Do clinicians want AI help for multimorbidity decisions?
Twenty HCPs saw AI as potentially safer for MLTC decisions but required trust, time-saving integration, transparent rationale, and preserved human final say.
Source
Perspectives of Health Care Professionals on the Use of AI to Support Clinical Decision-Making in the Management of Multiple Long-Term Conditions: Interview Study
What they did
Interviewed 20 HCPs (GPs, geriatricians, nurses, pharmacists) about MLTC management and used a patient case study to probe how an AI tool might change decisions, organizing themes with Buck’s determinants of attitudes toward AI.
What they found
MLTC work meant balancing competing factors/social context, managing polypharmacy, and working beyond single-condition guidelines. HCPs expected AI to improve safety/quality but worried about harming the therapeutic relationship. Adoption prerequisites: public/patient trust, time savings/system integration, and visible rationale with experienced clinician final decision.
The limits
What it doesn't show
Interview attitudes do not measure real-world AI performance, safety, or adoption after deployment.
Key terms
- MLTC
- Multiple long-term conditions (multimorbidity).
- Polypharmacy
- Use of many medicines with interaction/burden risks.
- AI decision support
- Tools proposing clinical recommendations from data.
- Therapeutic relationship
- Trusting clinician–patient alliance central to complex care.
- Explainability
- Making the rationale behind an AI recommendation apparent.
Flashcards
Research intelligence for this paper
See its role on concept claims, tensions it is part of, placement history, and related discoveries.
Quiz yourself
Interview sample size:
Common questions
How many HCPs?
20 interviewees.
Three MLTC themes?
Competing factors/social context, polypharmacy, beyond single-disease guidelines.
Main AI hope?
Safer, higher-quality decisions.
Main AI fear?
Damaging the clinician–patient relationship.