Business Analyst

Overview:

TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client is an American multinational information technology services and consulting company and is a leading provider of information technology, consulting, and business process outsourcing services, dedicated helping the world's leading companies build stronger businesses. Job Title: Business Analyst Location: Remo te Duration: 6 Months Job Type: Temporary Assignment Work Type: Remo te Position Summary a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; }

We are seeking a Business Analyst with strong FHIR and clinical data expertise to support investigation and remediation of recurring data quality, harmonization, normalization, and reconciliation issues across multiple healthcare data sources. The ideal candidate will analyze source FHIR payloads, transformed records, and patient-facing application outputs to identify root causes, validate mapping logic, and define scalable solutions that improve clinical data quality and consistency. This role will partner closely with product, engineering, and healthcare stakeholders to establish reusable validation rules, normalization logic, and long-term data quality improvements. Key Responsibilities Review and analyze raw FHIR source data and transformed clinical records. Validate FHIR resource mappings, transformations, semantics, and normalization logic. Identify whether data quality issues originate from source systems or internal processing logic. Recommend scalable solutions that address root causes rather than implementing one-off data fixes. Collaborate with product and engineering teams to implement reusable business rules and validation frameworks. Support clinical data harmonization across EHR, HIE, QHIN, and other healthcare data sources. Analyze source payloads and trace records through ingestion, normalization, reconciliation, and end-user presentation layers. Design data quality controls and validation processes to improve data consistency and patient experience. Document findings, recommendations, business rules, and remediation strategies. Primary Areas of Focus Encounters / Visits Determine methods for distinguishing true patient visits from calls, messages, and other non-visit encounter types. Investigate encounter classification challenges, particularly within QHIN environments where reliable filtering signals may not exist. Define encounter reconciliation and categorization rules. Diagnostic Reports Investigate incorrect, unknown, or inconsistent diagnostic report dates. Analyze duplicate and incorrectly assembled diagnostic reports. Establish data quality and standardization recommendations for report presentation. Medications Identify root causes of duplicate medication records. Define medication reconciliation and deduplication strategies. Recommend scalable medication normalization solutions. Conditions Review FHIR condition status and clinical date semantics. Improve condition classification logic used to distinguish active, current, and historical conditions. Validate condition lifecycle and status mapping approaches. Dates and Clinical Event Timing Establish consistent date-selection and mapping rules across FHIR resources and source systems. Resolve discrepancies between displayed dates and underlying clinical event dates. Define date hierarchy and business rules for patient-facing presentation. Migrated Encounters Investigate migrated encounter issues. Analyze data reconciliation challenges associated with encounter migrations. Recommend long-term mapping and harmonization approaches. Procedures Address incorrect classification of laboratory activities and immunizations as procedures. Define source-aware business rules that preserve clinically accurate patient-facing experiences. Improve classification logic across clinical data domains. Required Qualifications Strong hands-on expertise with FHIR R4. Experience with clinical data quality management. Experience with data normalization, harmonization, reconciliation, and deduplication. Experience working with EHR, HIE, QHIN, and healthcare interoperability platforms. Strong understanding of clinical terminology mappings and healthcare data standards. Experience analyzing source payloads and downstream data transformations. Ability to identify root causes across ingestion, mapping, transformation, and presentation layers. Strong analytical, troubleshooting, and problem-solving capabilities. Excellent communication and stakeholder management skills. Preferred Skills Healthcare interoperability expertise. HL7 FHIR implementation experience. Clinical informatics experience. Clinical data governance and data quality management. Experience supporting patient-facing healthcare applications. Knowledge of terminology standards such as SNOMED CT, LOINC, RxNorm, and ICD coding systems. Experience working with healthcare product and engineering teams. Top Skills FHIR R4. Clinical Data Quality & Harmonization. Data Normalization, Reconciliation & Deduplication. Years of Experience 5+ Years of Experience. TekWissen Group is an equal opportunity employer supporting workforce diversity