AI Scribes Compared: for OT, PT, and SLP

It is projected that $11.1 billion in venture capital will be invested in AI healthcare companies in 2024. 

For OT, PT and SLPs this means many shiny new tools (like AI scribes for rehab documentation) are coming to market, with large sales budgets directed at you.

To be honest, in allied health professions, we are not accustomed to this level of investment—and we’re not used to fielding these types of sales pitches. 

That’s why I want to start equipping you to be a savvy consumer of these products with side-by-side comparison information about the different AI scribe companies. As you scroll down this post, you’ll also find questions you can ask of these companies, and a course we created on the topic.

With the right collaboration from therapists, I believe these tools will transform our work for the better.

AI Scribes for Rehab Comparison Table

Here’s the AI scribe companies I found with rehab-specific solutions. (Please let me know in the comments if I am missing any.) These companies are largely geared to adult outpatient therapies—but watch for more solutions to come to market for hospital, home health, and pediatric therapists.

I will also be reaching out to each company to verify information, and plan to keep this post up-to-date as new information becomes available.

AI Scribe Company	Price/User /Month	EHR Agnostic	EHR Integrations	CPT Coding Suggestions	Analytics Captures	Multi-langauge	Investment	Valuation
Athelas Scribe	$150	X		X		X	$132mm	$6 billion
Comprehend	$99		WebPT, Prompt, Empower, PTeverywhere, Jane, Athena, Clinicient, Practice Pro, Practice Perfect, Heno, Core, TheraOffice, Raintree,  And a Universal integration that can be used within any web-based EMR	X		X	$15K
HippoScribe	$99	X
Prediction Health	$105		Athenahealth, Clinicient, Empower EMR, Prompt, WebPT	X	Practice Intel	X	$3.98 mm
ScribePT	$75	X

Podcast Course on AI Scribes and Documentation

One of the best ways to wrap your minds around these new technologies is to hear from a clinician who has been immersed in this space. That’s why I recorded this podcast course with Dennis Morrison, PhD.

In the episode we look at some of the latest research about ambient listening AI documentation tools (and identify the most important questions therapists need to ask about any new tools that are presented to them). 

This course was recorded with occupational therapists in mind, but I hope other allied health professionals (including physical therapy, speech therapy, and behavioral health providers) find this information helpful—and that they are asking similar questions! 

Questions to Ask During Your Demo of an AI Scribe

Okay, based on my above conversation with Dennis, here are questions I now believe it is important to ask when examining new AI scribes.

1. How are Clinicians Involved the Development of the Scribe?

Before you adopt any new tech tool, ask the vendor how clinicians were involved in its development. 

Ideally, they are equity owners and on the leadership team. 

If clinicians are only on the sales team or an advisory board—or if the solution was vaguely “developed alongside clinicians”—run for the hills. 🏃⛰️ Our care is so complex and healthcare is evolving so rapidly, companies to need to be triangulating information from tech, business, and clinical practice. If they are not, I’m afraid it is not a sustainable company.

2. How Was the AI’s Large Language Model (LLM) Developed—And How is it Maintained?

AI documentation tools leverage large language models (LLMs) to generate content, and you need to know how the AI’s large language model was built. 

To build on point one, clinicians should be deeply involved in the creation and upkeep of clinical models. These staff clinicians should be helping to ensure that models align with best practices.

🛑 If the large language model is SOLELY a general-purpose model like ChatGPT, this should be a red flag for you. 84% of ChatGPT’s LLM was scraped from the internet, and as a result, it carries all the bias found on the internet. 

✅  Ideally, the tool’s LLM is a purpose-built model specifically trained on clinical data that is relevant to your practice. This means the output it generates is more likely to be accurate and useful in a clinical setting. These companies may still leverage ChatGPT technology in the development process, but it should not be the only source of training information. Furthermore, the data you input into the tool should never be exposed to the public-facing version of ChatGPT.

Learn more about why they believe healthcare AI needs to be built specifically for your discipline here.

3. What Privacy and Compliance Measures Does the Tool Have in Place?

Privacy is one of the main concerns when using these tools. In the United States, it should be a given that any healthcare AI tool is HIPAA compliant. 

Beyond that, the degree to which patient information is stored can vary widely. That’s why it’s important to ask:

• How patient data is stored?
• Who has access to it ?
• How long it is retained?
• Can you request it gets deleted?
• What components of the data the customer owns (versus the components the AI tool owns)?
• Who authorizes third parties to access the data?
• How does the tool anonymizes data?
• What measures are in place to protect patient information from breaches (and, who is liable in the event of a breach)?

