Night Shift Remote Rn Data Abstractor Jobs in Grand Forks, ND

As of Aug 24, 2026, the average hourly pay for night shift remote rn data abstractor in Grand Forks, ND is $31.18, according to ZipRecruiter salary data. Most workers in this role earn between $17.98 and $42.45 per hour, depending on experience, location, and employer.

A Night Shift Remote RN Data Abstractor is a registered nurse who works remotely, typically during overnight hours, to review and extract clinical data from medical records. These professionals play a key role in ensuring that patient information is accurately entered into databases for quality reporting, research, or compliance purposes. Working remotely allows for flexibility, while the night shift often caters to facilities operating 24/7 or seeking to maximize data processing. Strong clinical knowledge and attention to detail are essential for this role.

To excel as a Night Shift Remote RN Data Abstractor, you need a current RN license, strong clinical knowledge, and experience in medical record review or data abstraction. Proficiency in health information management systems, electronic health records (EHRs), and familiarity with quality measure reporting tools are commonly required, along with certifications like CCDS or CDIP being advantageous. Outstanding attention to detail, self-motivation, and excellent written communication are vital soft skills for performing efficiently and independently during off-hours. These skills ensure accurate data extraction, compliance with reporting standards, and contribute to high-quality patient care outcomes in a remote, night shift setting.

Night Shift Remote RN Data Abstractors often encounter challenges such as maintaining focus during overnight hours, ensuring accurate data entry without direct supervision, and managing communication with daytime teams. To overcome these, it's important to establish a consistent sleep schedule, create a distraction-free workspace, and utilize collaboration tools for effective handovers. Staying organized and proactively reaching out to colleagues during overlap hours can also help maintain high data quality and team cohesion.