Competency Framework for Artificial Intelligence in Healthcare
Discover the Competencies for Your Future in Healthcare!
Start now
Competency Framework for AI Integration in Healthcare
Competencies: A Lever for AI Adoption
In 2020, the CHUM School of Artificial Intelligence in Healthcare (SAIH) developed the Competency Framework for Artificial Intelligence in Healthcare to help train healthcare professionals and develop the competencies they will need in the future.
Competency Framework for AI Integration in Healthcare
A Unique Competency Model
Similar to a roadmap, the framework serves as a guide to target behaviours, learning objectives, and development priorities to effectively deploy your competencies.
Click here to watch a brief explanatory video.
Competency Framework for AI Integration in Healthcare
The Added Value of the Framework
This framework is an essential tool for evaluating your current competencies and identifying those you still need to develop or acquire in the coming years to support the integration of AI in healthcare for the benefit of patients.
Competency Framework for AI Integration in Healthcare
The Added Value of the Framework
By focusing on target behaviours, you can pinpoint specific areas you need to work on to master a particular competency.
Competency Framework for AI Integration in Healthcare
Getting Started
The first step is to embrace the competencies of your future in healthcare to lay the foundation for your professional development.
Competency Framework for AI Integration in Healthcare
Module Objectives
Module 1 (15 minutes)
Examining the model to understand its mechanics.
1Exploration
Module 2 (5 minutes)
Reinforcing concepts related to learned competencies.
2Consolidation
Module 3(5 minutes)
Further extending acquired knowledge.
3Testing
Exploration
Competency Framework for AI Integration in Healthcare
In this first module, you will discover how the competency model is structured.
- Navigate the exploration plan
- Familiarize yourself with the possible views
- Click on the components of the interactive model to read its sections
- Take the time to grasp the model’s mechanics
Explore
Competency Framework for AI Integration in Healthcare
Exploration Plan
Discover the three explanatory sections by clicking the icon
General Structure
Overall View
Layer View
Biaxial Structure
Quadrant View
The 16 Competencies
Competency View
Overall View
Explore
Competency Framework for AI Integration in Healthcare
Overall View
Discover the model’s 2 distinct axes
Choose the layer view or the quadrant view to explore the framework. You can look at the competencies from 2 distinct perspectives.
Quadrant View
Layer View
Explore
Competency Framework for AI Integration in Healthcare
Layer View
Competency Levels
The framework includes 4 competency levels or layers, each of which is represented by a different colour. Click on each of the 4 layers of the interactive model to learn more.
Enabling Mindsets
Human Competencies
Specific Competencies
Human-AI interface Competencies
Quadrant View
Layer View
Explore
Competency Framework for AI Integration in Healthcare
Quadrant View
Competency Categories
The framework comprises 4 competency categories or quadrants, which are divided by two 2 perpendicular axes. Click on the 4 quadrants of the interactive model.
RELATIONAL Competencies
REFLECTIVE Competencies
SELF-ORIENTED Competencies
ACTION-ORIENTED Competencies
Quadrant View
Layer View
Explorer
Explore
Competency Framework for AI Integration in Healthcare
The Framework’s 16 Competencies
Each competency in the framework is structured as follows:
- Name: a descriptive name that evokes the targeted concept;
- Definition: a general description of the competency and its mastery;
- Key Elements: the distinct elements (typically 3) required for competency mastery.
Click on each of the words in the interactive model to explore the competencies in depth!
Consolidation
Competency Framework for AI Integration in Healthcare
In this second module, you will reinforce your understanding of the framework’s competencies. Let’s start by matching key behaviours or actions with the competencies to which they most relate.
