In the late 1800s, automobiles were the work of individual artisans who carefully crafted their creations from start to finish. The process was slow and painstaking, and each finished product was a little different than the others. They were also incredibly expensive, which is why only the wealthy owned them.

Then about a century ago, Henry Ford transformed the world with his Model T, the first automobile to apply the principles of mass production. Other manufacturing sectors quickly adopted Ford’s methods, and individualization took a back seat.

The pendulum began to swing back toward the individual in the 1990s, however, with a movement toward mass customization, a method that combines “custom-made” flexibility with the low unit costs and predictable quality of mass production. For example, Nike allows customers to choose colors for every element of a standard shoe. Japanese eyeglass retailer Paris Miki uses data, images and preferences to recommend best-fit glasses.

Automakers such as Ford—whose founder is famously quoted as saying customers could have a car painted in any color as long as it’s black—now offer millions of variations in style and functionality to cater to the preferences of increasingly selective consumers. The rise of the Internet has empowered customers to have a collaborative dialog with the providers of goods and services to generate a highly personalized outcome.

But what about when the customer is a patient, and the product is a potentially lifesaving treatment? Healthcare has stubbornly remained an outlier, a chimera of art and science, an odd combination of artisanal gut and intuition and evidence-based decision-making. Part of the reason, of course, is that unlike automobiles or other manufactured products that have parts that could be used in any similar unit, each human is unique. It is difficult to know where to customize generic principles and approaches, so healthcare tends to paint with a broader brush.

It is also due to the high stakes and lagging use of information technology to provide performance-based feedback to both providers and patients. In essence, decision-making has been stuck between cottage industry and the data-driven enterprise.

That is beginning to change, however. The widespread adoption of electronic health records has been a step in the right direction. According to the Office of the National Coordinator for Health IT, more than 75 percent of non-federal acute care hospitals had a basic EHR, and almost 97 percent had a certified EHR by the end of 2014. Yet in many cases, the EHR has been a mere repository for data, a record of what happened rather than a contributor to what should be done. In other words, it’s served as an electronic version of the traditional paper record.

The growing use of advanced analytics for population health management (PHM) is beginning to take better advantage of all that data. But it is still looking at a mass scale. Interoperability challenges between providers and even EHRs within a single health system also continue to be a limiting factor, preventing the sharing of data that would make PHM conclusions more reliable and effective.

One of the most exciting developments is the rapid broadening of available datasets beyond traditional clinical and claims sources. The internet of things, rise of social media, and the maturation of the genomics/panomics are rich sources of information poised to dramatically enhance our understanding of health and wellness at a far more granular level. Personalized or precision medicine leverages broad data sets including clinical, lifestyle and genomics data to move beyond the one-size-first-all model into care that is more individualized to the specific patient. It promises cost savings, better patient outcomes, and progress against diseases such as cancer, diabetes, and potentially even aging.

Personalized medicine has been slower to emerge than many had predicted. But according to a new study conducted by Oxford Economics, more than two-thirds of healthcare professionals say that personalized medicine is already having a measurable effect on patient outcomes. Roughly the same number expect that it will in the next two years.

Personalized medicine often conjures visions of a hyper-personalized therapy. It also, however, means the ability to hone evidence-based medicine by dividing large population segments into smaller groups based on medical, panomic and personal habits. Providers can then make decisions based on analysis of previous outcomes of similar patients across an enterprise, region, country, or even globally, tailoring treatment for the smaller group – or even the individual.

How will this work in practice? Take the case of a 70-year-old woman with a newly diagnosed cancer and a past medical history notable for lung cancer, Type II diabetes, and congestive heart failure. Although there are likely a number of clinical trials studying the cancer, the patient would almost certainly have been excluded from all of them due to the history of previous cancer, her age, as well as her other chronic illnesses. So, we would be stuck taking the clinical trial and trying to extrapolate based on recollection of previous similar patients. That is artisanal practice based data blended with gut and intuition. In the data-driven enterprise, analysis might show three possible courses of treatment. Of the two that have been historically effective, one might appear to be better tolerated in female diabetics.

Armed with this information at the point of care, the physician and the patient can discuss the situation and determine which course of treatment will be the best to follow given the patient’s specific goals.

But here’s the challenge. Although healthcare organizations are investing heavily in tools such as advanced storage and analytics tools to capture and analyze data, they still need to fully build out IT capabilities and find workers with the right digital skills. In addition, clinicians need to retool for the advent of richer, more nuanced and complicated datasets that are the coin of the realm in personalized medicine. As an example, the impressive advancements in panomic research have been slow to translate into impact at the bedside due to the challenges in creating the right multidisciplinary teams and the lack of standards that foster easy interoperability of information. When tapped, however, the outcomes are impressive, as researchers at institutions like Stanford University have discovered.

Yet we’re still trying to balance patient privacy with data sharing, as institutions also address issues of fostering collaboration. Finding meaningful trends using personalized medicine requires a huge amount of data, and for less common illnesses more data than any single institution can currently access. Solutions such as CancerLinQ from ASCO tackle this problem by aggregating data from member institutions, but more must let go of provincial notions of data ownership and accept the reality that sharing data and research outcomes is the key to finding cures and improving overall quality of care.

And while the economic case for personalized medicine is strengthening, it comes down to much more than cost savings. Truly personalized medicine will achieve superior outcomes with fewer complications while delivering care more closely aligned with patients’ individual values. Applied across a population, “mass customization” will drive the broad adoption of personalized medicine to improve quality and predictability. And that is only the start. Each additional patient’s experience will further contribute data, creating a virtuous cycle of learning—and thereby fostering the creation of the data-driven and personalized healthcare system that we want for our families, friends and ourselves versus relying on the knowledge and experience of a single individual – no matter how skilled.

Henry Ford’s application of mass production revolutionized the world and bettered the lives of individuals in ways that even he couldn’t foresee. Mass customization took those cost and quality gains while tailoring to the needs and preferences of the individual. Personalized medicine on a mass scale is on the brink of doing the same – but in a profoundly more meaningful way. It is time that we pivot strongly from cottage industry style of decisions by gut and intuition to an era of data-driven decision-making. There is simply too much at stake not to.

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David Delaney

David Delaney

David Delaney, MD, is Chief Medical Officer and head of the US healthcare team at SAP.