The Role of New AI-Powered Medical Devices in Relation to the Stress Level of Healthcare Professionals:

Are Health Institutions Forgetting That We Are Human?

By Ricardo Costa Val do Rosario

A - Talking about Types of Stress

1. Eustress as a Performance Booster

 Eustress isn’t just “pleasant anxiety”, it improves focus, learning and problem-solving when kept at moderate levels. 
 This is formalized by the Yerkes–Dodson law, which shows that peak performance occurs at moderate arousal,
 not too low, not too high.  

2. Stress Tolerance Is Fluid 

Your ability to handle stress isn’t fixed. It fluctuates with life stages, physical health and environmental factors. 
What overwhelms you today might energize you in a few months, or vice-versa.  

3. Perfectionism Can Be Counterproductive 

Striving for flawlessness often backfires it raises stress, fuels procrastination and lowers satisfaction. 
Embracing “good enough” frees up mental bandwidth for creativity and genuine growth.  

4. Micro-Breaks Are Microsized Stress Busters  

Short, frequent pauses—like a 60-second stretch or a quick mindfulness check-in can lower cortisol 
and reset your focus more effectively than a single, longer break.  

5. Movement and Massage Yield Double Benefits 

Walking or light exercise boosts endorphins and brain-derived neurotrophic factor (BDNF), sharpening 
cognition. 

Pairing that with a gentle massage reduces muscle tension and stimulates oxytocin, giving both physical
relief and emotional calm.  

6. Decisiveness as a Stress Filter  

Research shows that people who make quick, firm choices experience less internal turmoil. Every postponed 
decision pile up “mental clutter,” intensifying perceived stress.  

7. Guilt Traps and the “Stress Spiral” 

Feeling guilty about feeling stressed creates a feedback loop: guilt adds more stress, which fuels more guilt. 
Interrupting this spiral—by acknowledging that stress is a natural signal—can immediately ease tension.  

B - AI in the Health Sector: New Technologies and Their Impact on Health Workers

1. Automating Administrative Tasks

AI systems can handle scheduling, billing, charting and other routine paperwork that today consume as much as 
70 % of a clinician’s time. 

By offloading these processes, practitioners can devote more attention to direct patient care, improving 
outcomes and cutting burnout rates.  

2. Evolving Roles: Substitution → Support → Strengthening Rather than simply Replacing jobs

AI “task engines” tend to:  

- Substitute discrete, repetitive tasks (e.g., initial image reads).  

- Support clinicians by flagging anomalies or suggesting treatment options.  

- Strengthen expert judgment through predictive analytics and pattern detection.  

This four-fold framework means existing roles will morph rather than vanish, shifting emphasis 
from data entry to data oversight.  

3. New Skill Requirements and Upskilling

Health workers now need: 

- Digital literacy for AI platforms and dashboards.  

- Statistical fluency to interpret model outputs and confidence intervals.  

- Ethical training around bias, consent and data governance. 

For example, clinical AI is often classified into three modes, each demanding progressively 
deeper engagement and oversight from staff:

-	Assistive. 
-	Augmentative.
-	Autonomous.

4. Workforce Transformation Instead of Wholesale Reduction

A 2030 shortage of roughly 9.9 million physicians, nurses and midwives globally will persist, even as
40 millions new health-sector jobs emerge. 

AI can help bridge that gap by boosting productivity but won’t eliminate the need for human caregivers.  

New occupations, such as AI-clinical liaison, data steward and algorithmic auditor, are already appearing
in hospital org-charts and creating pathways for career growth. 

5. Psychological and Organizational Consequences

Introducing AI can trigger:  

- Anxiety over obsolescence or deskilling.  

- Role ambiguity as traditional tasks are reallocated.  

- Change fatigue if staff aren’t brought into decision-making early.  

- Successful deployments pair technology with:

• Structured change-management.

•  Transparent communication.

• Peer-to-peer upskilling cohorts.  

6. Policy, Ethics and Planning for the Future

To steer this transition responsibly, health systems must:

1. Co-design AI roadmaps with frontline workers, patients and professional bodies.  

2. Embed continuous education in clinical curricula and licensure requirements.  

3. Establish governance frameworks for fairness, privacy and safety.  

4. Monitor workforce analytics to track evolving skill gaps and redeploy staff effectively.