Team21_2B_AutoChart

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Introduction

Our project, AutoChart, is inspired by the critical challenges faced by the Emergency and Family Medicine Departments, characterized by a severe manpower shortage leading to prolonged patient wait times and doctor burnout. AutoChart aims to alleviate these pressures by streamlining medical documentation processes.

Inspiration

The heartbreak over manpower shortages in critical healthcare departments motivated us. These shortages result in patients enduring long wait times in discomfort and doctors experiencing extreme burnout, primarily due to the overwhelming burden of charting and medical documentation.

Our goal is to lighten doctors' workloads and expedite patient treatment with our innovative solution.

What It Does

AutoChart transforms doctor-patient conversations into transcribed, well-organized, medically precise charts. This process involves:

Transcribing the conversation using OpenAI's Whisper model.
Organizing the transcription into a structured medical chart with the help of OpenAI's GPT-4 model.

How We Built It

Our development process included:

Leveraging OpenAI's Whisper Model: Fine-tuned for Singaporean English using the Singapore ASR dataset.
Utilizing OpenAI's GPT-4: For formatting the conversation into a medical chart.

Challenges We Ran Into

The size of the ASR dataset was formidable at 1.5TB, leading us to use only a small sample.
Data transformation required converting pure audio data into an amplitude array at a 16000Hz sampling rate, demanding significant computational resources.
Extended training time for the Whisper model due to its complexity and the high computational resources required.

Accomplishments We're Proud Of

Successful Application Implementation: Hosted on Huggingface Spaces.
Effective Training: Achieved with the Whisper model.

What We Learned

Our journey taught us valuable lessons in audio data processing, including:

Transforming audio data into an amplitude array.
Utilizing the log-mel spectrogram to reduce audio data dimensionality.
Fine-tuning the Whisper model for enhanced audio data interpretation.

What's Next for AutoChart

Moving forward, we aim to:

Collaborate with Medical Professionals: To refine the document's precision.
Explore Integration Opportunities: In hospitals and clinical settings.
Pursue Regulatory Approvals: Such as FDA/ISO13485 certifications, to facilitate medical industry adoption.

Conclusion

AutoChart represents a significant step forward in addressing the pressing challenges within the healthcare documentation process. By leveraging advanced AI technologies, we envision a future where healthcare professionals can focus more on patient care and less on administrative tasks.