Data Analyst, Clinical Data Effectiveness
New
C
Counterpart HealthHealthcare Technology
Remote - USAFull-TimeMiddle
Salary$77,000 — $100,000 USD
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Job Details
- Experience
- 2+ years
- Required Skills
- PythonSQLData visualizationData analytics
Requirements
- 2+ years of hands-on data analytics experience with exposure to healthcare data interoperability.
- Strong hands-on experience with healthcare data standards: HL7, CCDA, and ADT messages.
- Proficiency in SQL with experience working with large, complex datasets.
- Background in analyzing interoperability networks such as Carequality, CommonWell, or eHealthExchange.
- Skilled at data visualization and presenting findings to technical and non-technical stakeholders.
- Strong investigative mindset and ability to identify root causes for missing data.
- Self-directed ability to manage multiple analytical projects without constant supervision.
- Preferred: Experience with TEFCA/QHIN frameworks and national interoperability standards.
- Preferred: Familiarity with data exchange platforms like Bamboo Health, HSX, Particle Health, or Kno2.
- Preferred: Background in value-based care, population health, or clinical data operations.
- Preferred: Experience with claims-clinical data reconciliation.
- Preferred: Comfort working with Python for data manipulation and automation.
Responsibilities
- Proactively identify and monitor healthcare facilities to locate and quantify clinical data gaps.
- Conduct deep-dive analyses mapping facility-specific data across claims, clinical care summaries, and ADT messages.
- Maintain transparent reporting on ADT coverage across geographic and technological dimensions.
- Recommend and guide technical configurations for data vendors to optimize clinical data quality.
- Evaluate potential new data integration technologies to assess viability and strategic fit.
- Develop a data-driven framework for prioritizing facility targets for direct data acquisition.
- Inform and guide vendor strategy around national initiatives such as TEFCA and QHIN adoption.
- Partner with Product, Engineering, and Clinical teams to define requirements and validate data utility.
- Act as a liaison to remove operational and technical bottlenecks in clinical data flows.
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