Clinical Data Quality Assurance

HealtheConnections•Syracuse, NY

11h•$75,000 - $90,000•Hybrid

About The Position

HealtheConnections is seeking a Clinical Data Quality Assurance professional with a background in healthcare, health information technology (HIT), or clinical data quality. This role is crucial for ensuring the completeness, accuracy, and reliability of data within our health information exchange, which serves thousands of healthcare facilities across New York State. The ideal candidate will enjoy working with clinical data, identifying discrepancies, and upholding data quality and reliability standards. Responsibilities include reviewing information flows, validating data, identifying patterns and discrepancies, and investigating issues through to resolution. This role involves collaboration with customers, EMR and HIT vendors, and internal teams to review data feeds, communicate findings, and support improvements.

Requirements

Experience working within the healthcare or HIT industry required.
2-year degree or higher in HIT, Healthcare, Computer Science, or related field. Equivalent experience and certifications will also be considered.
Experience working with healthcare interoperability standards such as HL7, FHIR, CCD/CCD-A, and terminology standards is required.
Experience working with SQL, database concepts, and data analysis tools like Big Query and Tableau is required.
Ability to work independently and balance competing priorities and complex situations.
Excellent analytical skills; critical thinking.

Nice To Haves

2+ years’ professional experience working with clinical data preferred.

Responsibilities

Review and analyze clinical data: Examine HL7 messages, FHIR Resources, and data feeds to identify missing, incorrect, inconsistent, or unexpected information.
Investigate data quality issues: Trace issues across systems, determine root causes, and work with internal teams, customers, and vendors to resolve them.
Monitor data and identify trends: Review data quality results, investigate recurring issues, and identify patterns that may indicate larger or systemic problems.
Validate data feeds and changes: Test new and existing data feeds, system changes, and workflows to confirm clinical data is being received, processed, and displayed as expected.
Communicate findings and coordinate resolution: Document issues, explain findings to technical and non-technical stakeholders, and track issues through resolution.
Improve data quality processes: Identify opportunities to improve how data quality is monitored and managed, and help develop standards, procedures, and documentation.
Manage assigned work independently: Prioritize multiple investigations and projects, determine when issues require escalation, and ensure work is completed and followed through.