Health Information Technologists and Medical Registrars

Apply knowledge of healthcare and information systems to assist in the design, development, and continued modification and analysis of computerized healthcare systems. Abstract, collect, and analyze treatment and followup information of patients. May educate staff and assist in problem solving to promote the implementation of the healthcare information system. May design, develop, test, and implement databases with complete history, diagnosis, treatment, and health status to help monitor diseases.

The occupation "Health Information Technologists and Medical Registrars" has a base automation risk of 57.8%, reflecting a moderate likelihood of job tasks being replaced by automation technologies in the coming years. This risk level arises from the significant portion of time these professionals spend on routine, rule-based tasks that are highly compatible with current automation capabilities. For example, one of the most automatable tasks is assigning patients to diagnosis-related groups (DRGs) using specialized computer software—a process increasingly handled by sophisticated algorithms that can rapidly process and classify medical billing codes. Similarly, compiling medical care and census data for various statistical reports is largely a data aggregation and formatting exercise, which can be easily managed by database systems and reporting tools. Finally, designing databases to support healthcare applications—while requiring an initial phase of human expertise—often becomes repetitive and systematic, allowing automated tools to ensure ongoing reliability, security, and performance. Despite the significant potential for automation, several core tasks remain resistant to being fully automated, thereby anchoring the value of human expertise in this occupation. Writing or maintaining archived procedures, procedural codes, or queries for healthcare applications demands context-aware reasoning and regular updates in response to changing regulations, which are challenging for machines to interpret without human oversight. Training medical records staff is inherently reliant on soft skills such as communication, mentorship, and adaptability, which current AI and automation tools cannot fully replicate. Retrieving patient medical records for physicians or other medical personnel, while partially automatable in digital environments, still requires a nuanced understanding of requests, privacy protocols, and occasional mediation, particularly in hybrid paper/electronic record environments. The main bottleneck skills that limit further automation in this occupation include complex problem solving (high), written communication (medium-high), and instructional abilities (medium). Complex problem solving is crucial when handling exceptions in data practices, ensuring integrity when systems fail, and interpreting nuanced medical coding scenarios—tasks that require judgment and experience beyond what rule-based automation can provide. Written communication is key when drafting or updating intricate procedures and documentation, as clarity and compliance are critical in the medical field. Instructional abilities are essential for effectively upskilling medical records staff who must adapt to evolving technologies and compliance requirements. These skill areas form critical bottlenecks, moderating the full impact of automation and preserving vital roles for skilled professionals in the field.