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How much do rn data abstraction remote jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for rn data abstraction remote in the United States is $44.91, according to ZipRecruiter salary data. Most workers in this role earn between $34.38 and $53.37 per hour, depending on experience, location, and employer.

What is an RN Data Abstraction remote job?

RN Data Abstraction remote jobs involve registered nurses (RNs) who work from home to review and extract clinical data from medical records for quality improvement, research, or regulatory reporting. These professionals analyze patient charts, ensure accurate and complete data collection, and enter information into databases according to specific guidelines. The work is typically project-based, requires attention to detail, and often supports hospital accreditation or performance measurement programs. Strong clinical knowledge, familiarity with electronic health records, and data entry skills are essential for this role.

What are the key skills and qualifications needed to thrive as an RN Data Abstraction remote?

To thrive as an RN Data Abstraction Remote, you need an active RN license, strong clinical knowledge, and experience with medical record review or abstraction. Familiarity with electronic health record (EHR) systems, data abstraction software, and possibly certifications like Certified Clinical Data Abstractor (CCDA) are typical requirements. Exceptional attention to detail, time management, and the ability to work independently are vital soft skills for remote success. These skills and qualifications ensure accurate, consistent data collection that supports quality improvement and compliance in healthcare organizations.

What are the typical challenges faced by RNs working in remote data abstraction roles, and how can they be addressed?

RNs in remote data abstraction often encounter challenges such as interpreting incomplete medical records, managing large volumes of data within tight deadlines, and navigating different electronic health record (EHR) systems. To address these, it's important to develop strong attention to detail, maintain up-to-date knowledge of abstraction guidelines, and utilize effective time management strategies. Regular communication with team members and seeking clarification from clinical staff can also help ensure data accuracy and resolve ambiguities, making the remote workflow more efficient and collaborative.

What is the difference between Rn Data Abstraction Remote vs Medical Records Technician?

AspectRn Data Abstraction RemoteMedical Records Technician
CredentialsRN license, data abstraction trainingHigh school diploma, coding certification
Work EnvironmentRemote, healthcare facilities, hospitalsHealthcare offices, hospitals, clinics
Industry UsageHealthcare, medical data managementMedical record keeping, coding
Job FocusAbstracting patient data, ensuring accuracyOrganizing, coding, and managing medical records

While both roles involve working with medical data, Rn Data Abstraction Remote primarily requires an RN license and focuses on abstracting patient information remotely, often for quality or research purposes. Medical Records Technicians typically handle organizing and coding medical records on-site or in healthcare settings. The roles differ mainly in credentials, work environment, and specific job functions, but both are essential in healthcare data management.

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Infographic showing various Rn Data Abstraction Remote job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 12% Part Time, and 2% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $93,419 per year, or $44.9 per hour.

Core Measures Abstractor (Remote)

Remote

American Data Network
11 - 50 employees

Other

Re-posted 23 days ago


Job description

Core Measures Abstractor

Join the American Data Network family and become part of an established, purpose-driven healthcare quality organization with more than 30 years of experience. At ADN, our work supports hospitals and healthcare clients in improving quality, patient safety, and clinical outcomes. We value integrity, accuracy, collaboration, and professionalism, and we are looking for experienced abstractors who take pride in producing high-quality work.

Position Summary: American Data Network is seeking experienced Core Measures Abstractors to support accurate and timely clinical quality measure abstraction for assigned healthcare clients. This role is ideal for candidates with recent, hands-on Core Measures abstraction experience who can independently review clinical documentation, apply measure specifications, and meet assigned deadlines with a high level of accuracy.

This is not a general data entry position. Candidates must have direct healthcare quality reporting, clinical chart review, or data abstraction experience specific to Core Measures.

Position Details: ADN hires both contractors and full-time employees for Data Abstraction roles. Contractors must be available for a minimum of 15 hours per week.

Key Responsibilities:

  • Reviews patient medical records and abstracts data for assigned Core Measures.
  • Applies specifications, definitions, inclusion and exclusion criteria, and abstraction guidelines accurately and consistently.
  • Interprets clinical documentation related to diagnoses, procedures, treatments, outcomes, discharge instructions, contraindications, exclusions, and related quality measures.
  • Enters accurate and complete data into assigned platforms, EHRs, vendor tools, or other data collection systems.
  • Maintains a high level of abstraction accuracy, completeness, and attention to detail.
  • Meets assigned productivity expectations, weekly progress goals, and internal and client deadlines.
  • Participates in quality assurance activities, including feedback, correction, validation, and IRR reviews.
  • Communicates professionally with account leads, team members, and client contacts when clarification is needed.
  • Maintains strict confidentiality and complies with ADN, client, HIPAA, and data security requirements.

Qualifications:

Required:

  • At least 2 years of recent Core Measures abstraction experience.
  • Ability to independently abstract Core Measures cases using measure specifications and clinical documentation.
  • Strong understanding of healthcare quality reporting, abstraction, clinical documentation review, and medical record interpretation.
  • Ability to work independently with minimal supervision while maintaining accuracy and meeting deadlines.
  • Strong attention to detail, organization, documentation, and follow-through.
  • Comfort working in multiple EHRs, abstraction platforms, vendor tools, and data collection systems as assigned.
  • Excellent interpersonal and written communication skills.
  • Contractors must have high-speed secure internet, a PC or laptop, and at least two monitors.

Preferred:

  • Experience using Core Measures abstraction tools, vendor platforms, quality reporting systems, and/or related data collection tools
  • Experience with IRR, QA review, audit validation, and/or abstraction accuracy review.

Screening & Onboarding: Qualified candidates will complete an initial phone screen and a Core Measures-specific abstraction assessment. The assessment is designed to evaluate understanding of measure specifications, abstraction judgment, accuracy, and completion speed.

ADN does not provide basic Core Measures abstraction training, so candidates should already have the abstraction experience needed for the role. After hire, ADN provides onboarding to ADN processes, client systems, EHR access, documentation tendencies, and other client-specific workflows.

Why This Role May Be a Good Fit: This position may be a strong fit for experienced Core Measures abstractors who enjoy independent work, flexible scheduling, clinical quality chart review, healthcare quality reporting, and contributing to meaningful work that supports hospitals and patient care. ADN offers a knowledgeable and supportive team environment, multiple abstraction opportunities, and potential growth into lead or expanded quality roles for strong performers.