Healthcare Interoperability & Clinical Data Specialist The Healthcare Interoperability & Clinical Data Specialist is responsible for onboarding, analyzing, transforming, validating, and supporting clinical data received from external and internal healthcare sources. The role has a strong focus on interoperability standards and clinical document exchange, including CCD/C-CDA, HL7, FHIR, and REST APIs. This individual will work with EHR vendors, health information exchanges, data aggregators, internal IT teams, and clinical quality stakeholders to understand the structure and meaning of new data sources, develop scalable ingestion and normalization processes, assess data quality and clinical usability, and troubleshoot integration issues. Experience with HEDIS and quality reporting is a strong plus, particularly for evaluating the value of clinical data for supplemental and digital quality reporting.
Key Responsibilities:
Analyze and interpret inbound clinical data from new and existing sources, with particular emphasis on CCD/C-CDA documents, FHIR resources, HL7 messages, and API-based data exchange. Evaluate the structure, semantics, completeness, consistency, and clinical usability of data received from EHRs, health information exchanges, data aggregators, and other interoperability partners. Develop, enhance, and maintain processes to parse, extract, normalize, transform, and validate clinical data from XML, JSON, delimited files, APIs, and other healthcare data formats. Map source data elements and clinical concepts to normalized internal data models and, where applicable, FHIR resources and standard terminologies such as LOINC, SNOMED CT, ICD-10-CM, CPT/HCPCS, and RxNorm. Lead or support onboarding of new clinical data sources by performing source discovery, sample-file analysis, field and code mapping, data profiling, validation, defect identification, and production-readiness assessment. Create automated data quality checks and reconciliation processes to identify missing sections, malformed documents, inconsistent coding, duplicate clinical facts, unexpected values, and source-specific anomalies. Investigate interoperability and data integration issues, perform root cause analysis, and coordinate resolution with EHR vendors, health information exchanges, data aggregators, IT teams, and business stakeholders. Develop and maintain reusable Python and SQL utilities for clinical data profiling, parsing, comparison, validation, exception handling, and large-scale analysis. Work with FHIR resources and RESTful APIs to retrieve, inspect, validate, and transform clinical data, including understanding resource relationships, references, profiles, bundles, and terminology bindings. Analyze CCD/C-CDA documents at both document and section level, including templates, entries, identifiers, codes, dates, providers, encounters, observations, procedures, and other clinically relevant content. Assess whether clinical data from new sources is suitable for downstream use cases such as quality measurement, supplemental data, clinical analytics, population health, and HEDIS reporting. Create and maintain technical documentation including source inventories, interface specifications, data dictionaries, mapping documents, validation rules, data lineage, known limitations, and onboarding procedures. Participate in user acceptance testing, production support, source certification, release validation, and ongoing monitoring of interoperability and clinical data pipelines.
Required Qualifications:
Bachelor's degree in Computer Science, Information Systems, Health Informatics, Data Analytics, Health Information Management, or a related field, or equivalent practical experience.5+ years of experience working with healthcare data, healthcare interoperability, clinical data integration, health informatics, or a closely related area. Hands-on experience analyzing and working with CCD/C-CDA clinical documents, including XML structure, sections, entries, templates, identifiers, and clinical coding. Hands-on experience with FHIR, including common clinical resources, Bundles, references, profiles, REST APIs, and JSON/XML representations. Strong SQL skills and practical Python experience for parsing, transformation, automation, validation, and analysis of large healthcare datasets. Working knowledge of healthcare interoperability concepts and standards, including HL7, C-CDA/CCD, FHIR, APIs, and common healthcare code systems and terminologies. Experience profiling unfamiliar healthcare data sources and translating source-specific structures into standardized or normalized data models. Experience troubleshooting data quality, mapping, ingestion, and integration issues across multiple vendors or source systems. Strong analytical, problem-solving, communication, and technical documentation skills, with the ability to explain complex data issues to both technical and business stakeholders.
Preferred Qualifications:
Experience with HEDIS, NCQA quality reporting, supplemental data, digital quality measurement, or Medical Record Review (MRR).Experience evaluating clinical data for quality-measure use cases and determining which document sections, coded facts, and source types provide meaningful reporting value. Experience with clinical terminology and value-set concepts, including LOINC, SNOMED CT, ICD-10-CM, CPT/HCPCS, and RxNorm. Experience with AWS or other cloud-based data and analytics environments, including large-scale file processing and automated data pipelines. Experience working with health information exchanges, EHR vendors, clinical data aggregators, or payer-provider interoperability initiatives.