Consolider
Competency Framework for AI Integration in Healthcare
Enabling Mindsets
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
Interdisciplinary Thinking:
Growth Mindset:
Scientific Thinking:
Entrepreneurial Mindset:
Consolider
Competency Framework for AI Integration in Healthcare
Human Competencies
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
Social Intelligence:
Analytical Thinking:
Engage and Inspire:
Creativity and Innovation:
Consolider
Competency Framework for AI Integration in Healthcare
Specific Competencies
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
Responsible AI:
Reinvented Learning:
Data Literacy:
Innovation-Cycle Application:
Consolider
Competency Framework for AI Integration in Healthcare
Human-AI interface competencies
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
AI Oversight:
Mindful Choices:
AI Training:
AI Translation:
Testing
Competency Framework for AI Integration in Healthcare
In this final module, you will learn to identify the key elements for competency mastery to better prepare for AI-related work transformations.
Answer the following questions by indicating whether the statement is true or false.
Testing
Competency Framework for AI Integration in Healthcare
Testing
Competency Framework for AI Integration in Healthcare
Testing
Competency Framework for AI Integration in Healthcare
Tester
Competency Framework for AI Integration in Healthcare
Tester
Competency Framework for AI Integration in Healthcare
Competency Framework for AI Integration in Healthcare
Necessary Preparation
Transformative technologies such as AI will continue to profoundly change how work is performed in the healthcare sector. The Competency Framework for Artificial Intelligence in Healthcare serves as a compass to guide individuals as AI-based systems are introduced into healthcare practices.
Competency Framework for AI Integration in Healthcare
Necessary Preparation
Gradual AI integration into all healthcare activities will necessitate upskilling and reskilling for all professionals involved.
Competency Framework for AI Integration in Healthcare
necessary preparation
Learning to use these technologies, considering their impacts, adapting to new working conditions, and continuously developing personal and relational competencies will be a priority for everyone contributing to improving tomorrow’s healthcare landscape.
Acknowledgments
The creation of this awareness tool was made possible through the financial support of the Quebec ministère de l’Économie, de l’Innovation et de l’Énergie through its NovaScience program. This program enables the implementation of innovative strategies and tools contributing to competency development to accelerate AI integration in healthcare.
Return to the main page
Reflective Competencies
Reflective competencies are conceptual and cognitive in nature; they involve ideas and leverage the human capacity for reflective and analytical thinking.
- AI Training
- Data Literacy
- Analytical Thinking
- Scientific Thinking
Axées sur la réflexion
Les compétences axées sur la réflexion, de l’ordre du conceptuel et du cognitif, font appel aux idées et à la capacité réflexive et analytique humaine.
- Entraînement IA
- Littératie des données
- Réflexion analytique
- Pensée scientifique
Human-AI Interface Competencies
The fourth and outermost layer comprises human-AI interface competencies that only come into play once teams start interacting with AI. They represent the various roles that need to be mastered once AI is implemented to continue effectively using it over time.
Growth Mindset
Continuously improving by adjusting one’s behaviour to the new demands of one’s
- Showing openness to feedback, change, and development
- Pushing one’s limits by making efforts to strengthen one’s abilities and potential
- Approaching challenges as opportunities to learn
Orientées vers soi
Les compétences orientées vers soi font appel à nos ressources personnelles.
- Choix conscients
- Apprentissage réinventé
- Engager et inspirer
- Posture de croissance
Data Literacy
Understanding the concepts, functioning, and relevance of AI in problem-solving through the ability to read, interpret, and use data
- Qualifying and manipulating collected or available data
- Interpreting and explaining results
- Experimenting with developing algorithms
Creativity and Innovation
Finding new or improved ways of doing things through the development, integration, evaluation, and sharing of ideas
- Encouraging the development of creative ideas
- Transforming an idea into a viable solution that delivers value
- Facilitating the implementation of changes in methodologies
Enabling Mindsets
The first and centremost layer focuses on competencies that relate to mindsets. Adopting these competencies is essential to foster changes that impact work culture, including digital transformations. These competencies are also essential for developing greater agility in adopting AI. They form the foundation for building the competencies of the next layer.
Human Competencies
The second layer comprises certain human competencies that are well known today but will likely play an increasingly important role in the coming years. A closer collaboration between humans and AI, where contributions from both intertwine, will make some tasks less useful and others more essential than ever. These competencies represent areas where humans currently surpass machines.
