Pippen was built with patient privacy at its core, ensuring the physical, technical, and administrative safeguards required for PHIPA and PIPEDA compliance. Privacy features include (but are not limited to):

• Data hosted on private, secure servers all within Canada
• Data encrypted at rest as well as in transit
• Auto-deletion of all audio recordings
• Minimal information required to start an encounter
• Formal PIA conducted

Yes! Pippen gives you the power, flexibility and functionality of large language models in your everyday practice without worrying about compromising privacy and security.

You can sign up directly at: app.pippen.ai/signup.

Pippen is offered for free with a daily token usage threshold (what’s a token? See here). Pippen is also offered on a subscription basis with options for Lite, Standard, and Pro.

Lite offers <50,000 tokens per day and is great for physicians who have 30 or fewer patients per day and primarily use the scribing function. 

Standard offers 50,000 to <150,000 tokens per day and is well suited for physicians who have 30 to 45 patients per day and have comprehensive use of Pippen’s features.

Pro offers 150,000 to 230,000 tokens per day and is best for physicians who see very high volumes (>45 patients per day).  

Pippen helps family physicians with more than just scribing the medical encounter. Think of Pippen’s platform as a secure playground for using AI in your practice – ask Pippen to suggest billing codes, write referral letters, suggest treatment options, and more.

Pippen is developed using a combination of large language models (LLMs), proprietary algorithms, and specialized datasets. It was trained on a broad and diverse range of publicly available datasets, along with licensed content. Pippen is further fine-tuning the model using proprietary medical datasets.

Pippen is continuously striving to identify and reduce bias in its platform. Although AI isn’t expected to be perfect, emerging research suggests that our foundational models are able to support clinicians in making diagnosis decisions without introducing significant unconscious racial and ethnic biases.