Medicine has always been hypothesis-driven, based on randomized controlled studies. As the healthcare industry transitions from a fee-for-service to a fee-for-value payment model, healthcare providers are increasingly assuming the financial risk of poor patient outcomes, and acknowledge the need to deliver higher quality care at lower costs. Now, the revolution that brought forth data-driven medicine can assist healthcare organizations in adjusting to these new transitions while simultaneously shifting the foundations of traditional medicine to higher ground.
Machine learning addresses non-linear relations and very complex relations that are depicted in data and are beyond the capacity of standard scoring models or formulas. Thus, in the medical world it could improve thousands of hypothesis-driven studies. Machine Learning offers the advantages of personalized, predictive and proactive outreach to patients at risk, and consequently leads to better treatment methods. It can create a recommendation based on a complex combination of a particular person’s health condition, medical history, lab results, resident country, race, sex, and age.
There is a new opportunity on the table to revolutionize healthcare, based on two innovations that have evolved over the last two decades:
For more information, we recommend reading “Machine Learning and the Profession of Medicine” in the Journal of the American Medical Association
Download ● McKinsey - The Big Data Revolution in Healthcare