Data Abstractor Jobs in Tennessee (NOW HIRING)

As of Jun 9, 2026, the average hourly pay for data abstractor in Tennessee is $22.99, according to ZipRecruiter salary data. Most workers in this role earn between $16.78 and $29.04 per hour, depending on experience, location, and employer.

Data Abstractors are professionals who review, analyze, and extract relevant information from various documents, such as medical records or research data, to ensure accuracy and completeness. They play a crucial role in organizing and summarizing data for use in databases, quality assurance, research, or reporting purposes. Data Abstractors must have strong attention to detail, knowledge of data management systems, and the ability to interpret complex information. Their work supports decision-making in healthcare, research, and other industries by providing reliable and accessible data.

Data Abstractors often work closely with clinical staff, such as nurses and physicians, to clarify medical records and ensure accurate data extraction. They may attend regular team meetings to discuss documentation standards and resolve ambiguities in patient charts. Effective communication and attention to detail are essential, as abstractors must interpret various formats of medical data and follow strict confidentiality guidelines. This collaborative environment helps ensure the integrity and quality of the collected data, which is crucial for research, compliance, and patient care initiatives.

To thrive as a Data Abstractor, you need strong attention to detail, analytical skills, and a background in health information management or a related field, often supported by an associate's degree or relevant certification. Familiarity with electronic health record (EHR) systems, data abstraction software, and medical coding (such as ICD-10 or CPT) is typically required. Excellent organizational skills, time management, and effective communication help ensure accuracy and clarity when extracting and summarizing complex data. These skills and qualities are crucial for maintaining data integrity and supporting clinical research, reporting, and regulatory compliance.