Leveraging Artificial Intelligence and Technological Advancements to Enhance Efficiency, Quality, and Patient Outcomes in Occupational Therapy

Evelyn Terrell, OTD, MS/HSA, OTR/L, FACHE, FATA

Dr. Jose R. Rafols, EdD, OTD, MHSA, OTR/L, BCTS

The role of Artificial Intelligence (AI) in healthcare, particularly in occupational therapy (OT), is rapidly expanding. AI enables computer systems to perform tasks traditionally requiring human intelligence, such as recognizing patterns, making predictions and forecasting outcomes. Machine learning, a subset of AI, allows computers to learn without being explicitly programmed (Artificial Intelligence (AI) at HHS, 2023). 

AI applications have the potential to enhance the accuracy, efficiency, and personalization of OT practices. Though publications on AI in rehabilitation services are limited, numerous clinicians report the positive impacts AI can have across various aspects of OT practice, including assessment, treatment planning, and progress tracking.

Key AI Applications in OT:

• Assessment, Diagnosis, and Intervention: AI-driven tools can improve assessment accuracy and personalize treatment planning by analyzing patient data, which in turn helps optimize interventions (Medenica et al., 2023).
• Rehabilitation and Therapy Tools: AI powers innovations such as robotics, virtual reality (VR), and augmented reality (AR), which enhance therapeutic interventions.
• Patient Monitoring and Progress Tracking: Wearables and predictive analytics enable real-time tracking of patient progress, improving both short-term interventions and long-term outcomes.
• Machine Learning and Precision Medicine: AI tools leverage predictive analytics to determine the most effective treatment protocols based on individual patient (Davenport & Kalakota, 2019). Technologies bring great potential to predict the risk of complex diseases (Mukherjee et al., 2024).

Benefits of AI in OT:

• Improved Accuracy and Efficiency: AI improves the precision of assessments and reduces errors in patient care.
• Personalized Treatment Plans: AI enhances the customization of treatment, ensuring that interventions are tailored to each patient's unique needs.
• Enhanced Decision Making: By providing real-time data and insights, AI assists clinicians in making informed, data-driven decisions.
• Equitable Service Delivery: AI can help reduce disparities in service delivery by ensuring that treatment recommendations are based on comprehensive data, including underrepresented populations.
• Better Patient Outcomes: Continuous monitoring and real-time data tracking improve long-term patient care and recovery.
• Time Savings and Quality Improvement: AI automates administrative tasks, allowing therapists to focus more on patient care, leading to better service delivery.
• Lengthier periods of patient engagement: Through the use of AI, patients are afforded the opportunity to engage AI minders that offer information, answer simple questions, and allow the customer to feel connected during non-office or work hours.
• Challenges and Considerations: Despite the significant potential of AI in OT, several challenges must be addressed:
• Ethical Concerns: The use of AI raises ethical questions regarding transparency, consent, and accountability in decision-making.
• Patient Data Privacy and Security: Ensuring compliance with privacy standards, such as HIPAA, is crucial when using AI to handle sensitive patient data.
• Bias in AI Systems: AI systems can perpetuate biases, especially if trained on non-representative data, which may exclude certain demographic groups and communities (Addressing Bias in Artificial Intelligence, 2024).
• Informed Consent and Transparency: Patients must be fully informed about how AI is being used in their treatment, and their consent must be obtained.
• Analyzing Administrative and Treatment Data: AI must be designed to handle and analyze large datasets accurately, ensuring the quality and applicability of recommendations.

Best Practices for Implementing AI in OT: To maximize the benefits of AI while addressing its challenges, it is essential to:

• Promote Collaboration: Foster collaboration among clinicians, AI experts, and data scientists to ensure that AI tools are relevant and effective in clinical settings.
• Provide Training and Education: Continuous education and training for OT professionals on AI technologies are necessary to ensure they can integrate these tools into their practices effectively.

