Tim OReilly - How Data Science Is Transforming Health Care

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In the early days of the 20th century, department store magnate John Wanamaker famously said, I know that half of my advertising doesnt work. The problem is that I dont know which half.

The consumer Internet revolution was fueled by a search for the answer to Wanamakers question. Google AdWords and the pay-per-click model began the transformation of a business in which advertisers paid for ad impressions into one in which they pay for results. Cost per thousand impressions (CPM) was outperformed by cost per click (CPC), and a new industry was born. Its important to understand why CPC outperformed CPM, though. Superficially, its because Google was able to track when a user clicked on a link, and was therefore able to bill based on success. But billing based on success doesnt fundamentally change anything unless you can also change the success rate, and thats what Google was able to do. By using data to understand each users behavior, Google was able to place advertisements that an individual was likely to click. They knew which half of their advertising was more likely to be effective, and didnt bother with the rest.

Since then, data and predictive analytics have driven ever deeper insight into user behavior such that companies like Google, Facebook, Twitter, and LinkedIn are fundamentally data companies. And data isnt just transforming the consumer Internet. It is transforming finance, design, and manufacturingand perhaps most importantly, health care.How is data science transforming health care? There are many ways in which health care is changing, and needs to change. Were focusing on one particular issue: the problem Wanamaker described when talking about his advertising. How do you make sure youre spending money effectively? Is it possible to know what will work in advance?

Too often, when doctors order a treatment, whether its surgery or an over-the-counter medication, they are applying a standard of care treatment or some variation that is based on their own intuition, effectively hoping for the best. The sad truth of medicine is that we dont always understand the relationship between treatments and outcomes. We have studies to show that various treatments will work more often than placebos; but, like Wanamaker, we know that much of our medicine doesnt work for half of our patients, we just dont know which half. At least, not in advance. One of data sciences many promises is that, if we can collect enough data about medical treatments and use that data effectively, well be able to predict more accurately which treatments will be effective for which patient, and which treatments wont.

A better understanding of the relationship between treatments, outcomes, and patients will have a huge impact on the practice of medicine in the United States. Health care is expensive. The U.S. spends over $2.6 trillion on health care every year, an amount that constitutes a serious fiscal burden for government, businesses, and our society as a whole. These costs include over $600 billion of unexplained variations in treatments: treatments that cause no differences in outcomes, or even make the patients condition worse. We have reached a point at which our need to understand treatment effectiveness has become vitalto the health care system and to the health and sustainability of the economy overall.

Why do we believe that data science has the potential to revolutionize health care? After all, the medical industry has had data for generations: clinical studies, insurance data, hospital records. But the health care industry is now awash in data in a way that it has never been before: from biological data such as gene expression, next-generation DNA sequence data, proteomics, and metabolomics, to clinical data and health outcomes data contained in ever more prevalent electronic health records (EHRs) and longitudinal drug and medical claims. We have entered a new era in which we can work on massive datasets effectively, combining data from clinical trials and direct observation by practicing physicians (the records generated by our $2.6 trillion of medical expense). When we combine data with the resources needed to work on the data, we can start asking the important questions, the Wanamaker questions, about what treatments work and for whom.

The opportunities are huge: for entrepreneurs and data scientists looking to put their skills to work disrupting a large market, for researchers trying to make sense out of the flood of data they are now generating, and for existing companies (including health insurance companies, biotech, pharmaceutical, and medical device companies, hospitals and other care providers) that are looking to remake their businesses for the coming world of outcome-based payment models.