The Importance of Data Analytics in Healthcare

What Is Healthcare Analytics and Why Does It Matter?

As the average human lifespan increases along with the global population data analytics in healthcare is poised to make a large difference in modern treatment. The use of healthcare analytics can potentially reduce the cost of treatment, predict disease outbreaks, circumvent preventable illnesses and generally improve the quality of care and life of patients.

Big data essentially takes vast quantities of information, digitizes it and then consolidates and analyzes it with specific technologies. The saying “an ounce of prevention is worth a pound of cure” is incredibly relevant to healthcare analytics as it can help doctors learn more about patients earlier in their lives, providing early warning signs of diseases and treating illnesses at their initial stages.

After nearly 20 years of steady increases, healthcare costs accounted for 17.6 percent of the GDP in 2018, nearly $600 billion more than the expected benchmark for a nation with the size and wealth of the United States.¹ With costs exceeding predictions, the healthcare industry needs data-driven solutions. These solutions benefit the healthcare providers as well as the economy. As more caregivers are paid based on patient outcomes, there is a financial incentive to cut costs for insurance companies while also improving patients’ lives. And since physicians’ decisions are more frequently based on evidence, research and clinical data provided by healthcare analytics is in much higher demand.

Examples of Data Analytics in Healthcare

With data analytics in healthcare, it can become easier to gather medical data and convert it into relevant and helpful insights, which can then be used to provide better care. Below are some examples of how healthcare analytics can be used to foresee problems and prevent them before it’s too late, as well as evaluate current methods, actively involve patients in their own healthcare, speed up and improve treatment, and track inventory more efficiently.

• Staffing: Finding the perfect balance so that managers don’t overstaff and lose money, or understaff and have poor patient outcomes, can be remedied by healthcare analytics. Hospitals in Paris used big data to analyze 10 years' worth of hospital admissions records, allowing them to find patterns and forecast visit and admission rates 15 days in advance. This enabled them to preemptively schedule extra staff when a high number of patients were expected, which led to reduced wait times and a higher quality of care for their patients.
• Electronic health records (EHRs): This is the most common application of big data in healthcare in the U.S. 94 percent of hospitals have adopted EHRs.² Each patient has their own digital health record which includes everything from allergies to demographic information. Every record is made up of a single adjustable file, allowing doctors to complete changes through the years with no paperwork and no risk of repeating data.Kaiser Permanente's fully implemented computer system, HealthConnect, ensures data exchange across all of its medical facilities by utilizing EHRs. This system not only improved outcomes in cardiovascular disease, but it also helped save an estimated $1 billion by decreasing office visits and lab tests.¹
• Enhancing patient engagement: Many potential patients are also consumers who are already utilizing smart devices that double as wearable health devices that track their steps, heart rates, hydration levels, sleep patterns, etc. Patients are directly involved in monitoring their health, and incentives from health insurance companies can motivate them to lead a healthier lifestyle.Patients who utilize wearable health devices can track specific health trends and upload them to the cloud where their physicians can observe them. Patients with asthma or high blood pressure may be able to reduce visits to the doctor and gain more independence with these devices.
• Preventing Opioid Abuse: According to the Center for Disease Control (CDC), on average, 130 Americans die every day from an opioid overdose.³ In fact, in 2017 opioid overdose became the most common cause of accidental death in the U.S., overtaking road accidents. The application of healthcare analytics can help solve this critical problem. Blue Cross Blue Shield started working with analytics experts to analyze years of insurance and pharmacy data. They were able to identify 742 risk factors that predict with high accuracy whether or not someone is at risk for opioid abuse.⁴

• Predictive Analytics: Predictive analytics is one of the biggest business intelligence trends, but their potential reaches much farther than just the business field. Optum Labs, a U.S. health services innovation company, collected the EHRs of more than 30 million patients to create a database for predictive analytics tools that will help enhance and streamline the delivery of patient care. Their goal is to use data and advanced analytics to achieve the "Triple Aim of improved outcomes, reduced costs and an improved patient experience." They help doctors make data-driven decisions within seconds, and can even predict and prevent the onset or progression of some conditions.⁵

Lead the Conversation

Explore how data analytics in healthcare is transforming the industry and the way patient care is delivered, and consider how an online Master of Science or Postbaccalaureate Certificate in Health Informatics could help you lead new conversations about how to improve patient experiences and outcomes through technological advancements.