Revolutionizing ER Experience with AI: A TeleMedCo Case Study
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
- Healthcare & Hospitals
- Product Research & Development
- Construction Management
- Speech Recognition
- System Integration
TeleMedCo, a startup formed by a team of doctors and business professionals, identified a significant inefficiency in the healthcare industry. The emergency room (ER) experience was found to be massively expensive and often inefficient, with 50-80% of ER visitors being in non-critical condition. This led to doctors having to prioritize treatment, running tests, and evaluating medical histories, which could result in slow or incorrect diagnoses. The challenge was to modernize, streamline, and expedite the ER experience for both doctors and patients. However, to secure buy-in from hospitals and doctor groups, TeleMedCo needed a proof-of-concept that caregivers could interact with and use. The team lacked software, hardware, or artificial intelligence expertise, and thus required a technology partner to develop a diagnosis solution and integrate it into a working prototype for demonstration purposes.
About The Customer
TeleMedCo is a startup formed by a team of doctors and business professionals. They identified a significant inefficiency in the healthcare industry, particularly in the emergency room (ER) experience. The team recognized that the ER process was not only expensive but also inefficient, with a large percentage of ER visitors being in non-critical condition. This led to a need for doctors to prioritize treatment, often resulting in slow or incorrect diagnoses. TeleMedCo sought to apply cutting-edge technological systems and processes to modernize, streamline, and expedite the ER experience for both doctors and patients. However, they lacked the necessary technical expertise to develop a solution and required a technology partner to help them.
In their quest for seed capital, TeleMedCo partnered with ENO8 to develop a working prototype capable of automating the entire ER 'chest pain protocol'. This prototype served as a proof-of-concept to secure backing. The solution began with a voice user interface between an iPad app and the patient. ENO8 integrated the app with IBM’s Watson and trained the AI to analyze and recognize specific conditions within chest x-rays and/or EKGs. This allowed the iPad app to move the patient either to a non-critical care protocol or automatically video conference in a physician if Watson identified an irregularity within the x-ray or EKG. The solution utilized capabilities such as native iPad app, SWIFT, Python, IBM Watson artificial intelligence integration, real-time image recognition, voice user interface, and video conferencing.
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