
- Familiarity with healthcare terminologies such as SNOMED CT, ICD, CPT, LOINC, or RxNorm.
- Experience working with healthcare data sources such as EHR, laboratory, billing, claims, or eligibility data.
- Understanding of healthcare data privacy, security, and regulatory requirements.
- Life sciences, clinical trials, or regulatory experience is a plus.
Preferred Experience
- Exposure to AI/ML model evaluation, annotation quality review, or healthcare data validation.
- Basic understanding of healthcare interoperability standards such as FHIR.
- Experience supporting cross-functional projects involving clinicians, data scientists, engineers, and business stakeholders.
Responsibilities
- Review and validate medical annotations to ensure clinical accuracy, consistency, and compliance with established annotation guidelines.
- Assist in the creation, maintenance, and refinement of clinical annotation guidelines and documentation.
- Perform quality assurance reviews of annotated datasets and provide feedback to annotation teams.
- Identify annotation discrepancies, edge cases, and data quality issues and escalate findings appropriately.
- Support the development and maintenance of clinical value sets using industry-standard terminologies such as SNOMED CT, LOINC, RxNorm, ICD, CPT, and other healthcare vocabularies.
- Collaborate with clinical experts, annotators, data scientists, and engineers to improve annotation quality and data consistency.
- Participate in AI/ML model evaluation activities by reviewing model outputs and identifying opportunities for improvement.
- Conduct basic error analysis and contribute clinical insights to improve annotation processes and model performance.
- Assist with data preparation, validation, and quality checks to support AI/ML initiatives.
- Document findings, recommendations, and quality metrics related to annotation and model evaluation activities.
- Support ongoing process improvement efforts related to clinical data quality, annotation workflows, and AI-enabled healthcare solutions.
- Stay current with healthcare standards, terminology updates, and industry best practices.
Skills
Required Skills
- Strong clinical knowledge and ability to interpret healthcare documentation and clinical workflows.
- Attention to detail and commitment to data quality and accuracy.
- Knowledge of healthcare terminologies including SNOMED CT, ICD, CPT, LOINC, or RxNorm.
- Familiarity with healthcare data sources such as EHR, billing, laboratory, claims, or eligibility data.
- Strong analytical and problem-solving skills.
- Ability to identify inconsistencies and quality issues within annotated clinical data.
- Effective written and verbal communication skills.
- Ability to collaborate effectively with cross-functional teams.
Preferred Skills
- Basic understanding of AI/ML concepts, natural language processing (NLP), and healthcare data annotation workflows.
- Familiarity with model evaluation metrics and quality measurement concepts.
- Experience using SQL, Excel, or other data analysis tools.
- Exposure to healthcare interoperability standards such as FHIR.
- Knowledge of care management programs, quality measures, or population health initiatives.
