Medical records specialists

Enter patient medical information into electronic health records systems

Data entry from structured or semi-structured medical documents into EHR fields is highly automatable via OCR, NLP extraction, and form-filling AI. Voice-to-text and document parsing systems already perform this task with minimal human correction needed for standard inputs.

BLS evidence: They may gather patients' medical histories, symptoms, test results, treatments, and other health information and enter the details into electronic health records (EHR) systems.

Use classification systems to assign clinical codes for diagnoses and procedures

AI medical coding systems can now assign ICD-10, CPT, and other clinical codes from clinical documentation with accuracy matching or exceeding human coders for routine cases. Systems like autonomous coding engines are already deployed in production, requiring only batch review for edge cases.

BLS evidence: Medical coders assign the diagnosis and procedure codes for patient care, population health statistics, and billing purposes.

Maintain and retrieve records for insurance reimbursement and data analysis

Record retrieval and maintenance are largely database operations that AI can execute via queries and automated workflows. Organizing records for insurance claims and analytics involves pattern-matching and data structuring that current systems handle well, though complex dispute resolution may need human judgment.

BLS evidence: Medical records specialists maintain and retrieve records for insurance reimbursement and data analysis.

Review patient information for preexisting conditions to ensure proper coding

AI can scan patient histories, identify preexisting conditions from structured and unstructured data, and cross-reference against coding requirements. NLP systems excel at extracting historical diagnoses from clinical notes, though complex medical history interpretation may need human verification.

BLS evidence: For example, they might review patient information for preexisting conditions, such as diabetes, to ensure proper coding of patient data.

Review patients' records for timeliness, completeness, and accuracy

AI can systematically check records against completeness criteria, flag missing elements, identify temporal inconsistencies, and verify data accuracy against structured rules. Human review is still needed for ambiguous cases and final sign-off, but AI handles the bulk of the screening work.

BLS evidence: Medical records specialists typically review patients' records for timeliness, completeness, and accuracy.

Control access to patient files and transmit records per protocols

Access control and record transmission can be largely automated through rule-based systems and AI-driven authentication protocols. AI can verify authorization, apply transmission protocols, and audit trails, though unusual requests or protocol exceptions may need human review.

BLS evidence: Medical records specialists serve as gatekeepers for access to patient files, ensuring access only to authorized people and retrieve, scan, and transmit files according to established protocols.

Ensure confidentiality of patients' records and safeguard patient privacy

AI can monitor access logs, enforce privacy rules, detect anomalous access patterns, and flag potential HIPAA violations automatically. However, nuanced judgment calls about legitimate access exceptions and handling sensitive disclosure requests still require human oversight in many contexts.

BLS evidence: When handling medical records, these workers follow administrative, ethical, and legal requirements for safeguarding patient privacy.

Consult with healthcare providers to clarify diagnoses and obtain additional information

While AI can draft clarification requests and identify documentation gaps, the interactive consultation with physicians requires real-time human communication, relationship management, and navigating clinical judgment nuances that AI cannot yet handle autonomously in most healthcare settings.

BLS evidence: They meet with these workers to clarify diagnoses or to get additional information.

Serve as liaison between healthcare providers and billing offices

Liaison work involves relationship management, negotiating discrepancies, explaining complex coding decisions to non-technical staff, and mediating between clinical and business priorities—tasks requiring human communication skills, political awareness, and contextual judgment that AI cannot replicate.

BLS evidence: They also work as the liaison between healthcare providers and billing offices.