Mars-Inspired Care

Why Big Data Necessitates Redesigning Health Care

Mars-Inspired Care


Guest Blog Post By: Lada Bogenschutz 

As technology advances, so does the ability to understand human behavior. Predictive analytics is a tool used to do just that. By studying people’s behavior, industries can determine the best way to approach consumers. In the past five years, different companies have increasingly used predictive analytics in order to see how they can more effectively apply their services to customers.

Right now, people utilize predictive analytics in diverse ways. Recent articles highlight the power of predictive analytics in retail industries. In a 2012 New Yorker article, “How Companies Learn Your Secrets,” Charles Duhigg explains how Target Corporation collects information about its potential customers in order to boost profits. Though many customers are unaware, Target is in fact able to buy data that helps them discover not only consumer habits, but personal habits as well.  As discussed in this intriguing article, some may not be completely comfortable with this idea.

Despite its controversy, the strategy is quite effective, since it helps Target earn millions of dollars. In order to achieve this feat, Target employs skilled statisticians such as Andrew Pole to evaluate the data. With extensive research to enforce his ideas, Pole explains a model for habitual patterns. If industries can understand consumer habits better, they can use that information to shape the way in which they deliver their product. Through clever design, Target can use knowledge of consumer habits to its advantage. This controversial yet insightful article clearly shows how people can use predictive analytics to reach a desired goal.

In other recent news, predictive analytics takes a similar role in health care. In a New Yorker article from January 2011, Atul Gawande poses the question, “Can we lower medical costs by giving the neediest patients better care?” In Camden, New Jersey, the neediest patients account for most of the medical costs, even though they are 1 percent of the patient population. (Gawande, 2011) His story describes how health professionals can use data to understand who the most expensive patients are, and how they can reduce their costs. Dr. Jeffrey Brenner is a key player in this mission, emphasizing the idea that “every data run tells a different human story” (Gawande, 2011). After studying and treating some of the most attention-demanding patients, this doctor proposes new approaches to treatment.

During his time with patients, he strives to understand their actions in order to get to the bottom of their issues. Dr. Brenner, along with other staff, go beyond the traditional types of treatment, often calling patients to discover outside factors such as housing, financial, or emerging health issues. Through his efforts, Brenner hopes to address the concerns of the neediest patients in a more personalized manner. In the end, by using inside information to the lives of different patients, hospitals can improve care and reduce medical costs.

As time progresses, predictive analytics becomes more and more prominent in health care. The 2013 article “The big-data revolution in US health care: Accelerating value and innovation,” describes data’s growing role in the health care system. Since health care expenditures account for 17.6 percent of our nation’s GDP, health care stakeholders are motivated to taking use new technology to their advantage in order to reduce costs. In today’s world, technology plays a vital role in accessing and analyzing patient information.

Because of the data revolution, improving technology encourages the best evidence-based practices constructed from in-depth patient information. At the same time, patient confidentiality needs to be protected. With these goals in mind, the authors explain five different value pathways that include: right living, right care, right provider, right value, and right innovation. All these pathways give equal attention to both care costs and patient well-being. Moreover, health professionals are no longer just recording data, but using data to predict how to approach the issues at hand and prevent negative health outcomes. Though by no means is the system perfected, they “estimate that the pathways could account for $300 billion to $450 billion in reduced health-care spending” (Kayyali, Knott, & Van Kuiken, 2013). With continued work, stakeholders could potentially use big data to change health care in a big way.

The work in other institutions inspires a similar project for the Project Mars team at CFI. Within the team, service designers Kate Dudgeon and Meredith DeZutter examine how Mayo can improve outpatient practice. By understanding patients at a level beyond their medical need they are hoping to more effectively create a customized treatment that fits the personalized needs of the patient. The designers realize that, “it’s not just about having the data; it’s about knowing how to use it.”

Through in-depth investigations into patients, experimentation, and review of other research over the past year and a half, they have created an initial pre-visit evaluation with open ended narrative questions, allowing patients to express themselves in a more dynamic way. The team is in the early phases of developing and putting into practice the infrastructure to collect, review, and take action on patient provided pre-visit information. Eventually, the team wishes to understand how a wide range of information can start to standardize the process and build an even more efficient predictive tool. In three to five years, the team anticipates this tool can be used to better know and understand the patient more than ever before. If they can use data as successfully as the outside world, they can provide more focused customized care, increase productivity and patient satisfaction.


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