Although the cost-saving benefits of the Affordable Care Act may not be realized anytime soon, some new tools and strategies are being employed to help bring down the costs of healthcare now. And Big Data analytics is one of those tools. Enabling healthcare professionals to capture and analyze mountains of digitized healthcare data to obtain new insights, Big Data cloud platforms are ushering in a new era of high-quality patient care at lower costs. According to a recent report by McKinsey & Company, in the future Big Data could save Americans $450 billion annually. Part of that future has already arrived. Heres a look at 5 big ways Big Data is reducing healthcare costs today.
- Faster time to treatment With todays huge patient loads, treating patients sooner saves both lives and healthcare costs. For physicians, delivering a fast and accurate diagnosis and treatment requires them to make informed decisions quickly. Big data analytics tools help expedite the process by factoring in unique circumstances, such as lifestyle choices and demographics, along with the patients symptoms to help physicians make a more accurate diagnosis and formulate the best treatment regimen in real-time.
- Reduced hospitalizations and readmissions One of the best ways to curb healthcare costs is to keep patients from entering the hospital in the first place. Using new data tools that send automatic alerts when patients are due for immunizations or lab work, more and more physicians are able to reduce hospitalizations by practicing better preventive care. Additionally, new sensor devices now being used by patients at home and on the go deliver constant streams of data that can be monitored and analyzed in real-time to help patients avoid hospitalization by self-managing their conditions. For hospitalized patients, physicians can use predictive analytics to optimize outcomes and reduce readmissions. Parkland Hospital in Dallas Texas has been using analytics and predictive modeling to identify high-risk patients in their coronary care unit and predict likely outcomes once patients are sent home. As a result, Parkland has reduced 30-day readmissions back to Parkland and all area hospitals for Medicare patients with heart failure by 31 percent. For Parkland, that represents an estimated savings of $500,000 annually, not to mention the savings patients realize by avoiding readmission.
- Improved physician performance The ability to capture and analyze physician data is having a major impact in optimizing patient outcomes while reducing healthcare costs. One case in point is Memorial Care—a six-hospital system based in Fountain Valley, CA—where physician performance analytics are currently being used to track the performance of both hospital doctors and outpatient providers. So far the results are impressive. According to MemorialCare, doctor performance tracking has reduced the average cost per adult patient by $280, resulting in an annual savings to the 6-hospital system of $13.8 million. Just as important, those savings reflect a reduction in readmissions, mortality and complications attributed to physicians delivering a higher standard of patient care.
- Risk stratification This data analytics tool helps hospitals track and identify the sickest—and often the costliest—patients in a proactive way. But the beauty of this predictive tool is that, along with symptoms, it brings other patient risk factors such as missed doctor appointments and poor blood sugar control, into the mix. When these potentially costly patients are flagged and categorized or stratified as to risk, physicians can target higher-risk patients and intervene early on to prevent more drastic and costly hospitalizations and treatments down the road.
- Improved medication therapy management (MTM) Adverse drug events plague todays healthcare system to the tune of thousands of patient deaths and hundreds of billions of dollars in expenses. Much of the problem stems from physicians being overwhelmed with mountains of patient data that must be evaluated correctly in order to implement optimal drug therapies. Clinical pharmacists, whose role it is to monitor and manage drug therapies, are also burdened as more and more patients are taking multiple medications. Fortunately, Big Data cloud analytics is helping clinicians and clinical pharmacists to better co-manage drug therapies by identifying drug interactions, adverse side effects and additive toxicities, all in real-time. While playing a vital role in reducing patient deaths, better MTM reduces healthcare costs through fewer doctor and emergency room visits, hospitalizations, and readmissions.
As healthcare continues to embrace Big Data, the predictions of McKinsey & Company concerning Big Datas future role in reducing healthcare costs are sure to be realized. Ultimately, Big Data could usher in a new era of healthcare by giving patients the information and tools they can use to be more proactive in monitoring and managing their own health and well being.
