
Posted 2 months ago
Clinical Informaticist
AI Summary
Investigates and resolves clinical data quality issues across multiple EHR systems, maps clinical concepts to standardized terminologies, and ensures data completeness and accuracy to support population health, quality reporting, and value-based care.
About this role
As a clinical informaticist on the product management team, you will play a crucial role in ensuring that clinical data sourced from dozens of EHR systems and health information exchanges is accurate, standardized, and actionable. Working at the intersection of clinical knowledge and data engineering, you will collaborate with engineers, product managers, data scientists, and clinical operations teams to build and maintain Aledade's clinical data integration platform — the foundation for population health management, quality measure reporting, and value-based care programs serving thousands of primary care practices nationwide.
The ideal candidate brings deep clinical data fluency, an investigative mindset, and the ability to translate complex clinical concepts into precise data specifications. You will be responsible for validating data quality across vendor pipelines, mapping clinical data to standardized terminologies, supporting quality measure accuracy, and serving as the clinical subject matter expert for cross-functional initiatives including AI-assisted clinical data extraction.
Primary Duties
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Identify, analyze, and resolve clinical data quality issues across Aledade's multi-vendor EHR ecosystem. Investigate root causes of data discrepancies at the interface, configuration, and source system level. Produce structured findings with actionable recommendations for engineering and integration teams.
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Map clinical data elements to standardized code systems and validate mapping accuracy across clinical domains. Support the development and maintenance of Aledade's terminology mapping capabilities, including rule-based and AI-assisted approaches.
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Support quality measure programs by ensuring clinical data completeness and accuracy throughout the reporting pipeline. Validate measure logic and code sets against published specifications. Identify data gaps impacting measure performance and collaborate cross-functionally to resolution..
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Serve as the clinical subject matter expert for new data sources, AI-powered extraction, and interoperability initiatives. Provide clinical context to engineering, product, and operations teams for data governance, vendor engagement, and process improvement efforts.
Minimum Qualifications
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Bachelor’s or Master’s degree in Nursing, Medical/Health Informatics, Bioinformatics, or a related field.
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5+ years in clinical informatics, healthcare data analysis, or health information management
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Strong skills in querying, analyzing, and validating clinical data (experience with Databricks/Spark SQL a plus)
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Solid understanding and experience with standard Health Information Technology (HIT) vocabularies and terminologies.
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Hands-on experience with one or more ambulatory Electronic Health Record (EHR) systems.
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Deep knowledge of C-CDA/CCD document structure, HL7 standards, and how clinical data is represented in structured exchange formats
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Experience with quality measure specifications (eCQM, MIPS, MCQM, HEDIS, etc) and understanding of how clinical data supports measure calculation
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Demonstrated ability to investigate complex data issues, identify root causes, and communicate findings clearly to both technical and non-technical stakeholders
Preferred KSA’s
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Experience with cloud-based data platforms (Databricks, AWS, Snowflake) for large-scale clinical data analysis and data management
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Familiarity with FHIR resources and modern healthcare interoperability standards
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Understanding of working with Health Information Exchanges (HIE) or TEFCA data flows
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Knowledge of healthcare data governance and best practices in maintaining data integrity and security.
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Understanding of population health management and the data infrastructure that supports it.
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Certification in clinical informatics or health information management (e.g., CPHIMS, CAHIMS) is advantageous.
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Experience in applying machine learning or AI technologies within healthcare settings.
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Familiarity with healthcare data governance, provenance tracking, and regulatory requirements for clinical data use
Physical Requirements
Skills
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