4.  What support does the company give on educating clients and obtaining consent?

Each workplace and state has different regulations about obtaining client consent.

Ultimately you as the clinician are accountable for this knowledge.

But, you should certainly ask the company about what—if any—support the company provides to help educate clients about the tool and obtain their consent. 

5. How Well Does the Tool Integrate with My Current EHR System?

This is one of the most complex questions and where you need to ask the most questions. In the simplest terms we are going to put the interface with EHRs into two broad buckets: 1.) EHR agnostic 2.) EHR integrated.

EHR agnostic tools overlay your EHR typically as a chrome extension. The benefit of this that it is relatively easy to get going, the downside is the lack of a feedback loop with your EHR. At the very least these companies should offer a one-click solution to insert the basics of your note (like the SOAP section) into your EHR.

🛑 If you have to copy and paste documentation into your EHR system, walk away. This means extra work for you, and a copy-paste workflow is prone to errors. 

EHR integrated tools have the upside of a feedback loop with your EHR, but what “integration” means varies widely. This is not a standardized term, so really dig into this during your demo. Ask questions like:

• Is there data sharing with the EMR?
• What’s the feedback loop with the team EMR/scribe?
• Is their a team that would support troubleshooting issues?
• Will there be any extra work for my IT team?

As you’ll note in the comparison table above, the tools really vary in how they approach interfacing with the EHR, and as a clinician you’ll have to decide what is right for you.

6. Can the AI Tool be Customized to Fit My Documentation Style?

Every therapist has a unique documentation style and voice. Sophisticated AI products should accurately capture not only the content of what a therapist writes, but also the writer’s voice. Inquire whether the AI tool can be tailored to fit your specific needs—for example, by including verbatim patient goals or generating summaries in particular formats like DAP or SOAP notes. 

That being said, while some degree of customization is nice, the scribe should also help you standardize best practices around things like compliance, so ask about the give-take between customization and standardization.

7. What are the Potential Efficiency Gains?

One of the main promises of AI documentation tools is increased efficiency and accuracy—especially compared to cutting and pasting note content. A good AI tool should generate note suggestions that are unique to this—and only this—session.

Ask for specific data on how the tool has improved documentation time in real-world therapy settings—and whether there are any studies or pilot programs that demonstrate its effectiveness. For example, I like how this behavioral health tool is actively researching its effectiveness. 

8. How Does the Tool Impact Patient Engagement?

Look for tools that allow you to remain fully present with your clients during sessions, minimizing task-switching and administrative distractions. Ask about any feedback mechanisms or studies indicating improved patient satisfaction, engagement, and outcomes.

In this research, 100% of clinicians reported being able to give the client their full attention when using the AI tool—versus 66% when using the EHR alone.

This improved client-provider interaction may even lead to better client retention. I was pretty blown away 🤯 that this study showed that when an AI documentation tool was used patients attended, on average, 67% more sessions compared to those whose therapy sessions were conducted without the tool. 

9. What Training and Support are Provided?

Ensure that the AI vendor offers comprehensive training and ongoing support. This includes initial training sessions, user manuals, and accessible customer service to swiftly address any issues or questions that arise.

10. Are There Additional Features to Enhance Patient Outcomes?

Beyond documentation, some AI tools offer features that can directly enhance patient care. So, it’s worth asking: Can the tool generate plain language summaries for patients? Does it provide alerts or suggestions based on patient history? These additional features can significantly impact patient outcomes and should be taken into account.

11. Is The Tool Geared Toward Helping Therapists See as Many Patients as Possible? 

🏃⛰️ Run for the hills if a company focuses solely on allowing you to see as many clients as possible. 

🛑 This smells of of a company that not understand rehab and healthcare. 

Increasing therapist caseloads is one way to boost organizational revenue and justify the cost of these tools. But, so is attracting the best therapists and reducing burnout and turnover. Your AI tool should be designed to improve the care experience—and help you retain the client through their full course of care. If the company behind the tool is focused on these two metrics, then it is a company that understands healthcare. 

I will also note that how therapists spend “freed up” time is a management decision, and clinicians should be advocating to management about leveraging these tools to help support and restore their work-life balance (i.e., by not having to chart after hours and weekends). 

Conclusion

By asking these critical questions, therapists can navigate the AI landscape more effectively, ensuring that the tools they adopt are secure, efficient, and genuinely beneficial for both clinicians and patients. 

What additional questions do you have? Please share in the comments!