Have you ever wondered whether a specific demographic of patients might be more at risk for a certain health condition? Or maybe you’d like a way to recognize patient utilization patterns and identify trends. Predictive analytics in healthcare can help you do that and much more.

The use of healthcare predictive analytics is growing at an incredible rate, and because this technology leads to improved patient outcomes, it’s easy to see why. Below, medical professionals will learn how predictive analytics in healthcare works and get actionable insights into implementing predictive analytics in healthcare institutions.

Understanding Predictive Analytics in Healthcare

Predictive analytics in healthcare, sometimes called data analytics, relies on predictive models featuring artificial intelligence and machine learning. These predictive analytics tools analyze historical healthcare data using a process called data mining.

Through predictive modeling, healthcare professionals can pinpoint trends in patient care. This allows them to identify patients with health risks and improve health outcomes based on patient data.

Predictive analytics in healthcare helps healthcare organizations deliver the right care to patients at the right time. Healthcare providers may also find it useful for the management of chronically ill patients. If you’re interested in a system that provides real-time data, reduces the burden of healthcare costs, and can help you improve patient outcomes, it pays to understand how healthcare predictive analytics works.

Predictive analytics in healthcare wasn’t always so sophisticated. Its origins can be traced back to the 1950s when those in the healthcare industry kept all records on paper. It’s hard to imagine for those of us in the digital age.

In the 70s, medical diagnostic decision support (MDDS) systems became available in academic research centers. Later on, these systems became more advanced as they spread throughout the U.S. healthcare system.

By the 90s, interest in healthcare analytics had skyrocketed. One 90s study found that an artificial neural network outperformed doctors in diagnosing heart attacks using electrocardiograms (EKG). Since then, analytics in healthcare has spread globally and has been improving patient outcomes, early interventions, chronic disease management, and healthcare delivery for people worldwide.

Key Components of Predictive Analytics

Predictive analytics in healthcare requires a couple of components to process patient data. The first is the data itself.

Big data and the field of data science are fascinating, but if you don’t care to learn about topics such as predictive algorithms and logistic regression, try this: Think of data as a library from which a predictive algorithm gets its information. Predictive models then train themselves on this data. By doing so, they can improve patient safety and early intervention for patients in poor health.

This system wouldn’t work without machine learning. Unlike the computers of yesteryear, machine learning models can make decisions for themselves. They can filter out useless noise, for instance, and solely focus on data impacting future events regarding a patient’s health.

The best part is that healthcare systems don’t really need to tell predictive modeling software what to do. Healthcare providers can set the predictive analytics program up with historical data and input some goals (for example, “Find trends that help us with enhancing patient care and identifying patients in at-risk groups”). Now, all your healthcare organization has to do is wait while the machine does the hard work.

The Impact of Predictive Analytics on Patient Care

We’ve already mentioned that predictive analytics in healthcare improves patient outcomes, and here’s an example of how that might work in the healthcare sector.

Marie comes into the doctor’s office complaining of arm pain and a headache. Doctors in many health systems would simply tell her to take a pain reliever and send her home. But predictive modeling software checks her electronic health records and recognizes that Marie falls into a group that’s predisposed to heart problems. Now, Marie’s doctor can dig deeper and craft a custom healthcare plan that’s suited to her unique issues.

Case Studies and Success Stories

Sure, predictive analytics sounds great, but don’t just take our word for it. In one case study, Huntsville Hospital in Alabama brought in clinical decision support (CDS) systems paired with analytics tools for sepsis detection. As a result, sepsis mortality decreased by 53% and 30-day admissions dropped from 19% to 13%.

Predictive analytics can also be very helpful when it comes to caring for our most vulnerable populations, especially elders. Medical Home Network is comprised of 10 facilities throughout Chicago. These facilities treat 122,000 Medicaid beneficiaries, many of whom are low-income and/or elderly.

The COVID-19 pandemic presented a big problem for its vulnerable patient base. Using AI, Medical Home Network was able to identify patients with respiratory issues who would be at serious risk for complications if they contracted COVID-19. They advised at-risk groups to stay at home, potentially preventing thousands of cases of coronavirus in their community.

Operational Advantages in Healthcare Settings

A predictive analytics tool can be invaluable for healthcare providers in all fields. We’ve already discussed how predictive analytics in healthcare can bolster patient outcomes, but that’s not all predictive analytics models can do.

