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Why use healthcare analytics?

The Big Data Revolution in Medicine

Why use healthcare analytics?

Healthcare analytics improves healthcare. Collecting relevant data provides actionable insights into trends, best practices, and overall patient care. Let's break down some of the benefits:

• Lower risk - With tools analyzing data on patterns of success in healthcare, physicians are less likely to misdiagnose and mistreat patients. Healthcare analytics can also help the medical field to determine which patients are innately at higher risk for health complications, and to ensure that those patients receive the specific treatments that they need. All of these benefits mean that patients will receive better healthcare, putting them at lower risk for complications that mistreatment creates.

• Predicting trends - Analytics can find trends that may otherwise be undetectable. In addition to preexisting medical conditions, there can be many factors at play that contribute to how at risk a person is to various medical conditions. Analytics has revealed that factors like employment, education, and habits can contribute to a patient's health, how at risk they are for medical problems, and how to best address treatment. This application of healthcare analytics helps make patient care more effective for each patient.

• Scalability - Especially recently, as primary care physician and nurse shortages emerge, the industry is looking for ways to make physician work scalable. According to the United States Census, more than 26 million Americans do not have access to healthcare. Using analytics can show patterns of effectiveness to help find the most effective treatments for patients quickly. This means that physicians can spend less time on each patient without compromising the quality of care. Healthcare analytics also makes finding correct treatments easier, which means that some work traditionally done by a physician can be reliably reproduced by nurse practitioners.

• Lower cost - There is no doubt that healthcare is costly. According to Kent State University, healthcare costs account for 17.6 percent of America's GDP. In an article from Villanova University, the United States spends more than any other country on healthcare. However, healthcare doesn't need to be as nearly as costly as it is in practice. Physician errors result in thousands of medical malpractice lawsuits annually. Medical malpractice drives up costs not only for patients paying for incorrect treatment but also for the doctors and insurance companies that pay the settlements. Nationwide spending could be greatly reduced by using healthcare analytics to improve the accuracy of diagnoses.

The Big Data Revolution in Medicine

Along with healthcare analytics, healthcare business intelligence is emerging as a powerful tool for the healthcare industry. Healthcare business intelligence leverages software and data in order to make good business decisions surrounding healthcare. Over the past several years, there has been a surge in the use of software in the healthcare industry, from digitized records to wearable health trackers. Click here to check out how healthcare organization's use Explo.

The University of Illinois Chicago reports that 96 percent of hospitals use electronic health record technology. Even general practitioners are shifting in this direction. Between 2008 and 2015, the use of EHR technology among primary care physicians increased from 7 to 84 percent. Electronic health records keep each patient's medical data on record in a single file that physicians can amend each time they see the patient.

Machine learning, which depends on large aggregations of data, is beginning to play a major role in healthcare. IBM and its Watson Health Computer System have partnered with Mayo Clinic, CVS Health, and Memorial Sloan Kettering Cancer Center to develop machine learning initiatives for healthcare. The University of Illinois Chicago says that introduction of machine learning in healthcare could improve disease identification and medical imaging readings. Essentially, machine learning algorithms can identify similarities between symptoms or imaging patterns in patients with certain diagnoses in order to aid physicians. In particular, machine learning can assist in finding cardiovascular abnormalities, musculoskeletal injuries, and cancers. Another useful application of machine learning in medicine is robotic surgery. Machine learning can use information about successful surgeries in order to perform robotic surgeries that mitigate human error.

The rise in big data in healthcare is also seen in the growing popularity of wearable healthcare technology which consumers wear to record and collect their data. Often, this data can be sent to physicians in real-time. In addition to commonly used wearable healthcare technologies like fitness trackers and smartwatches, Business Insider reports that there are wearable ECG monitors that can measure an electrocardiogram and detect atrial fibrillation, and wearable blood pressure monitors that can measure blood pressure and daily activity. These technologies help the user by monitoring them and sending relevant information to their physician, and they also help scientists aggregate data on how personal habits influence health.

All in all, just as many other industries are, healthcare is turning more towards software and data to improve performance. And it's paying off. The McKinsey Global Institute predicts that the use of big data in health care could save more than $300 billion annually in the United States. If you're building a platform in the healthcare space, exposing data insights and metrics will be crucial to the value of your product. If you have any questions about using data in your healthcare tool, how Explo can help embed data analytics into your product, or just want to chat, feel free to reach out to me at natalie@explo.co.