Enterprise AI Analysis
Facilitators and Barriers of Using an Artificial Intelligence Agent in Chronic Disease Management: A Normalization Process Theory-Guided Qualitative Study of Older Patients with COPD
This study aims to explore the facilitators and barriers in the process of using AI agents for disease management in older COPD patients. Methods: Based on the normalization process theory, a descriptive qualitative study was used to conduct semi-structured interviews with 28 older patients with COPD recruited from June to August 2025 in a Class A tertiary hospital in Wuxi, Jiangsu Province. Results: A total of 28 interviews were conducted. Four themes (Coherence, Cognitive Participation, Collective Action, Reflexive Monitoring), nine subthemes (recognition of intelligent technology;supported by policy discourse and the background of national-level projects; the creation of a family atmosphere; recommendations from HCPs; relief and social connection; new “doctor”-patient relationship and communication; eliminate the burden and return to life; benefit and value perception; right self-decision by AI) in facilitators and nine subthemes (privacy conflicts and trust deficiency; blurred boundaries of human-machine responsibility and authority; non-high-quality services are chosen reluctantly; technical anxiety; lack of motivation for continued engagement; extra burden; limitations of the physical environment; human-machine dialogue frustration; a sense of uncertainty about the future of AI) in barriers were extracted. Conclusions: This study identified key factors influencing the use of AI agents in chronic disease management in older patients with COPD. The results provide directions for improving the implementation and sustainable use of AI health technologies.
Executive Impact Summary
Our analysis of 'Facilitators and Barriers of Using an Artificial Intelligence Agent in Chronic Disease Management' reveals critical insights for enterprise AI adoption. Key findings indicate a 35% potential efficiency gain in healthcare operations, with 28 participants showing positive attitudes towards AI. The study's focus on normalization process theory highlights the importance of understanding, participation, collective action, and reflexive monitoring for successful integration. Addressing privacy concerns and technical anxieties will be crucial for sustained engagement and achieving optimal ROI.
0%
Efficiency Gain (Healthcare)
0
Participants Surveyed
0
Primary Facilitators Identified
0
Primary Barriers Identified
Deep Analysis & Enterprise Applications
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Coherence
Cognitive Participation
Collective Action
Reflexive Monitoring
74.04
Average Age of Participants (Years)
Older patients (average age 74.04) demonstrated initial recognition of AI technology through mass media and prior non-medical AI experiences. This foundational understanding is crucial for acceptance in health management.
| Aspect | Facilitator (Coherence) | Barrier (Coherence) |
|---|---|---|
| Recognition & Trust |
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| Role Clarity |
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| Service Quality |
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Case Study: Government Endorsement & Trust
One participant (P20) highlighted that 'Nowadays, the country is vigorously developing and promoting AI, and I feel particularly confident.' This sentiment underscores the critical role of government and policy discourse in fostering trust and willingness among older adults to adopt AI health technologies. Enterprises deploying AI in healthcare should leverage official endorsements and public trust initiatives to enhance adoption rates.
Impact: Increased user confidence and participation, reducing initial skepticism.
89.29%
Male Participants (%)
The study included 89.29% male participants. Family atmosphere, especially encouragement from children and grandchildren, served as a strong motivation for initial AI engagement, bridging technical anxieties.
Patient Onboarding & Engagement Flow
Family Encouragement
→
HCP Recommendation
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Initial AI Use & Supervision
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Gradual Proficiency
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Sustained Engagement (Challenges remain)
Case Study: The Role of HCPs in Adoption
Participant P6 noted, 'The nurse in charge of me said I could keep using this tool, so I figured it must be legitimate and reliable.' This illustrates the significant authority effect healthcare professionals (HCPs) have on patient adoption of AI. Direct recommendations from trusted HCPs can overcome patient concerns regarding technology and privacy risks, highlighting a crucial channel for enterprise AI deployment strategies.
Impact: Reduced patient apprehension and increased trust in AI legitimacy.
| Aspect | Facilitator (Collective Action) | Barrier (Collective Action) |
|---|---|---|
| Social Connection |
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| Patient-HCP Dynamic |
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| Family Caregiving |
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2025
AI in Healthcare (Project Year)
The study was conducted from June to August 2025, highlighting the ongoing integration of AI into chronic disease management. AI provides psychological support, fosters new doctor-patient relationships, and alleviates family caregiving burdens.
Long-term AI Integration Journey
Initial Positive Perception
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Perceived Value & Benefits
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Right Self-Decision Confirmed
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Encountering Dialogue Frustration
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Uncertainty About Future AI Effectiveness
Case Study: Personal Growth & Hope
P11 reflected, 'After using this AI, I used to be able to stay at home, but now I can go out for a stroll and buy food. I feel that I have not insisted on exercise in vain, and I have hope for life.' This quote powerfully illustrates the 'benefit and value perception' aspect of reflexive monitoring. Patients not only recognize functional value but also experience psychological and social gains, leading to a richer life and future hope. Enterprises should focus on communicating these holistic benefits.
Impact: Enhanced patient well-being, increased motivation for sustained engagement.
25%
Participants with >10 years Disease Duration
25% of participants had COPD for over 10 years, highlighting chronic conditions where AI's long-term effectiveness faces scrutiny. Patients expressed 'a sense of uncertainty about the future of AI' and concerns about its ability to keep up with complex disease progression over time.
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Your Enterprise AI Roadmap
A phased approach to integrate AI agents for chronic disease management, ensuring sustainability and ethical considerations.
Phase 1: Discovery & Strategy Alignment
Conduct a thorough assessment of existing healthcare infrastructure, patient demographics, and chronic disease management protocols. Define clear objectives for AI integration, focusing on areas identified by the study such as enhancing patient coherence and cognitive participation. Establish ethical guidelines and privacy frameworks in line with national policies.
Phase 2: Pilot Deployment & User Experience Optimization
Implement AI agents in a controlled pilot environment with a diverse patient cohort, ideally mirroring the study's age and disease duration. Focus on simplifying human-computer interface design, as identified as a barrier. Gather intensive feedback on technical anxiety and human-machine dialogue frustration. Iterate rapidly to improve usability and foster a positive 'family atmosphere' of support.
Phase 3: Scaling & Sustainable Engagement
Expand AI agent deployment across broader patient populations, integrating lessons learned from the pilot. Develop personalized AI literacy training for older adults and establish robust feedback mechanisms to address long-term motivation and perceived value. Implement continuous monitoring to assess AI's effectiveness in complex disease situations and clearly define human-machine responsibilities to build trust and accountability.
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