5 Ways to Use Predictive Analytics in Healthcare

Physicians are some of the smartest, most highly-trained professionals in the world. Taking on med school is enough to make most of us shutter, which is why we generally trust doctors to give us the best, most effective treatment possible the moment we fall ill. We expect doctors’ brains to be encyclopedias of symptoms, diagnoses and treatments – to run with the swift, calculating brain of a Google search engine. Most of the time, we leave the hospital a bit better with packages of antibiotics or mended bones. Unfortunately, that’s not always the case.

The truth is, the most trained, brilliant and experienced physicians are still human. They can miss things. Though it’s certainly rare, they can make incorrect diagnoses or miss life-threatening symptoms until it’s too late. This is why we have machines, and why big data and business analytics is expected to see double digit revenue growth by 2020. Predictive analytics-based healthcare is literally saving lives (and making tons of money, to boot).

Here 5 examples of predictive analytics in healthcare – the future of medical treatment.

To Quickly Diagnose and Treat Aggressive, Life-threatening Diseases

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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. Predictive analysis 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 were 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, Penn Medicine managed to detect 80 percent of severe sepsis cases within 30 hours from the start of symptoms.  This is revolutionary.

To Increase the Accuracy of Diagnoses

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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.

Predictive analytics 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 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.

For More Effective Preventative Treatment

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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 has been extremely successful is by identifying a 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) for later use. 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.

To Predict Insurance and Product Costs for Employees

Happy female brunette doctor at medical office with patient, writing on clipboard, wearing glasses, stethoscope and lab coat.

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. Predictive analytics can help employers calculate future medical costs and save where they can.

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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 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.

To Help Pharmaceutical Companies Better Meet the Needs of the Public

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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 were nixed 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 than don’t. Predictive analytics can help determine if a vaccine would be more harm than good.

Conclusion

Predictive analysis is changing the way healthcare works for the better. Patients will be more informed, physicians will be able to provide better, more precise treatment and pharmaceutical companies and insurance providers will be able to offer more cost-effect, effective medications and plans. Artificial intelligence is a win-win when it comes to medicine.