Earlier this month Shiv and Ryan published a piece in the Annals of Internal Medicine, entitled What Can Medical Education Learn from Facebook and Netflix? We chose the title because, as medical students, we realized the tools our classmates are using to socialize and watch TV use more sophisticated algorithms than the tools we use to learn medicine.
What if the same mechanisms that Facebook and Netflix use—such as machine learning-based recommender systems, crowdsourcing, and intuitive interfaces—could transform how we educate our health care professionals?
For example, just as Amazon recommends products based on other items that customers have bought, we believe that supplementary resources such as questions, videos, images, mnemonics, references, and even real-life patient cases could be automatically recommended based on what students and professionals are learning in the classroom or seeing in the clinic.
That is one of the premises behind Osmosis, the flagship educational platform of Knowledge Diffusion, Shiv’s and Ryan’s startup. Osmosis uses data analytics and machine learning to deliver the best medical content to those trying to learn it, as efficiently as possible for the learner.
Since its launch in August, Osmosis has delivered over two million questions to more than 10,000 medical students around the world using a novel push notification system that syncs to student curricular schedules.
Osmosis is aggregating medical school curricula and extracurricular resources as well as generating a tremendous amount of data on student performance. The program uses adaptive algorithms and an intuitive interface to provide the best, most useful customized content to those trying to learn.