⚡ Quick Summary
This study developed an implementation strategy for artificial intelligence (AI) in radiotherapy (RT) using implementation science methods. The results indicated a high level of acceptability (90%) and feasibility (75%) among participants, emphasizing the importance of multidisciplinary collaboration in AI integration.
🔍 Key Details
- 📊 Participants: 20 stakeholders from seven Dutch RT centers
- 🧩 Methods used: Stakeholder analysis, literature review, and interviews
- ⚙️ Focus: Identifying barriers and facilitators for AI implementation
- 🏆 Evaluation metrics: Acceptability (90%), Appropriateness (85%), Feasibility (75%)
🔑 Key Takeaways
- 🤖 AI applications in radiotherapy have the potential to save time and improve quality.
- 🔍 Implementation science provides a structured approach to develop AI strategies.
- 👥 Stakeholder involvement is crucial for successful AI integration.
- ⚠️ Identified barriers include concerns about privacy, data quality, and ethics.
- 💡 Facilitators include knowledge sharing and multidisciplinary collaboration.
- 🏥 High acceptability of the implementation strategy suggests it can be adapted across various centers.
- 🌍 Insights gained highlight the need for tailored strategies in different RT departments.
📚 Background
The integration of artificial intelligence in healthcare, particularly in radiotherapy, holds great promise for enhancing patient care and operational efficiency. However, the actual implementation of AI technologies has been limited due to various challenges. This study aimed to address these challenges by applying implementation science to create a structured strategy for AI integration in radiotherapy departments.
🗒️ Study
Conducted at a radiotherapy center in the Netherlands, the study utilized implementation science methods to develop a center-specific AI implementation strategy. This involved a comprehensive stakeholder analysis, literature review, and interviews to identify the key barriers and facilitators affecting AI adoption. The findings were then shared in a workshop with teams from seven Dutch RT centers to collaboratively develop their own AI implementation plans.
📈 Results
The stakeholder analysis revealed a diverse range of internal and external stakeholders, including physicians, physicists, RT technicians, and patients. The evaluation of the workshop indicated a high level of acceptability (90%), appropriateness (85%), and feasibility (75%) for the proposed implementation strategy. Participants expressed strong agreement on the usefulness of the methods employed.
🌍 Impact and Implications
The findings from this study underscore the necessity of a collaborative approach to successfully implement AI in radiotherapy. By addressing organizational challenges and fostering multidisciplinary collaboration, the proposed strategy can significantly enhance the integration of AI technologies, ultimately leading to improved patient care and operational efficiency across various RT centers.
🔮 Conclusion
This study highlights the critical role of implementation science in facilitating the adoption of AI in radiotherapy. The positive feedback from workshop participants indicates that the developed strategies are not only feasible but also adaptable to different settings. As we move forward, it is essential to continue fostering collaboration among stakeholders to maximize the benefits of AI in healthcare.
💬 Your comments
What are your thoughts on the integration of AI in radiotherapy? Do you believe that implementation science can effectively address the challenges faced? 💬 Share your insights in the comments below or connect with us on social media:
Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?
Abstract
PURPOSE: Artificial intelligence (AI) applications in radiotherapy (RT) are expected to save time and improve quality, but implementation remains limited. Therefore, we used implementation science to develop a format for designing an implementation strategy for AI. This study aimed to (1) apply this format to develop an AI implementation strategy for our center; (2) identify insights gained to enhance AI implementation using this format; and (3) assess the feasibility and acceptability of this format to design a center-specific implementation strategy for departments aiming to implement AI.
METHODS: We created an AI-implementation strategy for our own center using implementation science methods. This included a stakeholder analysis, literature review, and interviews to identify facilitators and barriers, and designed strategies to overcome the barriers. These methods were subsequently used in a workshop with teams from seven Dutch RT centers to develop their own AI-implementation plans. The applicability, appropriateness, and feasibility were evaluated by the workshop participants, and relevant insights for AI implementation were summarized.
RESULTS: The stakeholder analysis identified internal (physicians, physicists, RT technicians, information technology, and education) and external (patients and representatives) stakeholders. Barriers and facilitators included concerns about opacity, privacy, data quality, legal aspects, knowledge, trust, stakeholder involvement, ethics, and multidisciplinary collaboration, all integrated into our implementation strategy. The workshop evaluation showed high acceptability (18 participants [90%]), appropriateness (17 participants [85%]), and feasibility (15 participants [75%]) of the implementation strategy. Sixteen participants fully agreed with the format.
CONCLUSION: Our study highlights the need for a collaborative approach to implement AI in RT. We designed a strategy to overcome organizational challenges, improve AI integration, and enhance patient care. Workshop feedback indicates the proposed methods are useful for multiple RT centers. Insights gained by applying the methods highlight the importance of multidisciplinary collaboration in the development and implementation of AI.
Author: [‘Swart R’, ‘Boersma L’, ‘Fijten R’, ‘van Elmpt W’, ‘Cremers P’, ‘Jacobs MJG’]
Journal: JCO Clin Cancer Inform
Citation: Swart R, et al. Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?. Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?. 2024; 8:e2400101. doi: 10.1200/CCI.24.00101