The rapid emergence of new technologies has created key opportunities for innovation in health care. Artificial intelligence has the potential to transform many aspects of the health care landscape, including clinical documentation in Complex Rehabilitation Technology.  AI-based tools may incorporate natural language processing, speech recognition and machine-learning capabilities, and they are increasingly being integrated into documentation systems, clinical decision tools and user-facing chatbots.1

As AI expands across health care settings, it is essential to recognize the tension between its potential and the challenges that may arise. Although this dynamic relationship accompanies the adoption of most new technologies, one cornerstone of clinical practice must remain unchanged: the importance of meaningful, client-centered care. This article explores the potential benefits and challenges of integrating AI into clinical documentation and provides insights from Clinician Task Force member Elaine Lu, PT, MHEd, ATP, who is actively shaping how AI is utilized in CRT documentation within her clinical practice.

Streamlining clinical documentation  

Recent reviews of scholarly literature indicate that AI-based systems can improve the accuracy and efficiency of health care documentation.1 Streamlining documentation and reducing administrative burden may allow clinicians to devote more time to direct patient care, with a likely secondary benefit of reducing clinician burnout.1

In “A Giant Leap: How AI is Transforming Healthcare and What That Means for Our Future,” Dr. Robert Wachter indicates electronic documentation is a key contributor to burnout among health care professionals.2  Navigating numerous electronic screens, completing additional digital tasks and managing constant software notifications can quickly become overwhelming.2  CRT professionals understand that no single seating or wheeled mobility system meets the needs and goals of every client, environment and functional task. This same mindset should apply to AI-enabled systems in clinical practice. While AI does not need to be a perfect solution to make a measurable impact on quality, safety and patient experience, the tool should demonstrate clear and practical improvements in clinical practice.2

Potential challenges of AI integration

While these benefits are promising, integrating AI into clinical documentation is not without challenges. Managing documentation errors, navigating legal liability and integrating AI tools within existing electronic health record systems are among the most cited barriers.1 Ethical concerns regarding the use of AI with patient data have also been widely noted.1   

Although these tools may improve accuracy and efficiency, their use can raise concerns related to core principles of patient‑centered care.4.  Users’ reluctance to adopt new technologies, lack of trust, risk of system abandonment and misalignment of established workflows warrant careful consideration.3,5,6   Therefore, further study is needed to evaluate the long-term impact of AI-based technologies across and within health care settings.6

A clinician’s role in shaping AI in CRT documentation

CTF member Elaine Lu offers valuable insights into the application of AI capabilities within clinical documentation software. In recent months, Lu collaborated with the team at Jane Software Inc. to expand CRT-specific content within their platform. Their software offers customizable templates, quick prompts for key phrases and AI Scribe features that uses speech recognition technology to support clinical documentation.7

The AI Scribe feature allows clinicians to record clinical sessions or upload dictated audio files, while also allowing them to manually enter narratives, measurements and recommendations. Using information captured during the clinical encounter, the software generates clinical notes and reports in a fraction of the time required for manual entry. The platform also allows clinicians and health care organizations to manage audio data by choosing whether to delete or securely store recordings.7

As a physical therapist with specialized CRT expertise, Lu advocated for the development of CRT-focused content to better support documentation needs specific to this specialized practice area. By collaborating with software programmers, she helped develop CRT-specific phrases, prompts and templates to enhance the platform’s speech-recognition and natural language processing capabilities. She also structured outlines with narrative fields to allow clinicians to capture outcome measures, qualitative findings and detail clinical recommendations. Since using this AI-based platform in her practice, Lu has experienced greater efficiency with generating clinical reports and equipment justifications, while continuing to thoroughly review and edit all documentation before finalization.

Lu also shared practical strategies for navigating logistical challenges associated with AI-based scribing. Minimizing extraneous background noise within the clinic can be helpful to improve transcription accuracy. Additionally, intentionally introducing and repeating the names of all individuals present during the clinical visits helps the software distinguish between the clinician’s voice and those of the client, care partners, supplier and other professionals present during evaluations, equipment trials and follow-up sessions.  

