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5 Ways to Use Predictive Analytics in Healthcare

What are the 5 Ways to Use Healthcare Predictive Analytics Software?

Estimated read time: 5 minutes

This blog discusses 5 examples of using healthcare predictive analytics software – the future of medical treatment.

1. To Quickly Diagnose and Treat Aggressive, Life-threatening Diseases

If there’s anything changing the way we practice medicine, it’s predictive analytics in healthcare. Using big data, physicians are able to help diagnose, treat and cure patients faster than ever before. Healthcare predictive analytics software is particularly helpful when dealing with:

  • Quickly advancing diseases
  • Diseases with common, flu-like symptoms
  • Diseases that have mild, almost non-existent symptoms

One disease that has been particularly tricky because of its mild symptoms and aggressive escalation is sepsis. In the beginning, sepsis only shows symptoms a doctor may not think twice about – fatigue, fever, shortness of breath, confusion, etc.

These symptoms fit in line with a number of more common, less-deadly diseases; however, in the case of sepsis, a patient’s mortality rate increases by over 7 percent each hour they go undiagnosed. This means doctors must move fast.

In a healthcare-related predictive analytics case study from the University of Pennsylvania, predictive analytics model was shown to be incredibly successful at treating severe sepsis before damage sets in.

Without healthcare that focuses on predictive analytics, just 50 percent of septic shock patients receive effective therapy on time. This is terrifying.

With predictive analytics technology, Penn Medicine managed to detect 80 percent of high-risk sepsis cases within 30 hours from the start of symptoms.  This is revolutionary.

2. To Increase the Accuracy of Diagnoses

It’s difficult to know whether or not to hospitalize a patient if they‘re suffering from common symptoms. For example, a patient has chest pain. What is the cause?

  • A heart attack
  • An early symptom of coronary artery disease
  • Pancreatitis
  • A pulled muscle
  • An anxiety attack
  • Indigestion

These are vastly different diagnoses – ranging from life threatening to go-to-bed-you’ll-get-over-it. A doctor may not think mild chest pain is something that requires hospitalization, but it may be a marker of a larger problem. With predictive analytics, doctors can make a much more informed call.

Healthcare predictive analytics software lets doctors answer questions about the patient, which are then sent into a system and tested. This assesses the likelihood that patient can be sent home safely and notifies the doctor about what is most likely happening.

Predictive analytics models aren’t a fool-proof way to tell a doctor what to do and how to diagnose their patient, but they do help him back up his original clinical judgements.

3. For More Effective Preventative Treatment

Most us don‘t know when we’re going to get sick, but hidden deep in our genes are some major indicators. One way predictive analytics in healthcare sector has been extremely successful is to identify patient’s risk of contracting an illness in the future.

This can be anything from predicting certain types of cancer and diabetes to helping stave off Alzheimer’s.

For example, predictive analytics can scan a patient’s genome to find whether or not the patient has a gene marker for early onset Alzheimer’s disease.

If the gene is found in the patient’s family tree, preventative treatment – which includes memory-enriching activities, exercise and good nutrition — can begin immediately.

This one patient’s treatment can also be used to help create a more effective treatment plan for another patient in the future. Doctors can collect treatment data in an electronic medical record (EMR) to later analyze historical data extensively and in electronic health records to share EHR data between themselves.

For example, if this patient shows improvement after performing certain brain games but has not been responsive to a change in diet, predictive analytics will rule that maybe this particular diet is not effective in treating similar patients with early onset Alzheimer’s.

This allows doctors to focus on treatments they know work rather than find out preventative treatment hasn’t been successful when it’s too late.

4. To Predict Insurance and Product Costs for Employees

It’s no secret that healthcare is a big, big business. Insurance is a huge expense for companies looking to provide health coverage to their employees. Big data and analytics technology can help employers calculate future healthcare costs and save where they can.

Healthcare providers do this by generating prediction algorithms based on their own databases and employer data. Hospitals and insurance providers can synchronize their databases and actuarial tables to build specific, cost-effective health plans.

This helps employers in predicting patient behaviors and determine which providers can give them the best bang for their buck.

For example, if an employer has a large number of women of child-bearing age, predictive analytics may help them find a healthcare plan that focuses on prenatal care and doctor’s visits.

5. To Help Pharmaceutical Companies Better Meet the Needs of the Public

The pharmaceutical industry is a massive money-maker that focuses on bringing the largest amount of money possible with the lowest amount of cost. This is unfortunate because when it comes to healthcare, a good solution isn’t always the most lucrative.

There often isn’t room to develop or test out medications for smaller groups of individuals. If you use predictive analytics in this aspect of healthcare, the benefits can be extraordinary. The benefits include:

  • Wasting less money on ineffective medicine
  • Finding markets for lesser-used, but effective, medication
  • Evaluating the need for specific medications

Pharmaceutical companies are using predictive analytics to figure out if it’s economically feasible to bring back old medications that were dropped because they were not the most popular.

Research is able to predict which less-used medications could be economically lucrative to revive vs. the medications that were cancelled because they actually weren’t helping anyone.

This is apparent in vaccines. If 25,000 people get a vaccine to prevent 10 people from getting a disease, that’s a gigantic waste. If these medications have unwanted side effects, it could equate to more people suffering.

Predictive analytics can help determine if a vaccine would be more harm than good.

Do you plan to invest in healthcare predictive analytics?

Healthcare predictive analytics software is changing the way healthcare works for the better. Patients will be more informed, physicians will be able to provide better and more precise treatments.

Pharmaceutical companies and insurance providers through actionable insights will be able to offer more cost-effect, effective medications and plans. Artificial intelligence is a win-win when it comes to medicine.

Implementation of predictive models demand highly skilled data analytics individuals who have thorough understanding of the domain like big data handling, machine learning algorithms for predictive modeling, working of advanced artificial neural networks like deep learning, etc.

If you are looking to invest in healthcare predictive analytics software, you will require experts of the data science field. DevTeam.Space can help you here. DevTeam.Space is a community of field-expert software developers and data scientists experienced in developing market-competitive solutions using the latest tools and technologies.

You can get in touch by describing your initial healthcare-related predictive analytics requirements using this quick form. One of our technical managers will get back to you for further assistance in your predictive analytics journey.

Top FAQs on Healthcare Predictive Analytics

1. How predictive analytics is used in the healthcare system?

Predictive algorithms in health systems help medical professionals with overall improved population health management including better disease diagnosis, improved patient data security, better insurance data management, efficient communication between healthcare providers and patients, improved patient engagement, better chronic disease management strategies, etc.

2. What are some use cases of Artificial Intelligence in healthcare?

Medical imaging, pre-diagnosis, preventive healthcare, efficient patient care, drug discovery, etc. are prominent use cases of artificial intelligence in the healthcare industry.

3. What is the top predictive analytics tool?

Microsoft Azure Machine Learning, IBM Watson Studio, etc. are effective tools for predictive data analytics in healthcare organizations.


Alexey Semeney

Founder of DevTeam.Space

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