Job Description
Employment Type: Full-time
Designation: QA Data Analyst
The Radiology QA Data Analyst is responsible for reviewing radiology quality assurance data, analyzing reporting trends, identifying process gaps, and generating actionable insights to improve reporting accuracy, workflow efficiency, patient safety, and radiologist performance. This role combines radiology QA knowledge with data analytics skills to monitor discrepancy patterns, turnaround time, AI Assist performance, modality-wise trends, and root-cause categories.
The role requires strong analytical ability, attention to detail, understanding of radiology workflows, and the ability to convert QA findings into meaningful reports, dashboards, and improvement recommendations.
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Review and analyze radiology QA data from report audits, discrepancy reviews, peer reviews, AI Assist evaluations, and workflow quality checks.
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Compare preliminary/first reader reports with final/second reader reports to identify significant changes, missed findings, documentation gaps, and learning points.
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Classify QA cases based on severity, clinical impact, modality, body system, radiologist, client/site, shift, and root cause.
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Analyze high-grade QA cases and categorize them into root-cause groups such as training need, technology error, process failure, or overall category.
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Track QA trends across modalities such as X-ray, ultrasound, CT, MRI, mammography, emergency imaging, and subspecialty reporting.
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Prepare daily, weekly, and monthly QA dashboards covering discrepancy rates, error trends, turnaround time, productivity, audit volume, AI Assist performance, and compliance indicators.
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Perform data cleaning, validation, deduplication, standardization, and quality checks on QA datasets.
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Build and maintain structured trackers for QA cases, radiologist performance, AI Assist evaluation, turnaround time, report amendments, critical findings, and client escalations.
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Use Excel, Google Sheets, Power BI, Tableau, SQL, or similar tools to generate reports, charts, pivot tables, dashboards, and trend summaries.
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Identify recurring reporting errors, modality-wise gaps, process deviations, workflow delays, and opportunities for targeted training.
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Analyze AI Assist outputs to evaluate detection accuracy, false positives, false negatives, missed triggers, algorithm status, and overall AI performance.
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Support radiologists, QA leads, operations teams, and technology teams with data-backed insights for quality improvement.
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Present concise analytical summaries and recommendations to stakeholders during QA meetings and performance reviews.
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Monitor key performance indicators including QA accuracy, discrepancy percentage, major/minor error rate, report turnaround time, audit completion rate, and corrective action closure.
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Assist in creating SOPs, QA checklists, data dictionaries, audit templates, and standardized reporting formats.
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Maintain confidentiality and ensure responsible handling of patient, radiologist, and client data.
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Extract, transform, and organize QA data from RIS/PACS, reporting platforms, Excel trackers, AI Assist systems, and internal databases.
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Create automated or semi-automated reports to reduce manual QA tracking work.
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Develop dashboards to visualize quality trends by radiologist, modality, body part, client/site, shift, error type, and severity.
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Perform root-cause trend analysis to identify whether issues are related to training, workflow, technology, documentation, or process compliance.
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Monitor data integrity and ensure consistent use of QA categories, discrepancy definitions, and reporting standards.
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Provide actionable insights from raw QA data rather than only preparing descriptive summaries.
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Support predictive or preventive quality initiatives by identifying early warning patterns in QA performance.
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Collaborate with operations and technology teams to improve data capture, reporting automation, and QA workflow efficiency.
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Good understanding of radiology terminology, anatomy, imaging modalities, and report structure.
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Strong data analysis skills with the ability to interpret trends, patterns, and outliers.
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Proficiency in Microsoft Excel or Google Sheets, including pivot tables, lookup formulas, charts, conditional formatting, and data validation.
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Working knowledge of Power BI, Tableau, Looker Studio, SQL, Python, or similar analytics tools is preferred.
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Ability to clean, structure, and analyze large QA datasets.
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Ability to identify clinically meaningful discrepancies in radiology reports.
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Strong attention to detail for laterality, measurements, impression changes, missed findings, and report completeness.
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Ability to create clear dashboards, summaries, and stakeholder-ready reports.
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Understanding of QA metrics such as discrepancy rate, major/minor error rate, TAT, audit compliance, AI false positives/false negatives, and corrective action closure.
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Good communication skills to explain analytical findings to clinical and non-clinical stakeholders.
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Ability to maintain confidentiality and comply with healthcare data privacy requirements.
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Bachelor’s degree in Life Sciences, Radiology, Allied Health Sciences, Healthcare Management, Statistics, Data Analytics, Computer Science, or related field.
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Certification or training in data analytics, Power BI, Tableau, SQL, Python, or advanced Excel is preferred.
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Prior experience in radiology QA, healthcare analytics, medical documentation QA, clinical operations analytics, or teleradiology operations is an added advantage.
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Knowledge of PACS/RIS, teleradiology workflow, AI-assisted radiology tools, or healthcare quality standards is preferred.
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2–3+ years of experience in healthcare QA, data analytics, medical documentation, or radiology operations.
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Accuracy and completeness of QA data analysis.
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Timeliness of daily, weekly, and monthly dashboard/report submission.
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Quality of insights and usefulness of recommendations.
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Accuracy of discrepancy categorization and root-cause analysis.
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Reduction in manual reporting effort through automation or standardization.
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Identification of repeat error trends and training opportunities.
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Data quality, consistency, and tracker maintenance.
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Stakeholder satisfaction with QA reports and analytical outputs.
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Microsoft Excel / Google Sheets
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Power BI / Tableau / Looker Studio
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SQL / Python, preferred
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PACS / RIS / radiology reporting platforms
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QA tracking tools
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AI Assist or clinical decision-support platforms
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Presentation tools such as PowerPoint or Google Slides
The ideal candidate should have a combination of radiology QA understanding and data analytics capability. They should be able to review QA cases, structure raw data, identify trends, build dashboards, and provide actionable insights that help improve radiology reporting quality, operational efficiency, and patient safety.
