Business Intelligence in Healthcare

Healthcare spending in the United States is booming. At approximately $8,508 per person in 2013, the U.S. ranks as the top nation in the world in healthcare expenditures. To help ease the burgeoning cost on the industry, professionals have started to speculate about the potential role data analytics and business intelligence (BI) could play in managing healthcare expenditures. Other diverse industries, such as retail and law enforcement have already seen success utilizing these tools, suggesting a positive outcome for the healthcare industry. In addition to cost management, BI could also improve patient outcomes at the same time – an encouraging advantage.

Applications of Business Intelligence in Healthcare

Business intelligence can help healthcare providers gain the insight they need to reduce costs, increase revenue and improve patient safety and outcomes while complying with regulations and standards. One of the ways in which healthcare providers can benefit from BI is by gaining more visibility into their financial operations, including identifying both highly profitable and underutilized services, monitoring cash flow and generating compliance reporting.

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BI also can improve patient care and facilitate quality performance and safety analyses. By providing a foundation for evidence-based clinical decision-making, BI can help improve patient outcomes and enable physicians to better monitor and forecast patient diagnoses.

Operational performance, as well as claims and clinical analyses are other areas in which BI can possibly help lower costs. With business intelligence, providers can optimize pricing, streamline the claims process, control costs and improve operational efficiency. BI also can provide insight into the effectiveness of marketing efforts.

Big Data Healthcare Revolution

In healthcare, business intelligence solutions rely on big data. This is due to the ever-growing volume of digital information that healthcare providers and those in related industries, such as pharmaceutical professionals and insurance companies, can generate. The federal government and other sources have made clinical trial and insurance data available to the public. In addition, electronic health records have gained widespread implementation. Paired with advances in technology, it’s now easier than ever to amass and analyze patient data from multiple sources, such as hospitals, private providers and laboratories.

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As healthcare costs continue to rise, physicians are facing pressure from payors to reduce costs while optimizing patient outcomes. Some insurance companies are moving from a reimbursement plan based on a fee-for-service model to one that reimburses physicians, pharmaceutical companies and others based on whether or not treatments deliver the desired results. In this environment, it benefits all parties to compile and share information.

According to a recent report from the McKinsey Global Institute, applying big data to predict U.S. healthcare needs and enhance efficiency and quality could save between $300 and $450 billion annually.

Trends in Healthcare and Big Data

The increase in volume of data is one of the most significant trends in healthcare. Analysts at the McKinsey Global Institute predict that the average hospital will be closing in on having a petabyte of patient data by 2015 and most of this data will be unstructured, such as radiology and imaging scans. This massive volume of data, coupled with the challenges of storing and sharing unstructured data, will likely lead to the implementation of patient data warehouses at most hospitals. By providing a solution to these data challenges, data warehouses are expected to reduce the number of unnecessary or repeated tests and treatments.

Personalized medicine is another growing trend. As individual patient data becomes more accessible and the means to analyze it become easier, treatment protocols will move from a one-size-fits-all model to treatments based on each patient’s unique medical history and current medical issues. Analysis of genetic markers also will increase, allowing physicians to step in earlier to prevent disease or reduce its impact on patients. They also will be able to more precisely target treatment for diseases that are expensive to treat.

Prevention is another trend that is on the rise. By using big data, physicians can develop a better insight on patterns of factors, both genetic and behavioral, that increase patients’ risk of disease. Using this information, physicians can then recommend medications or guide patients to make lifestyle changes to reduce their overall risk of disease. Disease prevention represents a huge potential cost savings of $70 to $100 billion, according to the McKinsey Global Institute.

Big data can play a key role in managing more than patient treatment. Hospitals are also looking to big data in order to manage logistics such as patient throughput, improve patient flow in triage and make better predictions based on facility population level. Using big data for these types of analyses, hospitals would know optimal patient discharge times to make best use of bed space without sacrificing patient outcome. Physicians could more accurately prioritize and treat patients in emergency and trauma cases and generally improve patient outcomes while reducing costs by providing the right treatment at the right time.

There is no question that business intelligence and big data will play major roles in the future of healthcare. They carry the potential to positively impact all healthcare stakeholders from patients to clinical staff to leadership.