By Keavy Murphy, VP of Security, Net Health
LinkedIn: Net Health
The adoption of AI within healthcare to improve and streamline workflows and inform clinical decision-making has grown exponentially within the last three years. While the potential is clear, questions around data privacy, compliance, and clinicians’ trust in the technology continue to slow progress. In care settings where regulations are strict and provider relationships are deeply personal, AI can’t be treated like just another digital add-on. It needs to be introduced carefully, with security and transparency leading the way.
For providers in post-acute and rehabilitation settings, the stakes are high. New technology must adapt to individualized workflows, support rigorous standards, and reinforce, not replace, the clinical judgment at the heart of patient progress. Unlike acute settings where high-tech interventions often take center stage, rehab therapy depends on consistency, nuance, and a human-centered approach. AI must rise to meet that reality, augmenting care without introducing friction or eroding trust.
Designing AI for Rehab Therapy
When implementing AI in rehabilitation settings, providers should begin with an understanding of the clinical and regulatory landscape it’s entering. The specialty requires hands-on, relationship-driven interactions and precise care coordination, often dealing with conditions that leave little room for disruptive or opaque technology. AI solutions introduced into this space must instead be engineered for alignment.
When it comes to implementing new solutions, security is the baseline. As rehab therapy providers increasingly manage sensitive patient data across complex systems, AI must be held to uncompromising standards of privacy and compliance. But security can’t be treated as a one-time check. It must be embedded into every layer of an AI solution, from how data is sourced and processed to how it’s stored and surfaced in clinical workflows. Without that foundation, adoption stalls before it begins.
Beyond security alone, context determines whether AI can function within a provider organization. AI designed for generalized hospital settings often fails to translate in rehab therapy environments, where progress is tracked over weeks or months, and where clinicians rely heavily on observation, nuance, and cumulative knowledge. Effective AI tools must reflect this reality, supporting therapists with relevant, explainable insights that respect, not replace, their expertise.
Trust and Transparency: The Security Foundation
Even the most sophisticated AI technologies won’t gain traction in post-acute and rehabilitation settings without clinician trust. These environments are shaped by professional judgment, hands-on expertise, and tailored care plans. Any technology that undermines that dynamic, or operates behind a black box, will be met with skepticism. Rather than serving as a barrier, trust should be viewed as a precondition for meaningful innovation. AI will be implemented securely only when that foundation is in place.
To build trust, AI systems must be transparent by design. Clinicians need to understand the “why” behind what a tool is recommending. That means surfacing explainable insights, offering the ability to review and adjust suggestions, and clearly identifying where AI-generated inputs begin and end. Without that level of visibility, providers risk introducing tools that feel more like surveillance than support.
Effective adoption also requires cross-functional collaboration with clinical leaders, IT teams, compliance officers, and frontline therapists. Structured onboarding, peer-to-peer training, and feedback loops help ensure that clinicians feel both empowered and informed by the technology. When clinicians are treated as co-owners of the process, adoption becomes more sustainable.
A Framework for Sustainable AI Integration
The implementation of AI is a strategic, continuous process that must be adapted to evolving clinical needs and operational realities. The most successful efforts start small, focus on specific, high-friction problems, and scale only after proving measurable value.
Piloting AI in narrow, well-defined areas, such as documentation support or scheduling, allows organizations to assess impact, refine workflows, and build buy-in without overwhelming staff or systems. These early use cases offer critical insight into what works, what needs adjustment, and how clinicians engage with the technology.
Success can’t be based solely on technical performance, but should reflect clinician experience, care quality, and compliance outcomes. Metrics like time saved, reduction in documentation errors, or improved schedule alignment offer a clear picture of value. Simultaneously, qualitative feedback from staff is just as important.
Sustainable integration also requires flexibility. As care models evolve, patient populations shift, and regulations change, AI systems must be able to adapt. That means investing in platforms and processes that are configurable, auditable, and aligned with broader quality and compliance goals, rather than being locked into rigid or outdated designs.
As AI becomes more embedded into healthcare operations, its true value in rehab therapy will be defined by how effectively organizations incorporate it within clinical and operational ecosystems. Rather than viewing AI as a one-size-fits-all solution, organizations must recognize it as a strategic capability that evolves alongside clinical practices and practical priorities. When positioned this way, AI becomes less of a disruptive force and more of an asset that changes alongside the needs of patients, providers, and care models.
Over time, the impact of AI will be determined less by technical capabilities and more by how effectively it supports care delivery at scale. In rehab therapy, success depends on whether AI reduces operational friction, strengthens clinical workflows, and maximizes the security of patient data, without adding complexity or compromising professional judgment.