Ethical Integration of AI in OT: The American Telemedicine Association (ATA) published guidelines in 2023 on two critical aspects of AI in healthcare: AI principles and health data privacy principles. These publications emphasize securing patient data, promoting transparency, and ensuring the responsible and ethical use of AI in telehealth settings (Artificial intelligence (AI) principles: Utilizing AI in healthcare, 2023; Health data privacy principles, 2023). The ATA’s principles offer crucial insights into how AI can be used effectively while maintaining patient trust and ensuring privacy.

In line with the U.S. Department of Health and Human Services (HHS) AI Strategy, which prioritizes the responsible adoption and scaling of AI solutions, practitioners can benefit from increased funding and support to integrate AI into their practices (Artificial Intelligence (AI) at HHS, 2023). HHS is enhancing partnerships with external stakeholders, including academia and the private sector, to foster innovation and research in AI applications. 

AI in Addressing Workforce Shortages: One of the significant benefits of AI in healthcare is its potential to address workforce shortages. AI can help optimize clinical workflows, improve data usage, and support education and training in OT (US state-by-state AI legislation snapshot, 2023). Additionally, administrative functions, such as revenue cycle management and billing, can be streamlined with AI, improving efficiency and reducing clinician burnout.

Building Trust and Overcoming Bias: Despite AI's potential, its integration into healthcare faces resistance due to concerns about trust and bias. AI systems are often criticized for being trained on non-representative data, which can lead to decisions that do not fully consider diverse patient populations (Addressing Bias in Artificial Intelligence, 2024). To overcome these challenges, it is essential to ensure that AI algorithms are developed using comprehensive, diverse datasets that represent all patient populations.

Staying Informed of Regulatory Landscape: Organizations must stay informed in a rapidly-changing regulatory landscape, including proposed bills and enacted AI legislation in their state to ensure compliance and minimize risk (US state-by-state AI legislation snapshot, 2023).

Use Cases from OTs: As AI becomes more prevalent in OT, clinicians are reporting successful implementations of AI-driven tools. Below are recent interviews with clinicians practicing in California, Florida and North Dakota.

• Nicholas “Nick” Schulz, OTR/L, Owner, Adaptive Life Therapy, Fargo, North Dakota.  
• Amanda Speights, DHSc, OTR/L, Associate Professor of Clinical Occupational Therapy, University of Southern California.
• Alexa Martinez, Lead Clinical Sports Physical Therapist, Nicklaus Children’s Hospital, Miami, Florida.

Nick Schulz shared how his practice, Adaptive Life Therapy, is leveraging AI-driven tools to enhance clinical efficiency and patient engagement. Adaptive Life Therapy is pioneering the use of AI to improve therapy operations, including clinician productivity and patient outcomes. A key component of their approach is integrating ChatGPT, which has yielded transformative results across multiple areas of practice.

Dr. Amanda Speights discussed the use of AI-powered tools in the academic setting for strategic enrollment management and to enhance therapy interventions.  

Nicklaus Children’s Hospital’s rehabilitation services department is leveraging AI to enhance compliance with home programs and improve monitoring of patients’ progress during at-home rehabilitation. By collecting data on patient activities in the home environment, the clinicians will be able to adjust treatment plans more effectively and provide better-targeted interventions.

The following use cases and results were highlighted during interviews: 

