Why is Healthcare Data Analytics Important?

Big Data Saves Lives and Allows Hospitals to Prepare for the Worst

May 09, 2022
Two physicians in lab coats reviewing healthcare data on a computer screen
Healthcare data helps physicians make important patient care decisions

For those of you who have been reading our coverage of data in the healthcare world, it’s no surprise that we put a premium on data analytics in healthcare. Healthcare data analytics encompasses both macro and micro trends in the healthcare field, whether it’s using data to gauge the spread of a disease or aiding a clinician in detecting an anomaly in a cancer scan.

Like all data analytics fields, the term refers to the use of large amounts of data to give organizations or professionals actionable insights, applied here to the healthcare field. As healthcare spending continues to ramp up, cost saving measures from healthcare data analytics offer hospitals and healthcare systems an easy way to cut costs while improving outcomes.

Given the trillions spent on healthcare worldwide, it’s no surprise that “by 2025 the market for health-related analytics will increase to about $28 billion” (Healthworld.com).

Types of Data Analytics

Descriptive Analysis: This is the most basic of all analysis. This examines an event that happened in the past. For example, a healthcare data analyst might track data at a hospital over the past five years to look for seasonal patient admission trends. 

Diagnostic Analysis: This type of analysis is used to investigate why an event happened. For example, a healthcare provider might ask “Why is there an increase in patient dissatisfaction this month in our post-visit surveys?” 

Predictive Analysis: This form of analysis is used to forecast something that will happen in the future. For example, a hospital might predict, based on trends observed over the past decade, that incoming cardiac patients will most likely increase by 20% this year.

Prescriptive Analysis: This is possibly the most important form of analysis in healthcare and the trend that is growing quickest. This form of analysis takes pre-existing data and implements treatment plans. For example, a healthcare provider might use a smart device to automatically analyze a patient’s vital signs, preemptively alert them that they’re at risk for developing a medical condition, and instruct them to visit their healthcare provider. 

How Data Analytics is Used in Health Care

Below are three examples of how healthcare data analytics have affected the healthcare industry. 

Predicting Hospital Usage

If there’s one thing we learned from the COVID-19 pandemic it’s that there are a finite amount of hospital beds. While the pandemic might be considered a black swan event, being able to predict hospital bed usage is vital for any working healthcare system. In a form of predictive analysis, French hospitals used an analytics program created by Intel to predict emergency room visits and hospital admissions. Using a number of time series analysis algorithms, the team managed to create a browser-based interface that allowed doctors to predict admission rates by considering a variety of factors like flu season and heat waves.
“Seeing the prediction application take advantage of all the data and provide useful and actionable insights has allowed our medical staff to imagine the tremendous benefit it will provide to both the staff and our patients,” said Dr. Sébastien Beaune, emergency department director at one of the hospitals. “Having a better understanding of patient flows at our emergency departments—or even predicting these flows—is absolutely key if we want to improve our quality of care.” (Intel.com , DataPine)

Assessing Co-morbidities

Taking inspiration from the famous poem by John Donne, we can say that no disease is an island unto itself. Not only do clinicians have to treat the disease a patient is suffering from, but they need to be aware of other co-morbidities patients may have, especially diseases with wide-ranging effects like type 2 diabetes. Here again, data analytics can be of use.

Adam Wilcox, director of the Center for Applied Clinical Informatics at Washington University School of Medicine and a member of the American Medical Informatics Association, believes that the next frontier for healthcare data analytics will be in this critical juncture. Data analytics allows doctors to find out which patients are most likely to develop severe complications and those that are most at-risk of developing sepsis from longer hospital stays. Plus being able to calculate risks involved in co-morbidities will allow doctors to slow disease progression before things become that much harder to tackle (TechTarget).

Making Sure Patients Show Up

This one might seem like a no-brainer, but you’d be surprised to know how many adverse medical outcomes could be avoided simply if patients made it to their doctor’s appointments. (Not to mention how much money hospital systems lose when a patient doesn’t show up!) The American Journal of Roentgenology reported how one hospital system used a combination of artificial intelligence, predictive analysis and a simple reminder system to fix an alarmingly high MRI no-show rate. After the intervention, the hospital witnessed a 17% increase in attendance (American Journal of Roentgenology).

The Future of Healthcare

There’s no question that data analytics will totally transform the healthcare industry in the decades to come. We’re already seeing the beginning of a new era where analytical tools, IoT devices and AI technology are all helping to make health care more efficient and save lives. If you’re interested in a starting a career in the world of healthcare data analytics, our master’s program at Touro College Illinois is the perfect place to start. The Data Analytics Master's Degree in Healthcare will give you the tools and knowledge to make an impact in the health care industry.