Improving Patient Outcomes with Gen AI-Assisted Healthcare

Gen AI helped a healthcare provider auto-generate SOAP (Subjective, Objective, Assessment, Plan) notes from doctor-patient conversations helping improve patient care decisions, optimize referral process, and generate drug recommendations.

The Challenge

Our client, a healthcare provider, wanted to enhance doctor-patient conversations for better patient care:
  • Labor-Intensive: Creating and entering SOAP notes into EHR systems was time-consuming.
  • Lack of Insights: Doctor patient conversations were recorded but lacked further analysis and insights from data.
  • Need for Automation: Client wanted to reduce note creation time and enable real-time documentation i.e. more time spent on patient care.
  • Referral Process: Manual categorization and referral of non-specialized cases was time consuming.

Our Solution

We developed and deployed a comprehensive solution leveraging Gen AI and cloud technology:
  • REST API: Secure, high-performance API developed to convert doctor-patient conversations into SOAP notes with a response time of less than 30 seconds.
  • LLM Integration: Self-hosted Large Language Model (LLM) utilized for generating medical SOAP notes.
  • Cloud Deployment: HIPAA-compliant solution hosted on AWS in the Australia region, ensuring scalability and performance.
  • Quality Assurance: Rigorous testing to ensure API functionality, LLM accuracy, and overall system performance under concurrent usage.

The Impact

Gen AI delivered significant improvements across various aspects of healthcare delivery:
  • Automated Note Generation: Eliminated manual tasks, enabling real-time documentation and allowing practitioners to focus on patient care.
  • Time Savings: Reduced response time to less than 60 seconds for SOAP note generation and other AI-assisted tasks.
  • Scalability: Cloud-based infrastructure supports growth and increasing interaction volumes, allowing 30+ doctors to use the system concurrently.
  • Improved Decision Support: Automated drug recommendations and follow-up questions enhanced the quality of patient care and clinical decision-making.
  • Optimized Referral Process: AI-driven categorization and automated referral letter generation streamlined the process of directing patients to appropriate specialists.

Download Detailed Case Study

DOWNLOAD NOW

Related Resources

Case Studies

{{item.authore_images_alt}}

Vehicle damage detection using Computer Vision for insurance company

Our customer, a born-digital unicorn in Insurance industry wanted to use Computer Vision for automation.
{{item.authore_images_alt}}

Recommendation engine development for a Media Firm

Driving multifold improvement in recommendation efficiency Our customer in Media Distribution...
{{item.authore_images_alt}}

Digitizing Electronic Health Records and Making them searchable through Natural Language Processing

NLP based digitization of Electronic Health Records (EHR)
Share this...

To provide your business a competitive edge with our AI solutions

Contact Us