Innovation-Cycle Application
Transforming an innovative AI-based idea into a concrete project by utilizing and adapting available processes and tools
- Identifying a relevant problem to solve with the help of AI
- Researching and developing the AI-based solution
- Experimenting with and implement the solution, then measure its impacts to ensure sustainability
Mindful Choices
Making deliberate and thoughtful professional interventions based on one’s competencies, knowledge, and expertise
- Reflecting on decisions to be made
- Acting based on one’s judgment
- Taking responsibility for one’s action
Reflective Competencies
Reflective competencies are conceptual and cognitive in nature; they involve ideas and leverage the human capacity for reflective and analytical thinking.
- AI Training
- Data Literacy
- Analytical Thinking
- Scientific Thinking
Enabling Mindsets
The first and centremost layer focuses on competencies that relate to mindsets. Adopting these competencies is essential to foster changes that impact work culture, including digital transformations. These competencies are also essential for developing greater agility in adopting AI. They form the foundation for building the competencies of the next layer.
Human-AI Interface Competencies
The fourth and outermost layer comprises human-AI interface competencies that only come into play once teams start interacting with AI. They represent the various roles that need to be mastered once AI is implemented to continue effectively using it over time.
AI Translation
Combining expertise from data science and field experience to facilitate the implementation of AI based systems tailored to needs
- Simplifying data science knowledge for non-experts
- Contextualizing data based on industry knowledge
- Identifying obstacles and challenges related to the adoption of AI systems by field teams or organization
Engage and Inspire
Encouraging others to embrace change by providing direction, purpose, momentum, and by generating enthusiasm for it
- • Sharing a clear and meaningful vision
- • Strengthening healthcare actors’ commitment to transformation
- • Encouraging individual accountability
Specific Competencies
The third layer comprises AI-specific competencies that should be mastered over time, but that will be easier to develop once a certain foundational level of competencies has been acquired. These technical skills and knowledge will make it easier for healthcare professionals to navigate a world in which data, algorithms, and AI play an increasingly important role.
Relational Competencies
Relational competencies are used during our social interactions and in our dealings with others in a work context.
- AI Oversight
- Responsible AI
- Social Intelligence
- Interdisciplinary Thinking
Entrepreneurial Mindset
Seeking and seizing opportunities with significant impacts by recognizing their potential and accepting their share of risks
- Being on the lookout for potential opportunities
- Daring to move forward despite the risks
- Accepting an iterative, incremental, and adaptive development cycle
Scientific Thinking
Basing decisions on verifiable facts after fully understanding the situation
- • Seeking relevant information to strengthen thinking and knowledge
- • Distinguishing between true and false information
- • Adopting a critical attitude towards information
Mechanical Logic
The Right Competencies in the Right Place
Competencies at the core are considered foundational for AI integration. Peripheral competencies add value as integration progresses. Moving through each quadrant from its core to its outermost layer helps learners identify adjacent competencies that promote talent development. For instance, an entrepreneurial mindset can be useful in achieving further competencies, such as creativity and innovation.
AI Oversight
Ensuring ongoing monitoring of the trustworthy use of AI-based systems
- Ensuring the social acceptability of AI-based systems by verifying results
- Identifying potentially undesirable and unanticipated impacts on human conditions and the living world
- Avoiding potential deviations by immediately intervening
Human Competencies
The second layer comprises certain human competencies that are well known today but will likely play an increasingly important role in the coming years. A closer collaboration between humans and AI, where contributions from both intertwine, will make some tasks less useful and others more essential than ever. These competencies represent areas where humans currently surpass machines.
A competency is defined as “a complex know-how, relying on the effective mobilization and utilization of a variety of internal and external resources within a family of situations” [translation] (Tardif, 2006).