• Personalized Therapy Plans and Tailored Interventions: AI analyzes patient data to create customized treatment plans. Adaptive Life Therapy uses AI to analyze patient data and develop customized treatment plans. For example, Nick Schulz shared how AI was instrumental in creating a therapy plan for a patient with autism, aligning interventions with the patient’s unique interests and challenges. By incorporating adaptive learning techniques, AI enabled the creation of personalized therapy materials, such as visual aids that simplified complex concepts into manageable components. Notably, when implementing the SOS feeding approach, AI assisted in crafting individualized plans that utilized contrasting food types to promote learning. Dr. Amanda Speights uses Gemini for idea generation when planning therapy sessions, to develop creative and engaging activities tailored to specific skill development.  
• Enhanced Patient Engagement: AI-generated interactive social stories and tailored materials have significantly improved patient engagement at Adaptive Life Therapy. Nick Schulz described creating an AI-generated social story for a child with autism to help them understand social cues and improve classroom behavior. AI also supports real-time cognitive behavioral strategies by delivering personalized relaxation exercises and breathing techniques directly to patients’ smartphones. For instance, a college student suffering from anxiety benefited from a custom AI-designed relaxation routine, which incorporated their preferred techniques and calming sounds.
• Streamlined Administrative Tasks: AI has been a game-changer in reducing administrative burdens, allowing clinicians to focus more on patient care. Nick Schulz uses AI to draft detailed and comprehensive medical necessity letters for patients with complex conditions, ensuring that all relevant information is included to prevent therapy denials. Additionally, AI has streamlined payroll processing, revenue cycle management, and authorization and billing tasks, significantly reducing the time spent on these processes. Dr. Amanda Speights is leading AI work at the University of Southern California to improve the OT admissions process. Using Chatbots, they hope to streamline the process of responding to questions from potential candidates and reduce the administrative burden from the admissions team. Once implemented, they plan to analyze data on predicting behavior to create more targeted messages and increase engagement from applicants. 
• Innovative Therapy Tools and Interactive Therapy Materials:
  AI has empowered Adaptive Life Therapy to develop innovative tools that enhance learning and comprehension. For example, Nick Schulz used AI to create a visual aid featuring a video with voiceover and graphics to teach a patient about organization and time management. This resource also supports executive functioning skills training, making therapy more engaging and effective. Nicklaus Children’s is implementing an AI-powered platform to improve monitoring of patients’ progress during at-home rehabilitation. By collecting data on patient activities in the home environment, the clinicians will be able to adjust treatment plans more effectively and provide better-targeted interventions.
• Lessons Learned and Future Potential: Nick Schulz shared lessons learned from AI implementation. He emphasized the importance of optimizing AI tools, such as OT-specific plugins and image generators, to tailor materials for individual needs. By combining AI-generated resources with human creativity and clinical expertise, Adaptive Life Therapy has elevated the quality of care they provide.

By embracing AI, clinicians like Nick Schulz, Amanda Speights, and Alexa Martinez are transforming their field, improving efficiency, and enriching the lives of countless individuals. 

Conclusion: Artificial intelligence is transforming OT by improving efficiency, personalizing treatment plans, and enhancing patient outcomes. However, ethical considerations and biases must be carefully addressed to ensure the responsible integration of AI into clinical practice. Through continued collaboration, education, and adherence to best practices, AI can help OT evolve and meet the demands of modern healthcare.

References

Addressing Bias in Artificial Intelligence. (2024).  Center for Connected Health Policy. Retrieved November 14 from https://www.cchpca.org/resources/addressing-bias-in-artificial-intelligence/

Artificial Intelligence (AI) at HHS. (2023).  US Department of Health and Human Services. Retrieved November 14 from https://www.hhs.gov/programs/topic-sites/ai/index.html

Artificial intelligence (AI) principles: Utilizing AI in healthcare. (2023).  American Telemedicine Association. Retrieved November 14 from https://www.americantelemed.org

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal,6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94

Health data privacy principles. (2023).  America Telemedicine Association. Retrieved November 14 from https://www.americantelemed.org

Medenica, V., Ivanović, L., Ristić, I., & Čolić, G. (2023). Artificial intelligence in occupational therapy and special education and rehabilitation. Scientific Conference SANUS 2023, Bosnia and Herzegovina.

Mukherjee, J., Sharma, R., Dutta, P., & Bhunia, B. (2024). Artificial intelligence in healthcare: a mastery. Biotechnology and Genetic Engineering Reviews, 40(3), 1659-1708. 

US state-by-state AI legislation snapshot. (2023).  Bryan Cave Leighton Paisner. Client Intelligent. Retrieved November 11 from https://www.bclplaw.com/en-US/events-insights-news/us-state-by-state-artificial-intelligence-legislation-snapshot.html