AI, wearables and the health care workforce
Five to seven slides for ten minutes means roughly ninety seconds each, and the verb is evaluate. Naming one specific occupation is what stops this becoming a survey of future technology.
Editorial process
Last reviewed · August 15, 2026
Two technologies, one named sector, and a voice-over
Two constraints shape everything. The first is arithmetic: ten minutes across five to seven slides is about ninety seconds per slide, which is enough for one idea developed properly and not enough for a survey of everything happening in health technology. The second is the verb. Evaluate means weighing, so a presentation that describes what AI and wearables can do has not done the task — it needs to reach judgements about magnitude, timing and who is affected. The brief also asks you to choose a specific health care service, facility, or sector-related occupation, and that choice does most of the work. Radiology, medical coding, home health nursing, remote patient monitoring and medical transcription all face very different exposure, and naming one lets you say something concrete instead of hedging across the whole industry, which is what most presentations on this topic end up doing.
The most defensible framing is task-level rather than job-level. Occupations are rarely eliminated wholesale; particular tasks within them are automated or augmented, and the job reshapes around what remains. A coder's straightforward encounters become machine-suggested while complex and ambiguous ones stay human and the role tilts toward audit and query. Wearables generate continuous data that has to be triaged by somebody, which creates work as well as removing it — a point worth making because most presentations treat technology as purely subtractive. Then say what it means for the workforce in real terms: which skills gain value, what retraining would be required, and what governance is needed given bias and validation concerns. Remember the deliverables are a deck with notes plus a separate one-page document to read from, and the notes are where your argument lives since the slides carry only headlines. Marks for evaluation are earned in the notes, not on the deck.
Likely learning objectives
Inferred from the brief — check these against your own rubric.
- 01Evaluate a technology's workforce effects rather than describing its capabilities.
- 02Analyse automation at the level of tasks rather than whole occupations.
- 03Identify the governance requirements that accompany clinical AI deployment.
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This is a public forum for the class. Individual Forum: This is a private forum to ask me questions or send me messages. This will be checked at least once every 24 hours. Discussion: Development of artificial intelligence
Turn the brief into deliverables
- 01A 5-7 slide presentation designed for ten minutes of narration.
- 02Speaker notes carrying the argument, with headline-only slides.
- 03A one-page document to read from.
- 04At least three reputable references in APA format.
Building five to seven slides that evaluate
Name the occupation and why it is exposed
Choose one specific role and state what makes it susceptible to AI or wearable-driven change.
What the technology actually does now
Describe current deployed capability rather than projected capability.
Task-level effects
Identify which tasks are automated, which are augmented and which remain human.
Work created as well as removed
Show that continuous data and model oversight generate new roles and demands.
Governance and skills
State what validation, bias monitoring and retraining would be required.
Governance sources and workforce projections
Recommended databases
- World Health Organization
- Bureau of Labor Statistics Occupational Outlook Handbook
- JAMA and NEJM AI
- Office of the National Coordinator for Health IT
- PubMed Central
Search sequence
- 1.Use the Occupational Outlook Handbook for baseline employment projections in your chosen occupation.
- 2.Read the WHO guidance for the governance section rather than inventing requirements.
- 3.Search for evaluations of deployed clinical AI rather than announcements of new models.
- 4.Look for evidence on remote monitoring workload, which supports the work-created argument.
Reference shortlist
These are authoritative starting points, not a ready-made bibliography. A qualified reviewer must confirm that each source fits the assignment and supports the claim beside which it is cited.
Nothing here is cleared for citation until you have read it.
- 01
Ethics and governance of artificial intelligence for health
World Health Organization · 2021
The governance framing for AI in health services, useful for the workforce-effects question this presentation has to answer.
- 02
The association between perceived electronic health record usability and professional burnout among US nurses
Journal of the American Medical Informatics Association, 28(8), 1632-1641 · 2021
Measured evidence that new clinical technology reshapes workload rather than simply reducing it.
- 03
Interoperability
Office of the National Coordinator for Health Information Technology · 2024
Explains the data infrastructure wearables depend on, which bounds how quickly the workforce effects arrive.
- 04
About CAHPS
Agency for Healthcare Research and Quality · 2024
A reminder that patient-reported outcomes still require human collection and interpretation, which is work these technologies do not remove.
Review before submission
Common mistakes
- Describing capabilities instead of evaluating consequences.
- Covering the whole health sector rather than the one occupation the brief asks for.
- Treating technology as purely job-destroying and ignoring the work it creates.
- Putting the full argument on the slides and leaving the notes empty.
Submission checklist
- Is one specific service, facility or occupation named?
- Does the presentation reach judgements rather than describe?
- Is the analysis at task level rather than job level?
- Are the deck, notes, one-page document and three references all present?
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Written by
Aaron Bishop
MA, Education
assignment interpretation and research-methods coaching across disciplines
Aaron leads the EssayCrackers editorial desk. He works on how assignment briefs are read — what a rubric is actually asking for, and where students most often answer a different question than the one set.

Reviewed by
Dr. Nathan Cole
PhD, Rhetoric & Composition
Argumentation and thesis development
Nathan teaches first-year composition and directs a university writing center. He reviews EssayCrackers guides for argumentative soundness and citation accuracy.