Medical Records and Health Information Technicians

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Medical Records and Health Information Technicians (ISCO-08 3252) score an average of 0.52 on a 0–1 exposure scale — more exposed than about 90% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Translating narrative descriptions and numeric information from medical records and other documents on health services delivery into codes associated with standard classification systems;”

Scores 0.61 on the 2025 scale. The task of translating narrative descriptions and numeric information from medical records and health services delivery documents into codes associated with standard classification systems shares similarities with the tasks of maintaining patient health records and managing medical documentation, which had adjusted scores of 0.575 and 0.4575, respectively. These tasks involve a considerable degree of structured data processing, where Generative AI can assist effectively, given its capabilities in natural language processing and data categorization. The potential for automation is higher than tasks that require complex human judgment or interaction but lower than purely repetitive data entry. Since this task involves understanding medical narratives and ensuring accurate coding, human oversight is still crucial to ensure compliance and accuracy. Taking into account the capabilities of AI to facilitate data processing and the technological resources available in high-income countries like Poland, an adjusted automation score of 0.57 reflects AI's potential to enhance this task while acknowledging the indispensable human role in verifying precision and context-specific details.

Moving fastest, 2023 → 2025

“Supervising clerical and administrative workers involved in the maintenance of medical records.”

Model capability on this task changed by +0.21 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 3252, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

No U.S. role resolves through the crosswalk for this occupation. Search the encyclopedia for the closest match →

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Medical Records and Health Information Technicians sit at the 90th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Medical Records and Health Information Technicians rank in the 90th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Translating narrative descriptions and numeric information from medical records and other documents on health services delivery into codes associated with standard classification systems;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Medical Records and Health Information Technicians". https://singulariki.com/gradient/3252-medical-records-and-health-information-technicians.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.