Machine learning is the perfect fit for the healthcare industry due to the vast amounts of complex data it generates. This technology has already proven useful in diagnosis and medical coding, and it is uniquely primed to revolutionize patient outcomes.
The healthcare industry is inundated with complex data, stored in multiple places and constantly evolving. This makes it the perfect target for machine learning, a form of artificial intelligence that allows computer systems to learn and adapt without following explicit instructions. By using algorithms and statistical models, machine learning can analyze and draw inferences from patterns in data, making it a great fit for the ever-changing world of healthcare.
In a recent interview with Harshith Ramesh, co-CEO of Episource and an expert in machine learning, he explains that healthcare is uniquely primed for machine learning due to the exponential increase in patient data over the past two decades. About 30% of the world's data is generated by the healthcare industry, making it an invaluable resource for machine learning models to become more accurate at predicting patient outcomes. The widespread use of electronic health records (EHRs) and other data sources, such as medical devices, wearables, and labs, contribute to this wealth of information.
Furthermore, the healthcare industry produces more objective data than other industries, thanks to standardized procedures, automated systems, medical coders, and expert physicians. This objectivity makes the data especially compatible with machine learning technology. As more provider organizations take on downside risk under value-based contracting models, it has become increasingly important to efficiently, accurately, and cost-effectively measure patient outcomes. Machine learning is the key tool that can help providers accomplish this goal, ultimately revolutionizing the healthcare industry and improving the lives of countless patients.
