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The Role of Predictive Analytics in Healthcare and Insurance

Read Time: 4 minutes

Predictive analytics (PA) is transforming virtually every industry, from sports and banking to healthcare. This technology is essential in advancing the healthcare industry, as it offers a full-fledged idea of what a patient’s medical history looks like and compares it to data from patients with similar histories.

The biggest benefit of integrating predictive analytics into hospital servers is improved patient outcomes coupled with reduced operating costs. An advanced system automates some of the guesswork from health experts, speeding up treatment without having multiple specialists see a patient. The technology also helps bring patients back to the hospital where they previously received care since a more efficient care journey will prevent them from ceasing to seek treatment or going elsewhere. However, hospitals still save on insurance premiums and patients spend less because these advanced processes reduce readmission rates.

In short, having an efficient PA platform transforms medical wisdom into real-world results that benefit both patients and healthcare organizations. Adopting healthcare predictive analytics helps companies move into a new age of medicine with machine learning software that is integrated to help chronic disease management, improve hospital care and enhance supply chain processes.

A Predictive Analytics Platform That Considers All Variables

One of the key elements of a successful healthcare predictive analytics platform is scalable analytics that integrates and make sense of all the data from a hospital’s servers. This will help physicians determine the best treatment plans based on their patients’ information and background. Such a platform essentially serves as a tool that, combined with a doctor’s insight, gives them a clearer idea of what a patient is going through. This information includes patients’ vitals, blood work, medical history and biographical information.

For example, a solid predictive analytics platform would examine a patient with a newly diagnosed condition and consider their age bracket, race, gender, allergies, medical history and more. The software would then combine this data with the patient’s existing conditions and medications in order to find other patients with similar histories. Physicians would analyze this aggregation of data to discover the most fitting treatment option.

This tool is of great benefit for doctors who would like their treatment suggestions reinforced or to show physicians an angle of their patient’s condition they did not previously consider. A successful platform would operate at a higher accuracy level than current medical solutions, ultimately reducing patient costs and hospital operational costs while improving a patient’s outcome as quickly and effectively as possible.

Machine Learning’s Role in Improving Predictive Analytics

Machine learning refers to a field of data analysis that gives devices and computers the ability to learn patterns through the data it is given and find business solutions with this data. In the healthcare industry, machine learning aids hospitals and clinics by learning vast arrays of data about patients and categorizing it based on factors such as age, race, medical history, allergies and beyond. These networks are then leveraged to determine the best care for each patient by comparing their data with that of similar patients.

Recently, PA models have been using machine-learning techniques that find new patterns in data. For example, Mercy partnered with Ayasdi, an analytics company that integrates machine learning with algorithms that help generate patterns in big data. The added visual aspect of the company’s search functionalities helped analysts identify data from clusters that can be used to improve healthcare solutions. Mercy first used the technology to understand what influences hospital length of stay after joint replacement surgery.

The algorithms examined patients with a shorter length of stay, discovering that some patients were using pregabalin, a neuropathic pain reliever, after surgery. The patients who were treated with pregabalin used less opioid pain reliever and were ambulatory sooner. Without this technology, determining the best course of action for patients with joint replacement surgery would have been much tougher.

Mercy now has an automated system of 84 care paths that cover 80% of the care within the system using machine learning algorithms. These solutions evaluate data in real-time, measuring whether the technology is improving patients’ conditions. The company notes that the key is to understand the difference between target and reality for each care path and make changes in real-time according to how their situation is progressing. This advanced predictive technique reduces hospital costs and improves care quality.

The Future of Predictive Analytics in Healthcare

Healthcare predictive analytics platforms are still undergoing changes, as they require human intervention to accurately assess a patient’s condition and their potential care route. For example, a platform that examines a patient with high blood pressure may recommend a reduction in overall blood pressure, but a physician still needs to use their knowledge to determine the best course of action. While there are some variables that machine learning may not be equipped to handle yet, the field is advancing at a swift rate and any wrinkles should soon be ironed out as the medical world more widely accepts predictive analytics.  

Machine learning is at the forefront of predictive analytics modeling, as data analysts have developed deep learning neural networks capable of compiling and processing large amounts of data in order to reduce readmission numbers and improve a patient’s outcome. The technology is already bearing fruit for organizations such as Mercy, which is helping patients live longer, feel less pain and pay less for their care. As such, it looks like there is an exciting and promising future for the role of predictive analytics in healthcare.

If you’re interested in integrating predictive analytics into your company, you will need an experienced development team to help you meet your goals. Here at 7T, we offer predictive analytics solutions for businesses and have experience with the healthcare sector specifically. We’re also well-versed in other emerging technologies, including augmented reality, virtual reality, blockchain, artificial intelligence and natural language processing.

7T is based in Dallas, but we also serve clients in Austin, Houston and beyond. If you’re ready to get started on your development project, we invite you to contact us today.

Reach out to our team today!

Shane Long

As COO and President of 7T, Shane Long brings experience in mobility that pre-dates the term “smartphone” and the release of the first iPhone. His work has helped revolutionize the growth of mobility by bringing to market one of the first graphics processors used in mobile phones, technology that after being acquired by Qualcomm lived well into the 4th generation of smartphones, as well as helped pioneer the first GPS implementations in the segment. With a strong engineering and business background, Shane understands how the rise of mobility and Predictive Analytics is crucial to greater business strategies geared toward attaining competitive advantage, accelerating revenue, and realizing new efficiencies. As the leader of a B2B mobility solutions provider, he partners with business leaders including marketers and product developers to leverage enterprise mobile applications, big data and analytics, and mobile strategy.

Shane earned a B.S. at Texas A&M (whoop!) and studied mathematics as a graduate student at Southern Methodist University.


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