Big data has revolutionized how companies across all industries analyze, manage and interpret data. The far-reaching impact of big data on healthcare is seen in analytical techniques proving effective as tools to predict epidemics, reduce treatment costs, cut down on medical errors and improve the overall quality of medical care.
Big data is also proving to be an important tool for managing rising healthcare costs, which accounts for 17.6 percent of GDP in the United States, according to the management consultant firm McKinsey & Company.
Common Uses of Big Data Within Healthcare
A 2018 article in DataPine highlights popular uses of big data within the healthcare industry.
Staffing is one key area where technology can help medical staff predict how many health professionals need to be on hand on any given day. Citing a successful program in Paris, the article describes how four hospitals use data sets pulled from the previous 10 years to make hourly and daily predictions on staffing needs.
Improving access to health records benefits patients and doctors. Electronic health records (EHRs) are the most utilized application of big data in medicine. The records are personalized to patients and include data on demographics, allergies, medical history and lab results. The information is available through secure online platforms. The technology cuts down on paperwork and reduces the risk of human error due to handwriting misinterpretations.
Citing the McKinsey report, DataPine noted that, “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”
The United States is in the midst of an opioid crisis. Each day, an average of 116 people die as a result of opioid-related overdoses, according to the Health and Human Services Administration.
“Blue Cross Blue Shield have started working with analytics experts at Fuzzy Logix to tackle the problem,” according to DataPine. “Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify 742 risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids.”
Big data also aids in predicting the spread of epidemics. During the 2014 Ebola outbreak in West Africa, data from 150,000 area cell phones helped medical professionals decide where to establish clinics. A BBC article from the time detailed how Orange Telecom in Senegal passed voice and text data to a Swedish nonprofit involved in settling up treatment clinics.
How Big Data Is Revolutionizing Healthcare Management
The benefits go beyond improved treatment and shorter wait times. Healthcare administrators are using big data to increase hospital efficiency while cutting down on excessive costs.
Hospitals and medical clinics are prime targets for cyberattacks, meaning that advanced security techniques that can recognize changes in network traffic could be key to heading off cyberattacks. Analytics can also help streamline insurance claim processing, cutting down on wait times and helping caregivers receive pay more quickly.
The Centers for Medicare and Medicaid Services said they saved more than $210.7 million in prevented cases of fraud in one year, according to an article by MapR. Another MapR article describes how UnitedHealthcare is using a predictive analytics “factory” to cut down on inaccurate claims.
UnitedHealthcare’s “previous approach to managing more than one million claims every day (10 terabytes of data daily) was ad hoc, heavily rule-based and limited by data silos and a fragmented data environment,” the article said. “UnitedHealthcare came up with a unique dual model strategy, which meant focusing on operationalizing savings while at the same time pursuing innovation to constantly leverage the latest technologies. Hadoop (an open source software platform) is now the data framework for a single platform that’s equipped with tools to analyze a slew of information from claims, prescriptions, plan participants, contracted care providers and associated claim review outcomes.”
Big data is reshaping the healthcare industry, but an article in McKinsey highlights where possible setbacks may arise along the way.
“All stakeholders must recognize the value of big data and be willing to act on its insights, a fundamental mindset shift for many and one that may prove difficult to achieve,” the article said. “Patients will not benefit from research on exercise, for example, if they persist in their sedentary lifestyles. And physicians may not improve patient outcomes if they refuse to follow treatment protocols based on big data and instead rely solely on their own judgment.”
Big data also raises privacy concerns, the article said, necessitating a need for healthcare providers to provide secure online platforms for patient records. Finally, healthcare providers need to examine other technological revolutions for insights and potential problems.
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Sources:
BBC: Ebola, Can Big Data Analytics Help Contain Its Spread?
MapR: 5 Big Data Production Examples in Healthcare
Forbes: Big Data In Healthcare: Paris Hospitals Predict Admission Rates Using Machine Learning
McKinsey: The Big-Data Revolution in US Health Care
DataPine: 12 Examples of Big Data Analytics in Healthcare that Can Save People
Forbes: How Big Data Is Changing Healthcare
