Data Analyst, Clinical Data Effectiveness
New
Based in the United StatesFull-TimeMiddle
Salary77,000 - 100,000 USD per year
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Job Details
- Experience
- 2+ years
- Required Skills
- PythonSQLData analytics
Requirements
- 2+ years of hands-on experience in data analytics within healthcare or clinical data environments.
- Strong expertise in healthcare interoperability standards including HL7, CCDA, and ADT messaging.
- Proficiency in SQL and experience working with large-scale, complex datasets such as claims, clinical records, or interoperability logs.
- Familiarity with healthcare data exchange networks such as Carequality, CommonWell, or eHealthExchange.
- Strong analytical mindset with the ability to investigate root causes of data gaps and inconsistencies.
- Experience working with data visualization tools and presenting insights to both technical and non-technical stakeholders.
- Ability to manage multiple analytical workstreams independently with strong organizational discipline.
- Exposure to TEFCA/QHIN frameworks, vendor platforms (e.g., Bamboo Health, HSX, Particle Health, Kno2), or value-based care environments is a plus.
- Comfort with Python or similar tools for data manipulation and automation is preferred.
Responsibilities
- Own end-to-end analysis of clinical data completeness and effectiveness across multiple interoperability channels, identifying gaps, breakdowns, and inconsistencies in healthcare data flows.
- Analyze structured and unstructured healthcare data sources (including HL7, CCDA, ADT, claims, and clinical summaries) to detect missing, delayed, or misaligned clinical records.
- Build and maintain reporting frameworks to monitor data coverage across facilities, geographies, vendors, and EMR systems.
- Evaluate and optimize data integration pathways with vendors and interoperability networks, recommending improvements for data quality and reliability.
- Conduct comparative analyses and proof-of-concept assessments for new data exchange technologies and vendors to support strategic decisions.
- Define and maintain frameworks to assess clinical documentation completeness at the patient-encounter level.
- Partner with Product, Engineering, and Clinical teams to translate analytical findings into actionable improvements in data pipelines and platform performance.
- Support strategic initiatives around national interoperability standards (including TEFCA/QHIN) and ensure alignment with evolving healthcare data exchange ecosystems.
- Act as a key liaison in identifying and resolving operational and technical bottlenecks impacting clinical data flow and usability.
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