Medical education is dynamic and constantly adapting to the needs of society. With new technological advances, scientific discoveries, and healthcare policies arising each day, the amount of information medical students are required to learn increases exponentially. Many describe the early years of medical education as a vicious cycle of cramming and forgetting with block exams, shelf exams, and board exams. Long-term retention is rarely rewarded and the integration across topics is limited. On the contrary, medicine IS a life-long learning process that is heavily dependent on the ability to attain, integrate, and apply data.
Unfortunately, time is limited, and as a result, cramming often prevails as the method of choice for many students. As medical students, we constantly find ourselves re-learning large amounts of information time and time again, always preparing for the next exam or hurdle, rather than thinking years down the line when we will be taking care of patients. This is very inefficient.
In June, Duke medical students wrote an article entitled “Want to enhance medical education? Use Spaced Repetition”. This article proposed a strategy that revolves around the cognitive technique known as spaced repetition. Spaced repetition takes advantage of time and reinforces one’s knowledge the moment before one forgets it. This technique involves reviewing material according to a schedule determined by a temporal relationship known as the “spacing effect”.
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.