PreVizAI

Inspiration

Imagine you’re sick. You walk into a clinic, and before you even see the doctor, you’re handed a clipboard with 10 pages of repetitive questions. The other side of the story is that, doctors spend over 36 minutes per patient visit inside EHR systems. This time adds to burnout, reduces quality patient interaction, and creates inefficiencies across healthcare. Our inspiration for PreVizAI was simple: give that time back to clinicians. We wanted to design an AI copilot that transforms how providers interact with data, making documentation effortless, all in order to enabling more human-centered care.

What it does

PreVizAI listens, transcribes, and intelligently structures patient-provider conversations. It generates clinical notes in real time, integrates with medical knowledge bases, and provides a visual dashboard for faster review. With this, clinicians can cut note-taking time from minutes to seconds while still maintaining accuracy, compliance, and usability.

How we built it

• Speech-to-Text: We used OpenAI Whisper for real-time transcription of patient encounters.
• CedarOS Copilot Framework: Integrated to enable conversational UI and contextual prompts.
• Frontend: React with TailwindCSS for clean, responsive UI; Recharts for visualization.
• Backend: Python for handling transcription, note structuring, and API orchestration.

Challenges we ran into

• Ensuring Whisper transcriptions and CedarOS prompts stayed responsive enough for real-time use.

Accomplishments that we're proud of

• Integrated OpenAI Whisper with CedarOS to achieve near real-time medical transcription.
• Built a React + Tailwind frontend with dynamic visualization (Recharts) to present insights clearly.
• Developed a Python backend that contains transcription, note generation, and data serving.

What we learned

• Documentation is one of the biggest pain points in healthcare. Even small inefficiencies add up across hundreds of patient visits, amplifying burnout.
• Context transforms value. Transcripts become powerful only when paired with medical ontologies, structured formats (like FHIR), and visual dashboards.
• AI copilots must support human judgment, not replace it. Trust grows when the system enhances a provider’s workflow instead of dictating it.
• Time saved translates into patient care. Every minute taken back from EHRs is a minute that can be spent face-to-face with patients.

What's next for PreVizAI

• Full EHR integration with FHIR APIs.
• Expanding multimodal support (voice, text, image data).
• Adding clinical reasoning layers for real-time treatment suggestions.
• Scaling to patient-facing features: automated summaries, educational explainers, and proactive care reminders.