In a world of personalized experiences, from our Facebook feeds to our shopping recommendations, technology still hasn’t perfected personalized healthcare. Patients have tried to take healthcare into their own hands with on-demand doctor appointments, fitness trackers and genetic profiling, but beyond these consumer developments, how are doctors and hospitals using technology and data to improve healthcare? At a time when healthcare costs are in flux and just one percent of the population accounts for over one-fifth of total US healthcare expenditures, it's time we use modern analytic techniques to get as personalized as possible when it comes to patient care.

Finding specific groups who have high incidences of expensive diseases but where the cost of intervention is low is one way to make a tangible change in healthcare. To do this, hospitals need advanced analytics with augmented intelligence - that is, technology that lets a computer do the initial work to find patterns that a human being would not see (typically because a human can only think of a few questions or hypotheses while a computer can ask millions of questions without tiring). Then, allow experts to augment those findings with domain knowledge to determine if the pattern is worth acting on.

To cause people to act on patterns, we have to get specific. For example, statistics show teens are four times more likely than adults to get into crashes or near-crashes when texting and driving. For a specific teen, the probability may seem too small to bother. But, if we could show (hypothetically) that 18-20 year old females with less than six months of driving experience, who use Phone A and drive Car B, have a 50% probability of crashing, then maybe that much more specific group of teens would pay more attention to the very real risk that pertains to them.

There are so many unique characteristics when it comes to our health -- diagnoses, treatments, age, gender, race, pre-existing conditions, current medicine use. Traditional data analysis led by humans can see broad trends, but these are difficult to act on widely because they seem so generic. However, with the help of advanced analytics and augmented intelligence, doctors are better equipped to find patterns specific to small groups of people so that they can have a better chance of encouraging action and change.

In 2013, McKinsey & Co. used BeyondCore, now a Salesforce company, to analyze healthcare cost increases for 30 million patients across a million variable combinations at StrataRx Conference. They discovered that half of 18-35 year old females with diabetic ketoacidosis were readmitted to the hospital -- for such patients, this was not an abstract concern but an immediate and imminent possibility. The readmissions were primarily due to noncompliance with insulin regimens. The physicians who McKinsey briefed on this pattern were initially surprised, because young women are typically better at taking their medications. However, doctors soon realized that they had indeed seen such cases, but had not recognized it as an actionable pattern. They eventually figured out that for young women, not taking insulin might be an unorthodox weight loss strategy. The key here was that medical researchers who knew the biological drivers of diabetes had not thought about the psychological behavior patterns of young women and had thus never thought to ask this question. McKinsey themselves said they might have taken seven or eight months to manually test 250 variables while the automated analysis evaluated millions of questions in just hours.

Note that machine learning could not completely solve the problem on its own. It detected the specific pattern but human experts had to use their years of domain knowledge to translate the patterns into the fact that young women were dieting by not taking insulin. They also had to figure out how to best counsel this group of patients so that better health outcomes were actually delivered. The human and the machine working together could achieve what neither could solve alone. Once the pattern is detected, the adjustment to care is preventive, safer, and less costly -- but would be impossible if the pattern was not detected in the first place.