Understanding the Basics of Predictive Analytics in Healthcare Graphic from Husson University.

It’s difficult to overstate the way big data has altered multiple industries. As author and consultant Geoffrey Moore put it, “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”

One benefit of big data is being able to predict behavior. For instance, when people apply for a loan, credit scores assess the likelihood of default. With the information provided by big data, marketing campaigns can be better focused on generating customer responses or purchases and cybersecurity tools can better monitor actions and spot abnormalities that suggest vulnerabilities or threats. In addition, police departments can use algorithms to predict when and where crime will happen.

Dozens of case studies exist for using big data to predict the future, and healthcare is no stranger to the potential benefits. “Hiding within those mounds of data is knowledge that could change the life of a patient or change the world,” biomedical researcher Atul Butte said in Stanford Medicine’s magazine.

What Is Predictive Analytics in Healthcare?

There is some confusion and inaccurate perceptions about predictive analytics in healthcare. The field is not all about software tools that are commonly tied to predictive analytics found in a variety of industries.

In a report about healthcare predictive analytics from Rock Health, a company that provides seed funding to startups in digital health, it said that much of traditional medicine and healthcare work within predictive analytics. The only difference is that several years ago, physicians’ minds were predicting the unknown, based on their expertise and experience. Today, software tools are broadening the data set to encompass more data.

Predictive analytics in healthcare uses historical data to make predictions about the future, personalizing care to every individual. A person’s past medical history, demographic information and behaviors can be used in conjunction with healthcare professionals’ expertise and experience to predict the future. Software tools don’t define predictive analytics in healthcare — they represent the latest wave of technology to advance the field.

By all measures, the market is expected to thrive. The global predictive analytics in healthcare market garnered $2.20 billion in 2018, according to Allied Market Research, and it’s expected to grow to $8.46 billion by 2025, nearly quadrupling in size. The projected compound annual growth rate over that period is 21.2%.

How Predictive Analytics Are Used in Healthcare

There are several potential uses for predictive analytics in hospitals and other health environments. Here are a few examples of those types of solutions.

  • The University of Chicago Medical Center (UCMC) used real-time data to solve operating room delays that affected staff, patients and families. An algorithm helped each team with workflows by creating notifications and streamlining handoffs to the next team. As a result, turnover time decreased by 15-20%. This amounted to four minutes per room, according to the business intelligence company Dimensional Insight. The financial impact was estimated to be up to $600,000 Another result was that UCMC gained insight into what caused delays and what actions they could take in the moment to solve various situations.
  • Researchers and practitioners have developed ways to not only help providers react to sharp changes in a patient’s vitals, but also to predict an event before symptoms are evident. According to Xtelligent Healthcare Media, researchers have developed a predictive analytics tool that can identify patients on track for severe sepsis or septic shock 12 hours before the condition’s onset. In another example, practitioners have combined predictive analytics and clinical decision support tools to help reduce the mortality of sepsis by more than 50%.
  • Healthcare artificial intelligence is focusing on reducing self-harm and suicide attempts. As Intel AI explained at Forbes, machine learning can “read health records and identity with high probability the likelihood of future attempts.” Factors like pain medication prescriptions and the number of emergency room visits are used to help make these predictions. Another step to tools involved in self-harm and suicide is to automate intervention. By providing people who appear more likely to hurt themselves with sources of support prior to the next incident, we can reduce the number of people committing suicide.

Benefits of Using Predictive Analytics in Healthcare

Predictive analytics has the power to transform the healthcare industry. Its benefits encompass the quality and efficiency of patient care as well as the efficiency and effectiveness of healthcare staff and organizations. It also has positive implications for the profitability of hospitals and other healthcare organizations.

While there are challenges and pitfalls that researchers and practitioners need to keep in mind about predictive analytics in healthcare, there’s virtually no argument about the benefits it offers.

The market for those solutions is expected to increase four-fold in a seven-year span. Additionally, in a survey of more than 200 health payer and provider executives conducted by the Society of Actuaries (SOA), 93% said that predictive analytics is important to the future of their business. That figure is surprising since only 47% of them were using predictive analytics at the time. In 2017, 89% of executives either used or planned to implement predictive analytics within the subsequent five years.

The future looks bright for predictive analytics in healthcare, which is good news for professionals equipped with the knowledge and skills to perform data analysis and related tasks. In fact, in the SOA survey, executives ranked investing in those professionals with knowledge of predictive healthcare analytics highly. When describing the future of predictive analytics, survey respondents felt that this process could help in:

  • Refining data collection methods to increase security (20%)
  • Investing in staff with the expertise needed by the organization (18%)
  • Data visualization (17%)

Of course, all three of those statements require investing in data analytics professionals to make this possible. In healthcare as well as other data-driven industries like finance and marketing, there’s a strong demand for people who can analyze, visualize and work with big data to solve real-world problems. You can pursue a future in a rapidly growing field with an online data analytics degree or an online MBA. These programs from Husson University feature some of the lowest tuition rates in the market, and there’s no need to have a background in mathematics or programming.

Develop the skills and knowledge to thrive in any industry at Husson University. You’ll learn from dedicated professors who have years of experience in their fields in a convenient format that fits into your life.