Rapid Hypothesis Testing and Experimental Learning

Transform your practice from episodic care to proactive and preventive care through rapid hypothesis testing (also referred to as Experimental Learning )

Continuous Improvement can be accomplished through rapid hypothesis testing to validate and verify targeted changes to care, with data validation and cost/benefit analyses. 

Rapid Hypothesis Testing

Rapid Hypothesis Testing, also known as Rapid Experimentation, as it sounds, is a concept that encourages organizations to perform rapid experiments to ascertain if changes they propose can have a positive impact, but to do so in small experiments of little cost, thus allowing for multiple experiments to run in parallel and minimal cost. It is a concept used by a few companies you have likely heard of, including Amazon, Google, Intuit, Facebook, and others.            

It is through Rapid Experimentation that Amazon has grown not just as an eCommerce provider, but how they branched off into such services as Amazon Web Service (AWS), and become the world leader in cloud computing. The concept is simple, try a lot of things quickly on a small scale, and see if they work. If not. discard them. If they do (and there is a return on the investment), roll them out.   

So how does this apply to healthcare? It ultimately comes down to what is often referred to as the care plan. A care plan can have a wide range of meanings and can touch on a wide range of care types. For example, in the case of someone with a chronic condition receiving home health care, a care plan might include someone helping to pay monthly bills, providing on-site health checkups, providing around the clock nursing, delivering meals, or a host of other things.

Machine Learning training process