Tardif, J. (2006). L’évaluation des compétences. Documenter le parcours de développement. Montréal : Chenelière Éducation
Distinct Design
Special attention was given to the model’s structure and design to maximize its practicality. Unlike a list of competencies, a biaxial model achieves this objective. The 2 distinct axes make it possible to simultaneously align the framework’s layers (competency levels) while delineating their respective quadrants (competency categories). In turn, the intersection of both axes allows for the positioning of the 16 competencies within the model. 4 layers × 4 quadrants = 16 competencies
Relational Competencies
Relational competencies are used during our social interactions and in our dealings with others in a work context.
- AI Oversight
- Responsible AI
- Social Intelligence
- Interdisciplinary Thinking
Specific Competencies
The third layer comprises AI-specific competencies that should be mastered over time, but that will be easier to develop once a certain foundational level of competencies has been acquired. These technical skills and knowledge will make it easier for healthcare professionals to navigate a world in which data, algorithms, and AI play an increasingly important role.
Action-oriented Competencies
Action-oriented competencies focus on achieving results.
- AI Translation
- Innovation-Cycle Application
- Creativity and Innovation
- Entrepreneurial Mindset
Responsible AI
Considering the ethical issues and societal values underpinning the use of AI-based systems in healthcare for the benefit of humans
- Anticipating situations that may pose ethical and deontological issues
- Contributing to collective reflection to address ethical problems
- Acting with consideration for the consequences on safety and well-being
AI Training
Participating in the development and refinement of AI-based systems.
- Training algorithms in the use data sets
- Teaching algorithms how to incorporate certain human components
- Combining intelligences and learning from AI-based systems
Innovation-Cycle Application
Transforming an innovative AI-based idea into a concrete project by utilizing and adapting available processes and tools
- Identifying a relevant problem to solve with the help of AI
- Researching and developing the AI-based solution
- Experimenting with and implement the solution, then measure its impacts to ensure sustainability
Action-oriented Competencies
Action-oriented competencies focus on achieving results.
- AI Translation
- Innovation-Cycle Application
- Creativity and Innovation
- Entrepreneurial Mindset
Axées sur la relation
Les compétences axées sur la relation sont sollicitées au cours de nos interactions sociales et dans notre rapport aux autres dans le cadre du travail.
- Vigilance IA
- IA responsable
- Intelligence sociale
- Pensée interdisciplinaire
Interdisciplinary and Intersectorial Thinking
Adopting collaborative reflexes by integrating a diverse range of people into the search for better problem-solving and shared decision-making .
- Including different perspectives and approaches
- Engaging in exchanges to reach a higher common interest
- Considering the interdependencies between groups and the impacts of decisions on one another
Orientées vers l'action
Les compétences orientées vers l’action visent l’atteinte de résultats.
- Traduction IA
- Application du cycle de l'innovation
- Créativité et innovation
- Pensée entrepreneuriale
Self-oriented Competencies
Self-oriented competencies draw upon our personal resources.
- Mindful Choices
- Reinvented Learning
- Engage and Inspire
- Growth Mindset
Reinvented Learning
Engaging in continuous lifelong learning to adapt to the transformations brought about by AI
- Understanding the potential and scope of AI on one’s work and development needs
- Actively seeking learning opportunities by leveraging new learning methods
- Quickly applying new learnings
Social Intelligence
Responding to the needs of others in a manner appropriate to the situation by using verbal and non-verbal skills
- Paying attention to others through listening and observation
- Showing empathy
- Adopting a caring behaviour
Analytical Thinking
Understanding complex issues and concepts from multiple perspectives by recognizing logical connections between different pieces of information
- Gathering information from multiple sources
- Establishing associations between the information
- Making informed decisions regarding a given situation
Self-oriented Competencies
Self-oriented competencies draw upon our personal resources.
- Mindful Choices
- Reinvented Learning
- Engage and Inspire
- Growth Mindset
Reinvented Learning
Engaging in continuous lifelong learning to adapt to the transformations brought about by AI
- Understanding the potential and scope of AI on one’s work and development needs
- Actively seeking learning opportunities by leveraging new learning methods
- Quickly applying new learnings
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Transcript
Competency Framework for Artificial Intelligence in Healthcare
Discover the Competencies for Your Future in Healthcare!
