Enhancing Clinical Ambient Intelligence with Synthetic Patient Physician Conversations
Empowering Healthcare Providers & Patients: Enhancing ML Training with Synthetic Patient-Physician Conversations in a Clinical Environment Setting.
Project Overview
In the dynamic healthcare industry, effective communication between healthcare providers & patients is paramount to delivering quality care. However, traditional methods of patient-provider interactions often face challenges in capturing the nuances of medical conversations.
In an effort to advance medical training, a novel approach was taken to create synthetic conversations between Practicing/Real Physicians & Patients in the US. By simulating real-world conversations, healthcare providers can improve patient education, enhance communication, and streamline care delivery. The project aimed to collect and transcribe audio of role-played interactions for clinical AI Model training purposes, focusing on spontaneity and realistic scenarios.
Key Stats
Hours of synthetic
data collected
2,000 Hrs
No. of
Doctors
850+
Use Case
Synthetic Audio Generation &
Transcription
Challenges
Synthetic conversations needed to be realistic and should accurately reflect the complexities of real-world medical interactions, including medical terminology, patient symptoms, and provider assessments.
The project aimed to create a diverse pool of synthetic conversations that represented a wide range of accents, ethnicities, and age groups, reflecting the diversity of the U.S. population.
Strict measures were implemented to safeguard participant privacy, ensuring that no personal information was shared or compromised during the data collection and transcription processes.
Handling nonsensical or missing details in machine-generated scenarios while preserving the educational value of the interactions.
Participants required familiarization with provided scenarios without directly reading from them during the interaction.
A key challenge was managing ambient noise levels to ensure background sounds added realism without obscuring the primary conversation, requiring precise audio balancing.
Varied acoustic properties across different recording setups presented difficulties in maintaining consistent audio quality for all sessions.
Solution
To overcome these challenges, the project adopted the following strategies:
- Synthetic patient-physician conversations were recorded in a clinical environment setting, for which real physicians specializing in diverse healthcare fields were recruited. These professionals contributed to developing conversations designed to elicit natural dialogue reflective of typical medical scenarios, such as Hypertension, Diabetes, Pain Management etc.,. that closely resembled the flow and nuances of actual human conversations.
- Recruited a diverse participant pool to reflect the diversity of the U.S. population and healthcare professionals to ensure a diverse pool of speakers, capturing a wide range of accents, ethnicities, and age groups. And hence, real physicians practicing in various healthcare specialties were recruited from different parts of the US.
- Shaip implemented stringent data privacy and security protocols with a unique identifier system for tracking speaker participation while maintaining anonymity.
- Provided guidelines for participants on how to handle nonsensical machine-generated content.
- A nuanced layer of environmental noise (Ambient Noise Inclusion) was integrated, representative of an active adult family medicine clinic. 100% of the recordings featured ambient clinic or hospital noise factors, such as fan sounds, mechanical hums, medical device beeps, and muted
background conversations. - Real World Clinic Simulation for each recording location was meticulously arranged to mirror the dimensions and acoustics of a standard 8×8 foot family medicine exam room, not exceeding 200 square feet, with similar hard- surface flooring. The rooms were furnished with essential items such as chairs, tables, cabinets, and exam table to create a typical clinical setting.
Project at a glance
- Scope: Audio collection and transcription of synthetic healthcare interactions.
- Duration: Each interaction aimed for 5 minutes or more, targeting an average of 10 minutes.
- Volume: 2,000 hours of synthetic healthcare provider & patient conversations generated.
- Interactions: 12,000-24,000 individual synthetic interactions of 10 minutes’ average duration.
- Geography: US-based participants only.
- Diversity Goals:
- Gender: 400 male, 400 female, 50 non-binary or undisclosed.
- Age: Even distribution across age groups from 20 to 60+.
- Ethnicity: 55% of participants were Caucasian Americans, 8% African Americans, 8% Hispanic, 20% Asian, and 9% Other
- Technology: Use of iPhone and Android devices for recording.
- Healthcare Professional Participation: Physicians, physician’s assistants, nurses, and nurse practitioners.
The Outcome
Synthetic healthcare provider & patient conversations have the potential to revolutionize the way healthcare is delivered. By leveraging AI, we can improve communication, enhance patient education, and streamline care delivery, ultimately leading to better patient outcomes.
- High-quality synthetic conversations: The project successfully generated 2,000 hours of high-quality synthetic healthcare provider and patient conversations, meeting the client’s requirements for accuracy, diversity, and privacy.
- Balanced Representation: A healthy mix of genders, ages, and ethnic backgrounds among the participants, which contributed to the authenticity and inclusiveness of the training material.
- Comprehensive Database: Established a repository of synthetic conversations that can be used for various training and medical educational purposes.
- Enhanced communication: The synthetic conversations provided a valuable resource for healthcare providers and researchers, enabling them to improve patient care and communication strategies.
- Streamlined processes: The AI-generated conversations helped streamline documentation processes, reducing administrative burden and allowing healthcare providers to focus more on patient care.
- Enhanced Realism: The controlled yet authentic environment significantly increased the realism of the training data, providing medical professionals with a more immersive learning experience.
- Sound Diversity: The diversity of background sounds in the recordings added an additional layer of complexity to the training, preparing trainees for real-world clinical environments where multiple auditory stimuli are present.
Shaip’s integration of realistic ambient noise in their physician and patient conversations has significantly elevated our training data. The attention to environmental detail in these high-quality recordings has not only enriched the learning experience but also better prepared our providers for the dynamic nature of patient care environments. We’ve seen notable improvements in patient interactions, provider efficiency, and the accuracy of our documentation processes as a result.
Shaip’s dedication to data privacy and security further solidifies our trust in their services. Our organization is excited to sustain and expand this fruitful collaboration.
