With the advancements in technology, the healthcare industry can leverage modern predictive tools to capture, manage, and analyze patients’ health data on a large scale, and to help elderly and chronically ill patients to get optimum care at their doorstep. Predictive analytics is assisting doctors in offering better treatment to patients. Hospitals can now streamline their business operations and manage staff effectively.

According to a report by Markets and Markets, the global healthcare predictive analytics market is projected to reach a value of US$ 50.5 billion by the end of 2024 from US$ 14.0 billion in 2019. The report also suggests that the market shall witness a staggering 28.3% CAGR during the period.

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Also Read: It’s time to predict what the customers really want!

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Predictive analytics in healthcare can help doctors to be proactive instead of being reactive when a crisis occurs. Predictive analytics in healthcare proves to be beneficial in clinical care, administrative tasks, and operations. The technology has already delivered value in a multitude of healthcare settings, including small private practices, healthcare insurance companies, and some of the multi-national hospitals.

Use of predictive analytics in the healthcare industry is expected to become more prevalent. EHR (Electronic health records) systems already offer predictive analytics capabilities. Healthcare IT services are partnering with healthcare institutions to build proprietary systems designed to improve clinical care, administrative performance, and operational efficiency.

What is Predictive Analytics?

Predictive analytics is a branch of advanced analytics that predicts future events by analyzing historical data. Deep learning, statistical modelling, artificial intelligence, machine learning, and data mining are all used to analyze historical data and provide insights to healthcare leaders.

Healthcare predictive analytics has substantial scope in the future. So, let’s look at some of the most prominent use cases that determine the importance of predictive analytics in the healthcare industry.

Operations Optimization

Healthcare operations can be optimized with the use of predictive analytics. Let’s take an example of patients visiting a hospital or doctor’s office after making an appointment. What if many of them don’t show up? How is the schedule managed? How do you optimize doctor’s time? How do you streamline the operations?

It may seem impossible to track if a given patient is going to visit or not. But predictive analytics can help to sift through the patient’s data to understand the bigger picture. Perhaps most of the patients don’t have access to a car or stay very far from the hospital and hence couldn’t make it. This might help you to categorize patients by their likeliness of visit and make necessary changes to the schedule. It can also help healthcare leaders with decisions such as building a doctor’s office or another branch at a specific location where data shows patients cancelling due to non-proximity to the hospital.

Clinical predictions

Healthcare organizations and health insurance companies use predictive analytics to predict the likelihood of their patients developing certain medical conditions, such as diabetes, stroke, or cardiac problems. By leveraging data across the archives, such as EHR data, lab data, biometric data, and claims data, you can create models that can be applied to a group, community, or even an individual to predict the likelihood of a disease. Healthcare organizations use predictive analytics to identify if their patients need any intervention to avert diseases and improve health outcomes.

Predicting and Analyzing the Risk of Chronic Diseases, Outbreaks, and Pandemics

With the help of healthcare data, predicting the course of a disease has become quite a possibility. Predictive analytics has also been instrumental in predicting likelihood of certain chronic diseases such as cancer, diabetes, etc., and have helped healthcare professional in fighting them.

Researchers can use real-time and historical data to predict outbreaks and the spread of contagious diseases. This can help global health organizations to take appropriate measures to control the outbreak and minimize fatality.

Conclusion

Healthcare organizations can harness predictive analytics and turn existing data into a strategic, cohesive patient experience that helps them to streamline their operations and improve patient outcomes. Predictive Analytics has made it easier to understand the challenges to an excellent patient experience and build a stronger patient relationship that anticipates their needs.

The benefits of predictive analytics are continuing to accelerate, with AI and machine learning technologies enabling healthcare leaders to not only predict what, but also why it happened and how, to provide insights on the best course of action to take. Going forward, technology will continue to play a fundamental role in improving patient experience and health outcomes as healthcare leaders look to leverage the data at their fingertips and build a better healthcare ecosystem.