Waste is a big problem in healthcare. As much as one-third of U.S. healthcare spending is for naught, argued author Shannon Brownlee in her 2008 work “Overtreated: Why Too Much Medicine Is Making Us Sicker and Poorer.” A PwC report titled “The Price of Excess” went even further, stating that more than half of the country’s healthcare spending is waste.
It’s certainly not hard to point to inefficiencies in healthcare and there is plenty of finger-pointing to go around amongst the various entities in the healthcare web. For instance, on the clinical side, there is widespread redundant testing. On the patient side, there is a significant problem of noncompliance with doctor’s orders—whether they be to take a medicine daily, lose weight, or change one’s diet. The fee-for-service model has been implicated in medicine’s ever-growing costs, too, as have the healthcare ecosystem’s high operational costs.
The degree to which information technology can address waste in healthcare is fascinating. Artificial intelligence, or at least computer-driven analytics, can almost certainly help us greatly improve the value of healthcare our healthcare system. The question is when. At present, healthcare spending may be slowing, but overall costs continue to climb.
The need for a data science revolution in healthcare has never been greater as medicine becomes more personal, information driven, and mobile, and the web that is our healthcare system grows ever more complex.
In these respects, our healthcare system is similar to social platforms like LinkedIn. And one of the reasons that LinkedIn has been so successful is its reliance on analytics technology and data science. In essence, the site leveraged the power of numbers to address its most important needs.
The Harvard Business Review recently pointed out that about six short years ago, LinkedIn had close to 8 million accounts but, as the article explains “users weren’t seeking out connections with the people who were already on the site at the rate executives had expected.”
Following the lead of LinkedIn’s data scientist Jonathan Goldman, PhD, LinkedIn debuted the “People You May Know” feature, which caused the number of personal connections across the site to skyrocket. The feature became one of the biggest drivers in the site’s burgeoning success.
Our healthcare system needs open data streams and problem solvers like Goldman, who, as the above article explains, “make discoveries while swimming in data.” Such people “are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.”
Where are the data scientists in healthcare? We need them fast...