COOKED
The core task — converting dictated physician audio into structured clinical documents — is the single most directly solved task in healthcare support: medical-grade speech recognition plus LLM formatting now produces drafts at or above human first-pass quality, and ambient scribing tools skip the dictation step entirely. What remains is editing and quality assurance: catching drug-name and dosage errors, flagging internal inconsistencies, and enforcing template and payer-specific documentation rules, which is real judgment but requires a fraction of the headcount. No license is required and the physician, not the transcriptionist, signs the note.
Part 2020 shock, part continued decline in the years since.
Median pay $33,380 → $40,410 -3.2% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
-4.9% 43,900 → 41,800 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -4.9% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~7,400 openings a year on average, including replacing people who leave.
ScribeTranscriberMedical ScribeClinical ScribeProvider ScribeData TranscriberTranscriptionistOphthalmic ScribeMedical TranscriberMedical StenographerMedical Records ClerkDocumentation SpecialistEmergency Medical ScribeMedical TranscriptionistTranscription SpecialistMedical Record TranscriberPathology TranscriptionistRadiology TranscriptionistMedical Language SpecialistMedical Transcription EditorMedical Language ProfessionalTranscribing Machine OperatorClinical Medical TranscriptionistCertified Medical Transcriptionist
Holding it up: judgment & accountability . Weakest point: embodiment .
Core tasks are already automatable Dictation-to-document conversion is what ASR was built for — Dragon Medical and Nuance DAX already deliver 98%+ accuracy on drug names and anatomical terms, and ambient listening tools generate the H&P or operative note from the room audio without anyone dictating at all, leaving no core task on your daily queue that a model does not already draft.
Fully desk- and screen-based The whole shift is a headset, a foot pedal, and a text window — often from home — with no patient contact, no specimen handling, and nothing that requires being in the building.
No licence, no signature requirement AHDI's RHDS/CHDS credentials are voluntary and no state licenses transcription; the attending physician attests and signs the note, so a mis-transcribed dosage becomes a documentation-error claim against the practice, not a license action against you.
Meaningful discretion Editing an ASR draft means resolving genuinely ambiguous audio — hypo- versus hyper-, 15 versus 50 mg, laterality that contradicts the rest of the note — and deciding whether to fill the blank or flag it for the dictator, which is real discretionary weight on patient safety, but it sits at 8 rather than 14 because style guides (AHDI Book of Style), account-specific templates, and mandatory flag-don't-guess rules constrain nearly every call, and a physician reviews before signature.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (2/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (2/20) is whether the law requires a licensed human to sign. Trust premium (2/20) is whether buyers specifically pay for a person. Judgment and accountability (8/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 12 of this occupation's 15 points (80%).
Embodiment (1/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
Ophthalmic Medical Technicians EXPOSED
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 28/100, still COOKED.
Task-mix shift: once first-pass drafting is fully automated, the surviving work is the QA tier — reconciling drug/dose mentions against the med list, catching ASR homophone errors in high-risk fields (mg vs mcg, 'hypo/hyper'), resolving contradictions between ambient-captured dialogue and the structured note, and applying payer- and specialty-specific documentation rules for E/M level support. That residual tier is genuinely harder for models because it requires cross-referencing the EHR record, not just the audio. This raises average per-worker task_resistance even as headcount collapses — the score describes the remaining job, not the number of jobs.
If health systems formalize the editor role as a clinical documentation integrity (CDI) function with authority to hold or query a note before physician signature — the query workflow ACDIS/AHIMA already define for CDI specialists — the role owns a consequential call (does this note support the coded acuity, is this dose plausible) rather than transcribing. Watch for job postings retitling transcriptionist lines as 'CDI specialist' or 'AI documentation QA reviewer' with query authority.
A Joint Commission or CMS Conditions of Participation requirement that AI-generated clinical documentation carry an attested human review step before entering the legal medical record — analogous to how California AB 3030 requires clinician review of AI-generated patient communications — would create a named reviewer role. Note the ceiling: unless the attestation must come from someone other than the signing physician, hospitals will satisfy it with the physician's own signature and no transcriptionist is needed.
Malpractice insurers or EHR vendors requiring a documented independent QA pass on ambient-scribe output as a condition of coverage or indemnification — the same pattern insurers used to mandate double-check workflows for high-alert medications. This is a contract requirement, not a license, so it caps low, but it makes the reviewer headcount non-optional.
The limit. No plausible route to trust_premium: patients never see the transcriptionist and no buyer will pay extra for a human-typed note. No route on embodiment. Even with every lever above, this is a role that shrinks by an order of magnitude and survives as a small QA function inside CDI or HIM departments; the score rises for the survivors, not for the occupation's size. Absence of any licensure body for transcription is the hard cap on liability_shield — AHDI's CHDS/RHDS are voluntary credentials with no statutory signing authority, and no state has moved to license them.
| New York-Newark-Jersey City, NY-NJ | 2,910 | $44,010 +9% |
| Los Angeles-Long Beach-Anaheim, CA | 2,290 | $38,650 -4% |
| Dallas-Fort Worth-Arlington, TX | 1,300 | $34,910 -14% |
| San Antonio-New Braunfels, TX | 780 | $27,400 -32% |
| Chicago-Naperville-Elgin, IL-IN | 720 | $38,700 -4% |
| San Diego-Chula Vista-Carlsbad, CA | 710 | $45,420 +12% |
| Atlanta-Sandy Springs-Roswell, GA | 680 | $28,330 -30% |
| San Francisco-Oakland-Fremont, CA | 680 | $58,240 +44% |
| San Francisco-Oakland-Fremont, CA | 680 | $58,240 +44% |
| San Jose-Sunnyvale-Santa Clara, CA | 200 | $57,420 +42% |
| Norwich-New London-Willimantic, CT | 30 | $55,890 +38% |
Health New Zealand · Cleveland Clinic · Te Whatu Ora · St. Luke's Health System · Mass General Brigham · Carle Health · NHS · Beth Israel Lahey Health · Walter Reed National Military Medical Center · Alberta Health Services / University of Alberta · Rush, McLeod Health, Franciscan Missionaries of Our Lady Health System · Health PEI · Northwest Territories Health and Social Services Authority · Nova Scotia Health · Sharp HealthCare · Cabrini Health · Seoul St. Mary's Hospital · New Zealand general practices · Penn Medicine · US Department of Veterans Affairs · Ardent Health
Nature reports on Cleveland Clinic's enterprise-scale deployment of ambient AI scribe technology for clinical documentation via a health system-industry partnership.
Healthcare IT News reports Rhode Island enacted a law requiring patient opt-out provisions for ambient AI scribes used in clinical documentation.
Ontario's auditor general reported that AI scribe/clinical documentation systems in use by physicians in the province produce hallucinated content.
Fierce Healthcare reports Beth Israel Lahey Health has deployed the Heidi AI clinical documentation scribe across its health system.
Walter Reed National Military Medical Center reports using AI scribe technology to automate clinical note-taking for its medical staff during patient visits.
The American Hospital Association reports on six health systems using ambient AI scribe tools to document clinical encounters during care delivery.
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