Start now
Competency Framework for AI Integration in Healthcare
Competencies: A Lever for AI Adoption
In 2020, the CHUM School of Artificial Intelligence in Healthcare (SAIH) developed the Competency Framework for Artificial Intelligence in Healthcare to help train healthcare professionals and develop the competencies they will need in the future.
Competency Framework for AI Integration in Healthcare
A Unique Competency Model
Similar to a roadmap, the framework serves as a guide to target behaviours, learning objectives, and development priorities to effectively deploy your competencies.
Click here to watch a brief explanatory video.
Competency Framework for AI Integration in Healthcare
The Added Value of the Framework
This framework is an essential tool for evaluating your current competencies and identifying those you still need to develop or acquire in the coming years to support the integration of AI in healthcare for the benefit of patients.
Competency Framework for AI Integration in Healthcare
The Added Value of the Framework
By focusing on target behaviours, you can pinpoint specific areas you need to work on to master a particular competency.
Competency Framework for AI Integration in Healthcare
Getting Started
The first step is to embrace the competencies of your future in healthcare to lay the foundation for your professional development.
Competency Framework for AI Integration in Healthcare
Module Objectives
Module 1 (15 minutes)
Examining the model to understand its mechanics.
1Exploration
Module 2 (5 minutes)
Reinforcing concepts related to learned competencies.
2Consolidation
Module 3(5 minutes)
Further extending acquired knowledge.
3Testing
Exploration
Competency Framework for AI Integration in Healthcare
In this first module, you will discover how the competency model is structured.
Explore
Competency Framework for AI Integration in Healthcare
Exploration Plan
Discover the three explanatory sections by clicking the icon
General Structure
Overall View
Layer View
Biaxial Structure
Quadrant View
The 16 Competencies
Competency View
Overall View
Explore
Competency Framework for AI Integration in Healthcare
Overall View
Discover the model’s 2 distinct axes
Choose the layer view or the quadrant view to explore the framework. You can look at the competencies from 2 distinct perspectives.
Quadrant View
Layer View
Explore
Competency Framework for AI Integration in Healthcare
Layer View
Competency Levels
The framework includes 4 competency levels or layers, each of which is represented by a different colour. Click on each of the 4 layers of the interactive model to learn more.
Enabling Mindsets
Human Competencies
Specific Competencies
Human-AI interface Competencies
Quadrant View
Layer View
Explore
Competency Framework for AI Integration in Healthcare
Quadrant View
Competency Categories
The framework comprises 4 competency categories or quadrants, which are divided by two 2 perpendicular axes. Click on the 4 quadrants of the interactive model.
RELATIONAL Competencies
REFLECTIVE Competencies
SELF-ORIENTED Competencies
ACTION-ORIENTED Competencies
Quadrant View
Layer View
Explorer
Explore
Competency Framework for AI Integration in Healthcare
The Framework’s 16 Competencies
Each competency in the framework is structured as follows:
- Name: a descriptive name that evokes the targeted concept;
- Definition: a general description of the competency and its mastery;
- Key Elements: the distinct elements (typically 3) required for competency mastery.
Click on each of the words in the interactive model to explore the competencies in depth!Consolidation
Competency Framework for AI Integration in Healthcare
In this second module, you will reinforce your understanding of the framework’s competencies. Let’s start by matching key behaviours or actions with the competencies to which they most relate.
Consolider
Competency Framework for AI Integration in Healthcare
Enabling Mindsets
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
Interdisciplinary Thinking:
Growth Mindset:
Scientific Thinking:
Entrepreneurial Mindset:
Consolider
Competency Framework for AI Integration in Healthcare
Human Competencies
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
Social Intelligence:
Analytical Thinking:
Engage and Inspire:
Creativity and Innovation:
Consolider
Competency Framework for AI Integration in Healthcare
Specific Competencies
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
Responsible AI:
Reinvented Learning:
Data Literacy:
Innovation-Cycle Application:
Consolider
Competency Framework for AI Integration in Healthcare
Human-AI interface competencies
Match each example of a behaviour or action with the competency to which it most relates. Once done, click SUBMIT to see the correct answers.
