Strategy, Data Science, EMR Platform Development
How Rootstrap Used AI to Build a Scalable & Isolated Architecture for Preprocessing Medical Records in the Healthcare Industry
“Having a view that summarizes main points and trends of current hospital stay, as well as points important to my specialty.” – Surveyed Physician
A survey of two popular EMR platforms EPIC & CERNER, which combined make up for 56% of the EHR market share and boast over $8 billion in revenue, showed a 58% satisfaction rate with both systems.
With this data, Rootstrap’s Engineers wanted to leverage AI to provide healthcare professionals with a more efficient and robust architecture to instantly provide an updated summary of patients medical data within a requested date range.
The key objective of this project was to help medical professionals process medical records at scale. This would allow them to save time, reduce human element, and provide accurate and consistent data in a language understood across the healthcare industry.
Rootstrap has experience developing applications for this industry, and after being approached by numerous potential clients on this topic, their Data Science team conducted extensive research that involved surveying over 100 Medical Doctors and Nurses.
The results further highlighted the need for a robust and effective EMR (emergency medical records) platform within the healthcare industry
“I spend more time dealing with my EMR than attending my patient” – Surveyed Physician
Dealing with this type of data can present complex challenges when attempting to clean, organize, and make sense of it all. Here’s why:
The biggest challenge is determining and summarizing what data is actually relevant.
Medical Data Issues:
Natural Language Processing:
Rootstrap’s Data Science team manually analyzed medical records and detected different types of problems. This would allow them to create tasks in the machine learning model for each of the problems detected. They used Natural Language Processing (AI for machines to read & understand language) for the extraction of key information to convert clean medical records to a semantic network, following UMLS standards (Unified Medical Language System).
As there is an infinite amount of vocabularies, hierarchies, definitions etc transforming plain text to a semantic network, developing the architecture with this ability to run tasks is the most efficient approach to extract key data.
PROCESS FOR EXTRACTING DATA
PROCESS FOR DEVELOPING ARCHITECTURE
Rootstrap’s Machine Learning Engineers withstood this time-consuming and highly complex challenge
They developed a platform/architecture with a real-time summary of a patient’s clinical encounters for any given date. This AI-driven product can accomplish tasks instantaneously that would otherwise be a time consuming task for humans. They focused their efforts on developing the architecture with the ability to process data in a Unified Medical Language System with over 200 Vocabularies.
By regularly running these types of complex tasks, this machine learning model is constantly improving its capabilities and understanding of medical records terminologies and complex data, and as a result, providing Physicians with instant data visualization in one single location.