Through this experience, Lu identified broader lessons relevant to the CRT community. CRT stakeholders communicate through discipline-specific terminology.  However, they may be less familiar with AI technologies and software development. Collaborating with professionals outside the CRT field often requires additional time and effort to clearly communicate clinical goals and translate them into functional software solutions.  

According to Lu, many CRT colleagues and suppliers have expressed curiosity and enthusiasm about the integration of AI into clinical documentation software.  At the same time, others have been hesitant about these tools due to ethical concerns, limited time and bandwidth for training, or uncertainty as to how system will impact their established workflows. The adoption of technology in health care is multifaceted, impacting culture and requiring alignment among staff, processes and organizational values.6

Looking ahead

CRT stakeholders are navigating a period of change as AI-based technologies continue to expand across health care settings. The potential for streamlined processes and increased efficiencies is enticing. However, it is equally important to acknowledge and address the unintended challenges associated with implementing AI in clinical documentation. Given the essential nature of their work, CRT professionals routinely adapt to changes related to technology, legislation, research and payer systems. Through ongoing education, research and advocacy, the CRT community will continue to respond thoughtfully to these evolving systems. By approaching AI adoption with intention, collaboration and critical reflection, CRT professionals can leverage innovative systems while ensuring that patient-centered care remains the foundation of practice.

Acknowledgement

The author gratefully acknowledges Elaine Lu, PT, MHEd, ATP, for her valuable contributions to this article and for sharing her clinical insights and experiences related to integrating AI into CRT documentation.

Leslie may be reached at otrjackson@yahoo.com.

Leslie Jackson, OTD, OTR/L, ATP, CEASIII, LSVT BIG Certified, has served as an occupational therapist for over 25 years in various settings, including outpatient, acute care, home health, acute rehab and a doctoral-level academic program. She currently leads the outpatient Seating and Mobility Clinic for Marion Health and serves as an occupational therapist for the Department of Veterans Affairs. Jackson earned the Assistive Technology Professional certification from RESNA in 2008 and is certified in ergonomics and LSVT BIG, a treatment protocol for individuals living with Parkinson’s disease. She volunteers as an executive board member for Services for the Visually and Hearing Impaired, a nonprofit organization providing assistive technology and education to its clients. Jackson is honored to contribute through the Clinician Task Force’s advocacy and educational initiatives.

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References

  1. Lee, C., Britto, S., & Diwan, K. (2024). Evaluating the impact of artificial intelligence (AI) on clinical documentation efficiency and accuracy across clinical settings: A Scoping Review. Cureus, 16(11), e73994. https://doi.org/10.7759/cureus.73994
  2. Wachter, R. (2020). A Giant Leap: How AI is Transforming Healthcare and What That Means for Patients and Providers. New York: Oxford University Press.
  3. Wenderott, K., Krups, J., & Weigl, M. (2026). Comprehensive recommendations for the implementation of artificial intelligence in healthcare: a narrative review on facilitators and barriers. British Medical Journal Open Quality, 15, e003639. https://doi.org/10.1136/bmjoq-2025-003639
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  5. Sarraf, B. & Ghasempour, A. (2025). Impact of artificial intelligence on electronic health record-related burnouts among healthcare professionals: systematic review. Frontiers in Public Health, 13, 1628831. https://doi.org/10.3389/fpubh.2025.1628831
  6. Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A’Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S.  (2017). Beyond Adoption: a new framework for theorizing and evaluating nonadoption, abandonment and challenges to the scale-up, spread and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), e367. https://www.jmir.org/2017/11/e367. https://doi: 10.2196/jmir.8775
  7. Jane Software. (2026).  Charting, forms, surveys, & AI Scribe.  Retrieved March 3, 2026, from https://jane.app/features/charts-forms-documentation