AI exposure by occupation, O*NET 29-2099.08
Will AI replace Patient Representatives?
Patient Representatives: AI can already handle 4 of 13 tasks; the work mostly needs a standard-tier model.
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Every task, scored
The 13 tasks O*NET 30.3 lists for Patient Representatives, from most to least exposed. 4 reach 60 or more, the level at which AI already handles a task.
| Task | AI exposure | Evidence | Model tier |
|---|---|---|---|
| Develop and distribute newsletters, brochures, or other printed materials to share information with patients or medical staff. | Observed | Efficient | |
| Provide consultation or training to volunteers or staff on topics, such as guest relations, patients' rights, or medical issues. | Estimated | Standard | |
| Identify and share research, recommendations, or other information regarding legal liabilities, risk management, or quality of care. | Estimated | Standard | |
| Collect and report data on topics, such as patient encounters or inter-institutional problems, making recommendations for change when appropriate. | Estimated | Efficient | |
| Coordinate communication between patients, family members, medical staff, administrative staff, or regulatory agencies. | Estimated | Frontier | |
| Read current literature, talk with colleagues, continue education, or participate in professional organizations or conferences to keep abreast of developments in the field. | Estimated | Standard | |
| Explain policies, procedures, or services to patients using medical or administrative knowledge. | Observed | Efficient | |
| Teach patients to use home health care equipment. | Estimated | Efficient | |
| Interview patients or their representatives to identify problems relating to care. | Estimated | Efficient | |
| Refer patients to appropriate health care services or resources. | Estimated | Efficient | |
| Investigate and direct patient inquiries or complaints to appropriate medical staff members and follow up to ensure satisfactory resolution. | Estimated | Efficient | |
| Analyze patients' abilities to pay to determine charges on a sliding scale. | Estimated | Efficient | |
| Maintain knowledge of community services and resources available to patients. | Estimated | Efficient |
Observed: scored from AI use recorded in the Anthropic Economic Index (June 2026 release), 2 of 13 tasks. Estimated: scored from the task's O*NET 30.3 work activity, 11 of 13. Importance is O*NET's 1 to 5 rating of how central the task is. Model tier is the lowest tier that can do the task under the published rubric; "No tier" marks hands-on work.
The model tier this role needs
A standard-tier model or lower covers 91% of the role's importance-weighted task work. 100% of the work is something AI can address at some tier.
Efficient, standard and frontier tiers come from the JobRoute model tier reference. The recommended tier is the lowest one that covers 80% of the work AI can address. How the method works.
Wages, employment and outlook
- US employment
- 182,610
- Median annual wage
- $50,290
- Middle half of earners
- $44,170 to $64,340
- Projected change, 2024 to 2034
- +5.2%
Employment and wages: BLS Occupational Employment and Wage Statistics, May 2025, national. Projection: BLS Employment Projections 2024 to 2034. BLS publishes these figures by SOC code, so O*NET occupations that share a SOC code show the same figures.
Adjacent roles
The occupations that reuse the most of this role's skills, with the usual time to move. These are the first places to look when you plan to redeploy or retrain people in this role.
- Customer Service Representatives 61% skill overlap, about 14 months to move Exposure44
- Medical Secretaries and Administrative Assistants 59% skill overlap, about 15 months to move Exposure63
- Healthcare Social Workers 55% skill overlap, about 16 months to move Exposure43
- Community Health Workers 54% skill overlap, about 16 months to move Exposure45
- Receptionists and Information Clerks 54% skill overlap, about 17 months to move Exposure72
Skill overlap and transition time from the JobRoute role adjacency graph on O*NET 30.3 skills.
Questions about Patient Representatives and AI
Will AI replace Patient Representatives?
AI already handles 4 of the 13 tasks O*NET lists for this occupation, at a task exposure of 60 or more, and the role's importance-weighted exposure is 42 out of 100. JobRoute scores tasks, not whole jobs, so the score shows how much of the work changes rather than whether the job disappears. BLS projects employment to change by +5.2% from 2024 to 2034.
What is the AI exposure of Patient Representatives?
42 out of 100 on JobRoute's task exposure measure, the importance-weighted average of the role's O*NET task scores. Higher means more of the work is AI-doable today. 2 of the 13 task scores are observed in the Anthropic Economic Index (June 2026 release), and the other 11 are estimated from each task's O*NET work activity.
Which Patient Representatives tasks can AI already do?
The tasks with the highest exposure are: Develop and distribute newsletters, brochures, or other printed materials to share information with patients or medical staff; Provide consultation or training to volunteers or staff on topics, such as guest relations, patients' rights, or medical issues; Identify and share research, recommendations, or other information regarding legal liabilities, risk management, or quality of care. They are three of the 4 tasks that reach 60 or more.
What AI model tier does this role need?
A standard-tier model or lower covers 91% of the role's importance-weighted task work. JobRoute assigns each task the lowest tier that can do it under a published rubric and recommends the lowest tier that covers 80% of the work AI can address.
How many people work as Patient Representatives, and what do they earn?
182,610 people in the United States, with a median annual wage of $50,290 (BLS Occupational Employment and Wage Statistics, May 2025). BLS Employment Projections expect a change of +5.2% from 2024 to 2034.
How to cite
Data version 2026-11-02, JobRoute method v1.0.0. Download all occupations or this occupation as JSON. Methodology.
JobRoute (2026). AI exposure: Patient Representatives. Data version 2026-11-02, method v1.0.0. Built on O*NET 30.3, BLS OEWS May 2025 and the Anthropic Economic Index (June 2026). CC BY 4.0. https://www.jobroute.ai/jobs/patient-representatives
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