Lead Clinical Terminologist
OracleAbout the role
The Oracle Health Data Intelligence (HDI) Platform is looking for a highly motivated and experienced Lead Clinical Informaticist to support AI-driven and science-focused projects within HDI.
- Clinical experience as a registered nurse, pharmacist, clinical laboratory technician, respiratory therapist, or other clinical role
- Alternatively, relevant experience may be considered in place of formal clinical certifications.
- Experience in a clinical informatics role.
- Experience in supporting data annotation, machine learning, natural language processing (NLP), and/or AI-driven projects.
- Clinical informatics experience
- Familiarity with clinical EHR systems and data.
- Collaboration with healthcare professionals, providers, IT specialists, and business users in analyzing clinical workflows and identifying opportunities for improvement.
- Quality measurement, care coordination, or other outcomes-based improvement programs.
- Use of clinical data for evidence-based decision making.
- Proven track record in implementing quality control processes to ensure the integrity and reliability of data and outcomes.
- Life sciences and/or clinical trials experience is a plus.
- Create and curate clinical value sets composed of industry standard terminologies such as SNOMED CT, ICD, and CPT. Ensure that these value sets meet the clinical and business
needs of algorithms.
- Recognized as a subject matter expert within the team and lead less experienced team members as a mentor.
- Drive internal platform changes including data model, terminology ontologies, and the platform rules engine.
- Develop and document clear annotation guidelines, ensuring that they are understood by annotation teams.
- Implementation of quality control processes to validate the integrity and reliability of annotated data.
- Document all processes, methodologies, and guidelines related to data annotation projects.
- Oversee the collection, cleaning, and pre-processing of data, ensuring that data is ready for use in machine learning projects.
- Collaborate with cross-functional teams, including data scientists, annotators, engineers, and project managers, to enhance data quality and project success.
Skills
- Critical thinking and problem solving. Candidates will often have to work through complex specifications and design algorithms that achieve the business or clinical intent while
working within the real-world limitations of clinical and administrative healthcare data.
- Experience working with disparate healthcare data including EHR, billing, lab, eligibility, or claims.
- Knowledge of industry standard terminologies such as SNOMED CT, ICD, and CPT.
- Modeling of clinical and administrative healthcare data using industry standard definitions such as FHIR Resources or QDM Categories.
- Use of SQL and other analytical tools for analyzing healthcare data.
- Knowledge of care management best practices and services.
- Strong ability to interpret project requirements and develop clear annotation guidelines.
- Skilled in communicating and collaborating with cross-functional teams, including data scientists, annotators, engineers, and project managers, to enhance data quality and project success.
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