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Data-Driven Healthcare: How Analytics Is Revolutionizing Decision-Making

Data-Driven Healthcare: How Analytics Is Revolutionizing Decision-Making

Female health professional holds a tablet while reviewing medical data on a wall monitor.

It is estimated that the global healthcare analytics market share was $35.3 billion in 2022, with a projected compound annual growth rate (CAGR) of 21.4% from 2023-2030.1 This shows that the industry is not only a major factor within modern healthcare processes, but that even greater involvement is expected in the future.

The reason for the anticipated growth is that healthcare analytics is already transforming the way patients and clinicians receive and deliver care—and those insights will become sharper as the amount of data expands. Technological innovation will also enable greater amounts of data to be analyzed at once, and better software will make data both more available and secure. Those are just a few factors that will converge to improve patient care outcomes, reduce clinician workloads, streamline provider workflows and make the healthcare industry more efficient as a whole.

Here, we’ll examine the four different types of healthcare analytics and how each type is revolutionizing medicine as we know it. We’ll also explain which technological factors serve as both opportunities and barriers to the progress of the industry, as well as how Milwaukee School of Engineering can position you to contribute to the field.

The Four Types of Healthcare Analytics

Analytics, which is the process of collecting, cleaning and evaluating data to provide actionable insights into real-world scenarios, and then presenting those findings to others, turns bytes of information into useful knowledge. When applied to healthcare, it can give clinicians a clearer picture of their patients’ conditions, improve patient satisfaction and lighten the load of healthcare providers by making their processes work more efficiently—and those are just a few benefits it provides.

There are four different types of healthcare analytics—descriptive, diagnostic, predictive and prescriptive—and all of them are critical for improving the healthcare industry. These examples illustrate the differences between them, as well as how analytics can transform the industry.2

Descriptive Analytics

Sometimes considered the most basic of the four, descriptive analytics seeks to understand the current status of the environment being analyzed. It answers the “what” of a healthcare scenario and is the first phase of the four types.3

An example of descriptive analytics might include gathering hospital admissions data and nurse census levels to determine nurse-patient ratios within a facility. During the COVID-19 pandemic, the CDC created tracking forms that clinicians could fill out and submit to identify when a patient tested positive for the virus, giving an understanding of its spread.4 This type of data analysis is descriptive because it does not attempt to answer any questions about why nurse-patient ratios are at existing levels; it just describes what they are.

Diagnostic Analytics

This second phase of healthcare analytics seeks to understand how the status of the current situation was reached. It answers the “why” of a healthcare scenario and informs decision-makers of how they reached the scenario they're seeking to address.3

As an example, clinical data regarding a patient’s condition may be gathered to determine the patient’s diagnosis, which in turn can be used to identify the reason they’ve been hospitalized. Repeating the process over the course of a single patient’s history can reveal underlying health issues that were previously unknown, while applying the same data collection methods to a larger population may help identify systemic health concerns, especially among certain demographics. This is the case when insurance enrollment rates and quality-of-care metrics are assessed within low-income individuals.5

Predictive Analytics

By seeking to understand the most likely outcome of a current situation, predictive analytics asks, “What next?”3 This phase is heavily dependent on the findings of diagnostic analytics and can be useful not just for improving health outcomes, but for making healthcare processes more efficient as well.

For example, continuous glucose monitoring systems deliver data regarding a patient’s blood sugar levels to a clinician in real time, eventually revealing trends that help them predict when the next health event will occur.6 Hospital administrators can also evaluate previous years’ admissions rates during certain seasons so that they can hire enough staff to avoid a personnel shortage.

As a student in MSOE’s online MBA in Analytics program, you’ll have the opportunity to study predictive analytics in depth. In your predictive analytics course, you will learn to identify appropriate tools to address decision-making scenarios within an organization with special attention being paid to the application of analytics to predict future trends and probabilities. This course also places an ongoing focus on effectively communicating analytical results to a range of audiences. Check out the program curriculum to learn more about what you’ll study and practice as an online MBA student.

Prescriptive Analytics

The final phase of healthcare analytics, prescriptive analytics, aims to understand what the optimal response to a current situation should be according to a given set of inputs. The ultimate aim of healthcare analytics is to provide meaningful solutions for real-world scenarios, and by seeking to answer the question “What should we do about it?,” prescriptive analytics achieves that end.3

In the example above of continuous glucose monitoring systems, it would not be enough to simply anticipate the next blood sugar event based on the patient’s history. The goal is for clinicians to be able to prescribe a medication or suggest a lifestyle change that would stabilize the patient’s blood glucose levels overall. Prescriptive analytics can help achieve this end not only by predicting future outcomes, but by analyzing the patient’s response to given medications as well.

Healthcare Analytics: Opportunities, Hurdles and MSOE

Due to innovation in big data, connectivity, the web, sensor technology and a host of other factors, the amount of healthcare data expected to be available in the future is exponentially greater than the already staggering amount that analysts currently have on hand. The number of factors contributing to the rise of healthcare analytics in the future is another topic in itself, but clearly, the opportunities for innovations give the field enormous potential.

MSOE partners with industry leaders in the fields of healthcare analytics and other cutting-edge disciplines to give you the education you deserve. The online MBA program, specifically the online MBA in Analytics, is designed to ensure that you’ll be able to perform your work in analytics with excellence in any field you wish to pursue, including healthcare and manufacturing. Take advantage of MSOE’s strong connections with partners at the intersection of business and tech, including Milwaukee Tool, JCI, Generac and more.

Connect with admissions outreach advisor and start your journey into this ever-expanding field. Schedule a call.

Admissions Dates and Deadlines

Application Deadline
January 2
Spring 2024
Term Start
January 16
Spring 2024

Milwaukee School of Engineering has engaged Everspring, a leading provider of education and technology services, to support select aspects of program delivery.