AI Oversight:
Mindful Choices:
AI Training:
AI Translation:
Testing
Competency Framework for AI Integration in Healthcare
In this final module, you will learn to identify the key elements for competency mastery to better prepare for AI-related work transformations.
Answer the following questions by indicating whether the statement is true or false.
Testing
Competency Framework for AI Integration in Healthcare
Testing
Competency Framework for AI Integration in Healthcare
Testing
Competency Framework for AI Integration in Healthcare
Tester
Competency Framework for AI Integration in Healthcare
Tester
Competency Framework for AI Integration in Healthcare
Competency Framework for AI Integration in Healthcare
Necessary Preparation
Transformative technologies such as AI will continue to profoundly change how work is performed in the healthcare sector. The Competency Framework for Artificial Intelligence in Healthcare serves as a compass to guide individuals as AI-based systems are introduced into healthcare practices.
Competency Framework for AI Integration in Healthcare
Necessary Preparation
Gradual AI integration into all healthcare activities will necessitate upskilling and reskilling for all professionals involved.
Competency Framework for AI Integration in Healthcare
necessary preparation
Learning to use these technologies, considering their impacts, adapting to new working conditions, and continuously developing personal and relational competencies will be a priority for everyone contributing to improving tomorrow’s healthcare landscape.
Acknowledgments
The creation of this awareness tool was made possible through the financial support of the Quebec ministère de l’Économie, de l’Innovation et de l’Énergie through its NovaScience program. This program enables the implementation of innovative strategies and tools contributing to competency development to accelerate AI integration in healthcare.
Return to the main page
Reflective Competencies
Reflective competencies are conceptual and cognitive in nature; they involve ideas and leverage the human capacity for reflective and analytical thinking.
Axées sur la réflexion
Les compétences axées sur la réflexion, de l’ordre du conceptuel et du cognitif, font appel aux idées et à la capacité réflexive et analytique humaine.
Human-AI Interface Competencies
The fourth and outermost layer comprises human-AI interface competencies that only come into play once teams start interacting with AI. They represent the various roles that need to be mastered once AI is implemented to continue effectively using it over time.
Growth Mindset
Continuously improving by adjusting one’s behaviour to the new demands of one’s
Orientées vers soi
Les compétences orientées vers soi font appel à nos ressources personnelles.
Data Literacy
Understanding the concepts, functioning, and relevance of AI in problem-solving through the ability to read, interpret, and use data
Creativity and Innovation
Finding new or improved ways of doing things through the development, integration, evaluation, and sharing of ideas
Enabling Mindsets
The first and centremost layer focuses on competencies that relate to mindsets. Adopting these competencies is essential to foster changes that impact work culture, including digital transformations. These competencies are also essential for developing greater agility in adopting AI. They form the foundation for building the competencies of the next layer.
Human Competencies
The second layer comprises certain human competencies that are well known today but will likely play an increasingly important role in the coming years. A closer collaboration between humans and AI, where contributions from both intertwine, will make some tasks less useful and others more essential than ever. These competencies represent areas where humans currently surpass machines.
Innovation-Cycle Application
Transforming an innovative AI-based idea into a concrete project by utilizing and adapting available processes and tools
Mindful Choices
Making deliberate and thoughtful professional interventions based on one’s competencies, knowledge, and expertise
Reflective Competencies
Reflective competencies are conceptual and cognitive in nature; they involve ideas and leverage the human capacity for reflective and analytical thinking.
Enabling Mindsets
The first and centremost layer focuses on competencies that relate to mindsets. Adopting these competencies is essential to foster changes that impact work culture, including digital transformations. These competencies are also essential for developing greater agility in adopting AI. They form the foundation for building the competencies of the next layer.
Human-AI Interface Competencies
The fourth and outermost layer comprises human-AI interface competencies that only come into play once teams start interacting with AI. They represent the various roles that need to be mastered once AI is implemented to continue effectively using it over time.
