The field of health data analytics focuses not only on the care of individuals, but also defines best practices based on desired outcomes that can be applied to entire populations. Medical providers and insurance plans are capitalizing on trends and patterns identified using data analytics to adopt innovative strategies for people at risk for health issues.
Certain populations share interrelated conditions and factors that influence lifelong health. Chronic diseases such as diabetes are a primary focus of population health analytics. Diabetes affects more patients than heart disease, cancer and asthma combined, and the financial impact is staggering. According to the CDC’s 2017 National Diabetes Statistics Report, 30.3 million people have diabetes and 7.2 million of these cases (almost 24 percent) are undiagnosed. The Health Care Incentive Improvement Institute found in 2016 that preventable diabetes costs a total of $16 billion per year.
Identifying those individuals who have diabetes — and preventing future cases — lie at the intersection of data science and health care delivery. The data necessary to find pre-diabetic patients and evaluate diabetic care is available from several sources, including electronic health records (EHRs), medical claims, laboratory results and pharmacotherapy information. There is no shortage of health data; in fact, today’s biggest challenge is understanding the data we do have.
Advanced data mining techniques allow for more automated investigation of hidden connections in huge data sets. Adaptive computer models can actually learn when exposed to new data and reliably predict the impact of interventions and treatments. But this cannot be done without a human understanding of the context in which all these results are generated. Skilled data scientists take the time to evaluate and explore results, looking for meaningful patterns and trends. They work together with researchers and health care professionals to focus on problems and solutions, telling a story with data that can have an important impact on the successful treatment of patients.
For example, chronic conditions require active self-management. Lifestyle changes like diet modification, stress reduction and regular exercise are important to each person’s well-being but aren’t available in a one-size-fits-all solution. While activity trackers and workplace incentives may work well for some groups of employees, evidence may show other approaches to be even more effective. A focused analysis on real outcomes based on multiple variables can illuminate where health care dollars will do the most good.
Data analytics benefits the partnership between providers, payers and employers by identifying trends and compiling treatment results across patient populations. This yields significant findings to be used in developing policy, protocol and processes. Marquette’s Health Care Data Analytics Master of Science program takes advantage of the strengths and trajectories of mathematics, statistics, computer science and nursing in combination with our Jesuit tradition to make a career in the innovative, exciting field of data analytics a reality.