Here are just a few perks of using predictive analytics solutions in the healthcare industry:

  • Population health management: Healthcare providers can use predictive analytics models to identify patients who share certain characteristics. They can then group these patients into a population cohort and find people with similar conditions. This allows doctors to treat patients far faster and potentially save more lives.
  • Customized treatments: Healthcare providers have long followed a traditional, one-size-fits-all approach to healthcare services. However, not all patients respond to the same treatment in the same way. Healthcare organizations can improve patient care and enjoy better healthcare outcomes by using predictive analytics to craft custom-tailored treatment plans.
  • Identify at-risk patients: Thanks to healthcare predictive analytics, there’s no need to wait until patients develop a condition before starting treatment. Healthcare organizations can use predictive analytics in healthcare to comb through hundreds of electronic health records and flag any that could suggest future heart problems, diabetes, or other conditions.
  • Reducing hospital readmission rates: Even if a patient receives quality care at the hospital, there’s always a risk of complications, such as infections and bleeding. Hospitals can use predictive analytics to identify patients who may develop complications after a procedure.
  • Predicting health insurance costs: Individuals can use predictive analytics to accurately predict their insurance costs based on age, gender, and medical history.

Reducing Costs and Improving Efficiency

Did you know that predictive analytics can help healthcare organizations save thousands in operational costs and healthcare resources? A predictive analytics model can reduce healthcare costs for patients, too. Here’s how:

  • Predicting staffing needs: Do you need five nurses on call tonight or 10? Predictive healthcare analytics can suggest the optimal number of staff to keep on hand. This ensures you’re ready for emergencies and patient engagement while eliminating frivolous spending on staff you don’t need.
  • Identifying fraud: Both patients and healthcare professionals commit fraud, and it’s surprisingly common. For instance, a patient might lie to get a prescription for pain medication and then sell the drugs to friends for a profit. Front desk staff may bill a non-covered service as a covered service to get more money out of health insurance companies. Predictive analytics can flag unusual activities so healthcare professionals can stop them in their tracks.
  • Preventing human errors: Let’s face it: No matter how careful you and your staff are, they’re only human, and they’ll make a mistake every once in a while. Predictive algorithms can spot unusual entries in your records and notify you before those little mistakes cause a big problem.
  • Forecasting equipment maintenance needs: If you’ve ever had an X-ray or MRI machine break down right when you need to use it, you know how upsetting equipment failures can be. Using predictive analytics, a healthcare system can pinpoint when it’s time for equipment maintenance. This allows technicians to service equipment during downtime when the facility is less busy.
  • Identifying potential no-shows: Have you ever had patients skip their appointments without calling to cancel first? No-shows can seriously throw off your daily schedule and cost your practice lots of money. Predictive analytics software can scan healthcare data and flag patients most likely to miss appointments. Your practice can engage with them by sending regular appointment reminders or offering to send transportation to pick them up if necessary.

Weave Practice Analytics gives you powerful insights to help you make smarter decisions, connect better with patients, provide more amazing experiences, and grow your practice. Gain insights into every call, message, review, and practice activity. Identify key trends to enhance patient experiences, empowering your team and delighting your patients.

Want to learn more about Weave Practice Analytics? Watch the video below, or learn more here.


Challenges and Considerations

Predictive analytics may sound like just what your practice needs, but it’s not without challenges and risks. These include:

  • Algorithm bias: The results of predictive analytics scans are only as good as their datasets and the algorithms behind them. If the algorithm isn’t trained well, it can develop a bias against certain populations.
  • Ethical problems: Once doctors see the power of predictive analytics in action, they’ll find it too easy to rely on the software for all the answers. They may operate under the assumption that the software is responsible for patient outcomes. Instead, providers must realize that predictive analytics can only make recommendations, and it’s up to trained professionals to decide whether those suggestions are useful or not.
  • Data privacy: Data privacy is a big concern with predictive analytics tools. Patients may feel uncomfortable about the availability of their information in such systems. Organizations must take care only to use software that employs robust security to prevent data breaches.
  • A lack of acceptance: While some providers embrace predictive analytics with open arms, others are naturally more skeptical. They may shy away from using new tools, instead preferring to do things by the book as usual. The best way to overcome this is by giving reluctant providers a chance to provide their input into which systems you choose. You should also offer training sessions to familiarize providers with the software.

Enjoy Improved Operational Efficiency With Weave

Predictive analytics in healthcare can help take your practice to the next level, and so can Weave! Our practice management software has everything you need to run your clinic smoothly, from appointment reminders to online bill payment options.

Plus, it’s packed with analytics tools, such as:

  • Inbound call tracking
  • Patient conversion rates
  • Treatment acceptance rates
  • Optimal messaging timing
  • Average review rating
  • And more

To learn more about Weave, schedule your free demo now.

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