Medical Records and Health Information Technicians
Category: Healthcare support · Industry: Healthcare
AI Exposure
58/100
0.0
Moderate exposure
Last updated
12 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
Evidence
2 sources
5 core tasks shown
Verdict
Medical Records and Health Information Technicians High exposure: 3 of 5 core tasks show high task automation potential : exposure 58/100 (moderate exposure).
1 model with published evidence against this job. Strongest: Llama 3.1 405B(84/100 capability).
Share this verdict:
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
Strength of current models on the capabilities this job's tasks require.
Task automation potential62/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption58/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength47/100
Quantity and source quality of the evidence behind the score.
2 evidence item(s) contribute. Calculated 19 hours ago. 12 Oct 2026 UTC.
Overview
Organises and codes health information. Coding suggestions and record extraction are highly automatable; release-of-information and audit decisions are human.
Employment
≈190,000
approx. BLS OES
Median wage
≈$48,000
approx. BLS OES
Tasks tracked
5
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
2 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
model releaseincreasesconfidence 88/100 · magnitude 4623 Jul 2024
Llama 3.1 405B and subsequent open-weight releases let organisations run near-frontier models on their own infrastructure, changing the cost and compliance barriers to automation.
A cost-and-adoption enabler rather than a capability jump. Included because cost is one of the main reasons high capability does not translate into adoption.
adoptiondecreasesconfidence 72/100 · magnitude 421 Feb 2024
The authors argue that AI can enable workers with less formal training to perform expert tasks, potentially rebuilding middle-class occupational demand rather than only displacing it.
A credible counter-direction hypothesis. Included so that the index does not systematically overstate substitution, and because the strongest experimental evidence (support agents, consultants) shows exactly this experience-compression effect.
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Score · 23 points · 2021-01-01 to 2026-07-01
Reconstruction, not measurement. 14 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
Research published
▲ IMPACT 40
"I just assumed that it would translate": examining MT risk awareness among healthcare staff with abbreviations as a use case
arXiv:2610.02496v1 Announce Type: new Abstract: In the UK, public healthcare staff report turning to machine translation (MT) - predominantly Google Translate (GT) - to communicate with patients across language barriers. Though intended to support their duty of care, potentially uninformed reliance on MT in such contexts could have serious consequences for patient safety. Research nonetheless remains limited on staff awareness of the possible risks posed by higher-stakes MT use in general and with patient medical records in particular, most existing literature instead examining its use in inte
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Occupation score change
▼ IMPACT 70
medical records technician exposure decreased 7.0 points
AI Exposure Score moved from a previous calculation to 58/100 under methodology v1.0. 2 evidence item(s) contributed.
Verified signal/Confidence:50 · low/Verified by AIKillsWhat
Occupation score change
▲ IMPACT 20
medical records technician exposure increased 2.0 points
AI Exposure Score moved from a previous calculation to 65/100 under methodology v1.0. 2 evidence item(s) contributed.
Verified signal/Confidence:50 · low/Verified by AIKillsWhat
Llama 3.1 405B and subsequent open-weight releases let organisations run near-frontier models on their own infrastructure, changing the cost and compliance barriers to automation.
Frontier release/Confidence:88 · high/Verified by AIKillsWhat
AI adoption signal
▼ IMPACT 42
AI may expand expert work rather than only substitute for it
The authors argue that AI can enable workers with less formal training to perform expert tasks, potentially rebuilding middle-class occupational demand rather than only displacing it.
Enterprise deployment/Confidence:72 · moderate/Verified by AIKillsWhat