AI Translation
Combining expertise from data science and field experience to facilitate the implementation of AI based systems tailored to needs
Engage and Inspire
Encouraging others to embrace change by providing direction, purpose, momentum, and by generating enthusiasm for it
Specific Competencies
The third layer comprises AI-specific competencies that should be mastered over time, but that will be easier to develop once a certain foundational level of competencies has been acquired. These technical skills and knowledge will make it easier for healthcare professionals to navigate a world in which data, algorithms, and AI play an increasingly important role.
Relational Competencies
Relational competencies are used during our social interactions and in our dealings with others in a work context.
Entrepreneurial Mindset
Seeking and seizing opportunities with significant impacts by recognizing their potential and accepting their share of risks
Scientific Thinking
Basing decisions on verifiable facts after fully understanding the situation
Mechanical Logic
The Right Competencies in the Right Place
Competencies at the core are considered foundational for AI integration. Peripheral competencies add value as integration progresses. Moving through each quadrant from its core to its outermost layer helps learners identify adjacent competencies that promote talent development. For instance, an entrepreneurial mindset can be useful in achieving further competencies, such as creativity and innovation.
AI Oversight
Ensuring ongoing monitoring of the trustworthy use of AI-based systems
Human Competencies
The second layer comprises certain human competencies that are well known today but will likely play an increasingly important role in the coming years. A closer collaboration between humans and AI, where contributions from both intertwine, will make some tasks less useful and others more essential than ever. These competencies represent areas where humans currently surpass machines.
A competency is defined as “a complex know-how, relying on the effective mobilization and utilization of a variety of internal and external resources within a family of situations” [translation] (Tardif, 2006).
Tardif, J. (2006). L’évaluation des compétences. Documenter le parcours de développement. Montréal : Chenelière Éducation
Distinct Design
Special attention was given to the model’s structure and design to maximize its practicality. Unlike a list of competencies, a biaxial model achieves this objective. The 2 distinct axes make it possible to simultaneously align the framework’s layers (competency levels) while delineating their respective quadrants (competency categories). In turn, the intersection of both axes allows for the positioning of the 16 competencies within the model. 4 layers × 4 quadrants = 16 competencies
Relational Competencies
Relational competencies are used during our social interactions and in our dealings with others in a work context.
Specific Competencies
The third layer comprises AI-specific competencies that should be mastered over time, but that will be easier to develop once a certain foundational level of competencies has been acquired. These technical skills and knowledge will make it easier for healthcare professionals to navigate a world in which data, algorithms, and AI play an increasingly important role.
Action-oriented Competencies
Action-oriented competencies focus on achieving results.
Responsible AI
Considering the ethical issues and societal values underpinning the use of AI-based systems in healthcare for the benefit of humans
AI Training
Participating in the development and refinement of AI-based systems.
Innovation-Cycle Application
Transforming an innovative AI-based idea into a concrete project by utilizing and adapting available processes and tools
Action-oriented Competencies
Action-oriented competencies focus on achieving results.
Axées sur la relation
Les compétences axées sur la relation sont sollicitées au cours de nos interactions sociales et dans notre rapport aux autres dans le cadre du travail.
Interdisciplinary and Intersectorial Thinking
Adopting collaborative reflexes by integrating a diverse range of people into the search for better problem-solving and shared decision-making .
Orientées vers l'action
Les compétences orientées vers l’action visent l’atteinte de résultats.
Self-oriented Competencies
Self-oriented competencies draw upon our personal resources.
Reinvented Learning
Engaging in continuous lifelong learning to adapt to the transformations brought about by AI
Social Intelligence
Responding to the needs of others in a manner appropriate to the situation by using verbal and non-verbal skills
Analytical Thinking
Understanding complex issues and concepts from multiple perspectives by recognizing logical connections between different pieces of information
Self-oriented Competencies
Self-oriented competencies draw upon our personal resources.
Reinvented Learning
Engaging in continuous lifelong learning to adapt to the transformations brought about by AI