There is no way to understate the role that new technologies will play in healthcare in the coming years.
Artificial intelligence (AI), connected devices, advanced robotics and countless other developments have left the industry almost unrecognizable from even a decade ago — and it’s almost certain that changes will happen even faster in the years to come.
But if there’s a connecting thread between the majority of these technologies, it’s this: They allow us to learn from the past as a means to better inform the future.
This is where predictive analytics becomes so critical. Healthcare providers are sitting on a mountain of data — about their patients, staff members, efficiencies and shortcomings — which predictive tech can use to make strong, confident assumptions about the best path forward.
The advancements in predictive analytics come at a much-needed time, when providers face unique challenges in terms of both care and administration. That’s not to mention costs, which are expected to exceed $18.3 trillion industry-wide by the year 2030.
Below, we’ve broken down some of the most exciting ways healthcare organizations can harness this tech to tackle each of those problems — and several more.
Improving patient care
Every healthcare organization’s No. 1 priority is to provide the best possible care for its patients. Predictive analytics promises to help providers accomplish this while simultaneously cutting several of the costs associated with a poor patient experience.
For one, organizations can use predictive modeling to analyze past patient data and predict which patients are the most at risk for complex, worrisome issues like diabetes, cancer and heart disease.
That’s exactly what Penn Medicine did with help from Intel. By studying prior data, the hospital massively improved its ability to predict both sepsis and heart failure cases — two of its most costly and commonly seen issues.
The result, which included correctly predicting 85% of sepsis cases, led to better patient outcomes and, by helping staff treat those cases earlier, allowed the hospital to use its resources much more efficiently.
Doctors found similar success in the early days of the COVID-19 pandemic. By using predictive analytics to identify at-risk patients, hospitals were able to essentially halve the virus’s mortality rate in a six-month span from April to October of 2020.
It’s clear that the benefits are quite literally life-saving, giving providers the ability to triage patients appropriately and dedicate resources where they’re needed most.
Adapting to patients’ needs
Providers can also use analytics to craft more specialized care models — ones that simultaneously cater to each patient’s specific needs and aim to prevent chronic health issues.
This approach can help reduce readmission rate, which, when high, is a huge financial burden for providers. As it stands now, roughly 14% of patients are readmitted within 30 days, and those readmissions cost providers an average of $15,200.
Not only is a high readmission rate costly, but it may also indicate that patients aren’t receiving the best care possible, which in turn leads to worse outcomes and lower patient satisfaction.
This is where providers can use predictive modeling to craft bespoke, prevention-based plans for each and every patient. This can be done by analyzing demographic data — predicting potential risk factors based on age, family history, geography and more — but it can also be done on a patient-by-patient basis.
Thanks to the popularity of wearable health trackers like Apple Watches, Fitbits and Oura Rings, providers can now access a mountain of real-time data about a person’s overall health. Considering 59% of Americans now use a health wearable — and the majority of them wear their devices every day — it’s a wasted opportunity for healthcare organizations to ignore this highly accessible information.
In both cases, providers can use predictive analytics to identify the biggest needs and potential issues their patients are facing, crafting specialized health plans that lead to fewer readmissions and better outcomes in general.
Identifying high-cost areas
Predictive analytics can also help businesses identify high-cost areas for improvement. In healthcare, routine analyses of this kind are especially critical, as any inefficiencies could lead not only to lost revenue but also to increased risks for patients.
Perhaps the biggest benefits can be seen in administration, where providers can use analytics to identify inefficient practices, staffing issues, equipment needs and more. Administrative costs are the largest source of wasted money in the healthcare business, meaning there’s plenty of room for improvement.
Zooming in, predictive analytics can also improve the documentation process, helping staff members prioritize their work and identify the most important details to include. In billing, this can help expedite the payment process and identify fraud, a crime that costs providers around $455 billion a year.
Automating key processes
When combined with AI, predictive analytics can even help providers automate full aspects of administration. In patient-facing services like scheduling and billing, the applications here are obvious, with analytics helping AI-based software provide the best options for appointment updates, medical history changes and much more.
In the years to come, it seems that this combination of tools may also play a role in patient record-keeping, with several AI note-taking programs showing promise in the medical sector. However, many of these options also come with privacy and reliability issues, so it’s clear that much more development is needed before the technology goes mainstream.
Other benefits
Beyond the most obvious benefits — which focus mainly on cutting costs and improving the patient experience — there are other ways predictive analytics will likely shape the healthcare industry. These aspects may not be as relevant for healthcare management in the short term, but they’re worth being aware of given their tremendous potential upside.
Predictive analytics is already playing a role in drug trials, by reviewing past data to suggest treatment options that might otherwise take years to discover. The same is true, of course, for new technologies and medical devices, which could soon be tested in a much more efficient and informed manner.
Ultimately, the hope is that these analytics can help improve the potential outcomes of treatments in general, diving into massive datasets to match specific solutions and patients in a hyper-specific way that we’ve never seen before. Like with all possibilities around predictive analytics, the ceiling is high.

Joseph Muscente
Joseph is a Content Marketing Analyst at LendingTree where he works to empower people to make their best financial decisions. He earned his B.A. from Penn State University.