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Enhancing Occupational Therapy Through AI: A Deep Dive into the PEOP Model

  • Name
    Bella Martini

peop model and ai

Enhancing Occupational Therapy Through AI: A Deep Dive into the PEOP Model


Occupational therapy is advancing rapidly with technological progress, and one area showing significant promise is the integration of artificial intelligence (AI). Among the various models employed in occupational therapy, the Person-Environment-Occupation-Performance (PEOP) model stands out for its holistic approach. In this article, we'll explore how AI can complement the PEOP model in occupational therapy, offering new opportunities to enhance engagement and participation for individuals across diverse populations.

Understanding the PEOP Model:

The PEOP model, developed by Christiansen and Baum in 1997, provides a structured framework for understanding how personal factors, environmental influences, and occupational activities interact to shape an individual's performance and participation in daily life. Let's briefly examine each component of the PEOP model:


The person refers to the individual seeking occupational therapy services. This component encompasses the person's unique characteristics, including physical, cognitive, emotional, and social attributes. AI technologies such as machine learning algorithms can analyse large datasets to identify patterns and personalise interventions based on individual needs and preferences.


The environment includes the physical, social, cultural, and institutional contexts in which occupational performance occurs. AI-powered sensors and monitoring systems can assess environmental factors in real-time, providing insights into accessibility, safety, and ergonomic considerations. Additionally, virtual reality (VR) simulations can create immersive environments to help individuals practise and adapt to different contexts.


Occupations refer to the various activities and tasks that individuals engage in daily. AI-driven activity recognition systems can track and analyse occupational performance, offering objective feedback and identifying areas for improvement. Virtual assistants and chatbots can also provide guidance and support for carrying out specific tasks or accessing resources related to occupations.


Performance involves the actual execution of occupations within specific contexts. AI technologies such as natural language processing (NLP) can analyse qualitative data from client interactions to assess performance and measure outcomes. Wearable devices equipped with sensors can monitor physiological responses and movement patterns, providing valuable insights into performance metrics.

Integration of AI in Occupational Therapy:

The integration of AI in occupational therapy holds immense potential to enhance assessment, intervention, collaboration, and advocacy efforts based on the principles of the PEOP model:

AI-driven Assessment:

AI-powered assessment tools can streamline the evaluation process by analysing large volumes of data quickly and accurately. For example, AI algorithms can analyse electronic health records, sensor data, and patient-reported outcomes to identify trends and risk factors. This data-driven approach can inform decision-making and facilitate personalised treatment planning.

Adaptive Interventions:

AI algorithms can adapt interventions dynamically based on real-time feedback and individual progress. For instance, virtual reality-based interventions can adjust task difficulty levels or environmental settings to challenge individuals appropriately. Personalised recommendations generated by AI systems can also support self-management strategies and promote adherence to therapy plans.

Collaborative Platforms:

AI-powered platforms can facilitate collaboration among multidisciplinary teams, including occupational therapists, healthcare professionals, educators, caregivers, and community resources. These platforms can streamline communication, data sharing, and decision-making processes, ensuring that interventions are coordinated and holistic. AI-driven chatbots and virtual assistants can also provide support and information to clients and caregivers remotely.

Advocacy and Policy Development:

AI technologies can analyse large-scale population data to identify disparities and inform advocacy efforts aimed at promoting social justice and equity. By highlighting systemic barriers to participation, AI-driven research can advocate for policy changes and resource allocation to address the needs of marginalised communities. Additionally, AI-powered predictive modelling can forecast future trends and inform proactive interventions to improve population health outcomes.

Case Example:

Consider a case involving an elderly individual with mobility issues living in a rural area. Using the PEOP model integrated with AI technologies, an occupational therapist conducts a comprehensive assessment of the person's functional abilities, home environment, and social support network. AI-powered sensors installed in the home monitor the individual's movements and activities, detecting potential hazards and providing real-time feedback to the therapist. Based on this data, the therapist collaborates with the client and their family to implement interventions, including home modifications, assistive devices, and tele-rehabilitation sessions. Over time, the individual experiences improved independence and quality of life, demonstrating the efficacy of AI-enhanced occupational therapy.


The integration of AI in occupational therapy offers exciting opportunities to enhance the application of the PEOP model in promoting engagement and participation in daily life. By leveraging AI technologies for assessment, intervention, collaboration, and advocacy efforts, occupational therapists can deliver more personalised, efficient, and impactful services to individuals across diverse populations. As the field continues to evolve, embracing AI as a complementary tool in occupational therapy practice can lead to transformative outcomes for clients, caregivers, and communities alike.