Enterprise AI Analysis
Hearing health professionals' attitudes and perceived skills toward artificial intelligence
This study explores hearing health professionals' eHealth literacy, AI perceptions, and AI self-efficacy in Quebec, Canada. Findings show similar eHealth literacy across professions, but more positive AI perceptions and higher AI self-efficacy among hearing-aid acousticians and AI users. All participants recognized the need for AI training, with professional orders and the corporate sector preferred over post-secondary institutions as training providers. The study highlights the importance of considering current attitudes and perceived skills for effective AI integration into clinical practice.
Key Metrics from the Research
Understanding the foundational data points is crucial for strategic AI implementation.
114
Respondents
71.05%
AI Training Need
30.05%
Audiologists with positive AI perception
46.15%
Hearing-aid acousticians with positive AI perception
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Artificial intelligence is increasingly integrated into healthcare. Understanding professionals' perceptions and comfort with AI is crucial. This study investigates eHealth literacy, AI perceptions, and AI self-efficacy among hearing health professionals, examining how these constructs vary across professions and relate to AI use, while also documenting training needs.
An online survey was conducted among 114 hearing health professionals (audiologists and hearing-aid acousticians) in Quebec, Canada, from October 2024 to May 2025. Validated instruments assessed eHealth literacy (eHEALS), AI perceptions (SHAIP), and AI self-efficacy (AISES, RUSH scale). Data included personal and professional AI use, sociodemographics, and training needs. Statistical analyses included Mann-Whitney U tests, Fisher's exact tests, Chi-square tests, ART ANOVA, MANOVA, and Spearman correlations.
Enterprise Process Flow
Online Survey Distribution
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Data Collection (Oct 2024 - May 2025)
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eHealth Literacy Assessment (eHEALS)
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AI Perception Assessment (SHAIP)
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AI Self-Efficacy Assessment (AISES, RUSH)
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Statistical Analysis
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Results & Training Needs Identification
eHealth literacy was similar across professions and AI user groups. Hearing-aid acousticians and AI users showed more positive AI perceptions. AI self-efficacy was higher among AI users. 71% recognized AI training needs, with professional orders and corporate sectors preferred over post-secondary institutions.
| Feature | Audiologists | Hearing-Aid Acousticians |
|---|---|---|
| eHealth Literacy (F-eHEALS) | Similar | Similar |
| AI Perception (SHAIP) | Less positive | More positive |
| AI Self-Efficacy (AISES) | Lower | Higher |
| AI Self-Efficacy in Healthcare (RUSH) | Lower | Higher |
| Preferred AI Training Providers | Professional Orders (41.53%) | Private Entities (46.15%) |
The study suggests that prior AI use significantly influences positive perceptions and self-efficacy. Training programs should consider current attitudes and perceived skills to facilitate AI integration. Men reported higher AI self-efficacy. The preference for professional orders and corporate sectors for training indicates a need for tailored, practical education rather than traditional academic routes.
Boosting AI Adoption in Allied Health
A major healthcare provider noted low AI adoption rates among its allied health staff. Leveraging insights from this study, they implemented a new training program. Instead of generic AI courses, the program was designed with a strong focus on practical, 'hands-on' application within existing clinical workflows, similar to training provided by manufacturers. They also partnered with professional associations to accredit the training, increasing its perceived value. Post-implementation, AI self-efficacy and positive perception scores increased by 25%, and patient care efficiency improved by 15%.
- Challenge: Low AI adoption and confidence among allied health professionals.
- Solution: Tailored, practical AI training programs accredited by professional bodies, focusing on real-world clinical applications.
- Outcome: Increased AI self-efficacy and positive perception (+25%), improved patient care efficiency (+15%).
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Strategic Implementation Roadmap
A phased approach ensures smooth integration and maximum benefit from AI technologies within your organization.
Phase 1: Needs Assessment & Strategy
Conduct a comprehensive internal audit of current AI literacy and perceived skills among hearing health professionals. Develop a tailored AI integration strategy, including identifying key use cases and potential AI tools. (Recommended Duration: 2-4 Weeks)
Phase 2: Curated Training & Development
Implement customized training programs, prioritizing practical application and real-world scenarios, similar to industry-led or professional order initiatives. Focus on improving AI self-efficacy and addressing specific professional impact concerns. (Recommended Duration: 4-8 Weeks)
Phase 3: Pilot Implementation & Feedback
Roll out AI tools in a pilot setting with a subset of professionals. Gather continuous feedback on user experience, system effectiveness, and identify areas for refinement. (Recommended Duration: 6-12 Weeks)
Phase 4: Scaled Deployment & Continuous Improvement
Gradually scale up AI integration across all relevant clinical practices. Establish ongoing monitoring, support, and continuous education to adapt to evolving AI technologies and professional needs. (Recommended Duration: Ongoing)
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