We live in an increasingly digitized world where developers are using data to help companies gain valuable insights regarding their business. This information can be leveraged to determine flaws in an organization’s structure and operations, churning out smart solutions that reduce costs and increase revenue. This is especially true in the healthcare industry, where predictive analytics (PA) plays a key role in bolstering a patient’s treatment plan and reducing out-of-pocket costs.

Healthcare organizations are using predictive analytics technology for innovative solutions.

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Simply put, PA uses advanced technology and statistics to filter large swathes of data. The technology then analyzes this information in order to predict outcomes for patients and suggest the most effective treatments. With PA, developers have created applications that amass data from past treatment outcomes and the latest medical research.

In the healthcare industry, these predictions can include everything from how a patient may respond to a certain medication to the chances of readmission. The technology can predict infections from certain suturing methods, help physicians make a diagnosis, determine the likelihood of a disease based on a patient’s symptoms and predict future wellness. In short, the possible uses of predictive analytics for healthcare are virtually limitless.

Predictive Analytics Improves the Accuracy of a Diagnosis

One of the most effective ways PA can help healthcare organizations is by enhancing the diagnosis process to ensure higher accuracy. Using data from patient hospital records and wearables, predictive modeling creates an algorithm that determines a profile for an individual based on their vitals and performance over time. This is valuable for hospitals and clinics because it targets specific individuals and creates unique profiles for them instead of lumping illnesses into groups.

It’s important to note that this technology would not replace doctors, but it would help confirm a doctor’s diagnosis by answering questions regarding a patient and their condition. By relying on an accurate predictive algorithm that determines the chances of a patient being sent home safely, the doctor’s own medicinal wisdom would be aided greatly.

For example, there was a case in which a doctor had monitored a patient for years. After the doctor found the patient’s genome, PA technology suggested they could experience early onset Alzheimer’s disease. The doctor then had the patient exercise, develop good nutritional habits and play with mobile brain apps. The idea was to monitor the patient’s progress and use the app, which monitors diet and activities, to amass data on the patient.

As the patient progressed, this data was uploaded into the electronic medical record (EMR) that linked to the patient portal. This ultimately helped the patient and the doctor, as well as future patients at risk of Alzheimer’s, as this data can lead to a predictive model of an individual’s progress based on certain treatment courses. Over time, this data can be used to create a treatment course and gene therapy that will aid a patient’s condition and improve their diagnosis in the long run.

Using PA to Create Preventive Treatment Models

When medicine fails, it is often because healthcare has been involved in treating patients when they are already sick. After all, the chances of a patient receiving a positive prognosis after an illness has spread are lower since most medications are only fully effective during the early stages of a disease.

However, PA is making waves because data from a patient’s blood work, vitals and family history can be used to identify at-risk patients. Based on this data, doctors can recommend lifestyle changes for patients in order to help them reduce the risk of getting sick. While the main purpose of preventive medicine is to expand an individual’s lifespan and quality of life, it also reduces medical expenses for patients.

It’s conceivable that medications in the future will be designed using PA methods that will determine whether someone is at risk based on other patients with similar subtypes and genetic backgrounds. The technology can also go a long way in reducing the number of expenditures a healthcare organization is burdened with by increasing the efficiency of work processes.

PA Technology to Optimize Nurse Staffing

Hospitals and clinics face a slew of daily logistical problems that reduce the efficiency of an organization and its staff. Administrative tasks may seem minuscule on the surface, but without an organized and efficient system, nurses are unable to provide the best care for their patients.

The staffing of registered nurses can be a difficult task that bogs down the speed and integrity of an organization. However, PA could ameliorate this issue with advanced scheduling technology to effectively predict a patient’s demands and staffing needs. A recent survey by AMN Healthcare and Avantas found that 80% of nurses did not know PA was available for nurse scheduling, proving this technology has gone largely unused in the industry despite its potential.

Additionally, 94% of nurse managers say their organizations are understaffed due to scheduling and staffing problems that negatively affect staff morale. About 70% of these managers expressed concern regarding the impact this decreased morale has on patient satisfaction, while more than half said they were worried about its effect on quality of care.

Predictive analytics is growing in popularity due to its ability to smooth out the labor-intensive elements of a nurse’s job. The technology can amass data regarding the schedule of nurses, the needs of patients (such as the time of their medications and how frequently vitals need to be taken), and data that can be passed on to doctors. When you combine all these factors in one PA model, you can streamline an organization’s workflow and reduce the amount of time spent organizing nursing staff.

Predictive Analytics for Healthcare Makes for a More Efficient System

As technology continues to seep into every aspect of our lives, the role of predictive analytics in the healthcare industry will continue to expand. After all, PA does more than simply create a digital compilation of EMRs, as it contains clever algorithms that offer advanced work output solutions that aid in patient diagnosis, preventive care and organizing a nursing staff. With this technology, hospitals and patients can save time and money while doctors can save the lives of their patients by developing more efficient and effective treatment regimens.

If you’re hoping to enhance your healthcare organization’s work processes and improve patient care, you will need a developer with the experience to help you achieve your goals. The team at SevenTablets offers machine learning and predictive analytics services that will lend you a competitive edge. We’re also well-versed in other emerging technologies, including augmented reality, virtual reality, blockchain, artificial intelligence and natural language processing.

SevenTablets is headquartered in Dallas, but we also serve clients in Austin, Houston, and beyond. If you’re ready to discuss your project, we invite you to contact us today.


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Shane Long

Shane Long

President at SevenTablets
As President of SevenTablets, 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.
Shane Long