AI & Rehab Therapy—It Isn't Scary, It's Just Math

Artificial intelligence (AI) is rapidly becoming an integral part of various industries, including healthcare, revolutionizing patient care and clinical decision-making. In physical therapy, AI-driven interventions make it easier for clinicians to drive behavior change and achieve meaningful objectives. But how does AI work in a clinical setting like physical therapy? And what benefits can it offer to both clinicians and patients?

AI in Rehab Therapy: It’s Just Math

At its core, AI is about pattern recognition. Specifically, machine learning, a subset of AI, helps computers learn from examples, improving accuracy and predictions over time. In clinical settings, AI can analyze vast amounts of data—such as patient records, clinical notes, and treatment outcomes—and identify patterns that might not be immediately obvious to human clinicians.

For example, PredictionHealth specializes in AI-driven healthcare solutions for rehab therapy compliance, documentation, and CPT coding. Our AI platform has evaluated millions of physical therapy notes, including unstructured text, to set practice benchmarks. These AI-driven benchmarks help practices succeed in coding, compliance, and documentation support.

The exciting part? AI doesn’t require clinicians to program it with rules explicitly. Instead, by feeding AI models more data, they become smarter, learning from each interaction. This allows physical and occupational therapists to use AI to guide decisions in real-time, whether in documentation, treatment plans, or compliance with healthcare regulations.

How AI Works in Physical Therapy

In physical therapy, administrative AI interventions can be categorized into a few core areas:

1. Burnout Reduction: AI helps automate repetitive tasks, such as documentation, coding, and compliance checks. This reduces the administrative burden on clinicians, giving them more time to focus on patient care and reducing burnout.

2. Compliance: AI systems can ensure that therapy notes are accurate and in line with payer requirements, improving reimbursement rates and reducing errors that might lead to audits or denials.

3. Coding: Selecting the right CPT code to reflect the type of care provided to the patient accurately can be time-consuming and overwhelming. Over-code something, and you could face a denial or even insurance takebacks. Under-code, and you’re leaving reimbursement on the table. AI can help take the guesswork (and angst) out of this process by checking a therapist's CPT code selections at the point of care and helping them understand if there is a better selection to be made and why. 

4. Productivity: By streamlining workflows, AI tools can help clinicians focus on delivering quality care. For example, AI systems integrated with electronic medical records (EMR) can predict necessary documentation steps, enabling PTs to move through their tasks more efficiently.

How AI Recognizes Patterns

A fundamental component of AI in healthcare is its ability to recognize patterns in text. Consider this analogy: if you describe a "cute, fuzzy creature with a white nose and wagging tail," a person would easily recognize that as a dog. Similarly, AI learns to predict what words or phrases mean by analyzing patterns from millions of examples.

For physical therapists, AI can analyze complex medical notes filled with jargon, acronyms, and unique terminology. AI systems convert these words into "tokens" (essentially numbers), allowing the machine to "read" and "understand" clinical language. The more data it processes, the better it becomes at predicting the meaning of a clinical note or suggesting the next step in treatment.

A Simple Example: Pattern Matching in Action

Imagine a simple scenario to understand how machine learning works. You provide descriptive text as input, and the model predicts the corresponding label:

• Input: "Cute, fuzzy creature with a wet nose and a wagging tail"
  Prediction: Dog
• Input: "An animal bigger than a bread box with black-and-white spots that says moo"
  Prediction: Cow
• Input: "Cherry red with four wheels, a steering wheel, zipping around windy roads"
  Prediction: Sports car

If a model is trained with many such descriptions, it can predict the category of a new, unseen description based on patterns it has learned. It’s similar to how a human might infer meaning from prior knowledge.

The Challenge of Messy Language

Language is incredibly nuanced, filled with synonyms, acronyms, and phrases that can mean different things in different contexts. This complexity makes it challenging for a machine to understand language without structure. To process language, machine learning models first turn words into numbers—a process called tokenization.

For example, “low back pain” might be broken into tokens, assigning each token a numeric representation. To us, these numbers seem meaningless, but for the model, they provide a framework for recognizing and predicting patterns.

Even emojis, like a smiley face, are tokenized. The machine doesn’t see them as just punctuation marks but learns their meaning as distinct entities. This ability to assign meaning to tokens enables models to process text with remarkable precision.

From Tokens to Predictions

Once words are tokenized, models like ChatGPT and other generative AI systems use patterns from massive datasets to predict the next token in a sequence. For example, when prompted with “The patient is a 54-year-old male for evaluation of...,” the model predicts the next words based on prior patterns in similar contexts.

By training these models on vast amounts of data, they don’t just recognize patterns—they can also exhibit reasoning that feels advanced, even human-like.

The Power and Pitfalls of Off-the-Shelf AI

Large language models (LLMs) like ChatGPT are accessible and powerful tools. They come ready with impressive general knowledge and can be customized for specific tasks. However, they also come with limitations:

1. Workflow Integration: Off-the-shelf models aren’t designed to integrate seamlessly into specialized workflows, such as those of physical therapists.
2. Compliance: General-purpose models are not HIPAA-compliant, posing risks when handling protected health information (PHI).
3. Specialization: These models lack the customizations needed for niche fields like rehab therapy, where specific tasks and terminology are critical.

Large Language Models: The Power Behind AI

Large language models (LLMs), such as ChatGPT, are AI-trained to understand and generate human-like text. These models are trained on vast amounts of data and can predict the next word in a sentence based on previous context. In healthcare, this ability enables AI to support clinical documentation by generating relevant text, improving coding accuracy, and even assisting in decision-making during patient consultations.

However, using off-the-shelf LLMs like ChatGPT in clinical settings comes with risks. Many are not HIPAA-compliant, meaning they don’t safeguard sensitive patient information. Additionally, these general models aren’t fine-tuned for the specific needs of physical therapists. In contrast, AI models specifically designed for healthcare, like those used by Prediction Health, are tailored to address the unique challenges clinicians face, such as optimizing workflows and improving patient outcomes.

AI's Role in Clinical Transformation

Incorporating AI into physical therapy practices allows clinicians to move beyond manual data analysis and subjective decision-making. AI provides objective insights, enabling better, data-driven decisions directly impacting patient care. From coding and compliance to reducing burnout and enhancing productivity, AI-driven interventions create a more efficient clinical environment.

AI helps clinicians focus on outcomes by identifying patterns and supporting better decision-making. It allows practices to monitor performance improvement strategies continuously and implement AI-driven solutions to track progress toward meaningful clinical objectives.

The Future of AI in Physical Therapy

AI is not just a futuristic technology—it's already reshaping the landscape of physical therapy. As more practices adopt AI tools, they will see improvements in compliance, productivity, and overall patient outcomes. By using AI to streamline workflows, reduce burnout, and provide data-driven insights, physical therapists can focus on what truly matters: delivering excellent patient care.

In the years ahead, AI will continue to evolve, becoming an even more integral part of physical therapy practices. As the technology improves, it will be increasingly fine-tuned for clinical tasks, ensuring that it helps clinicians make the best decisions for their patients while navigating the complexities of healthcare compliance and coding.

In the end, AI is just math—but in physical therapy, it's math that makes a real difference.