Undoubtedly, predictive analytics is a total game changer in current COVID era healthcare. Doctors are some of the most trusted professionals when it comes to providing us with the best and the most effective treatment when we fall ill. People think that the brain of the doctors is like encyclopedias of the name of diseases, their symptoms, their diagnoses and their treatments. Just like the swift brain of Google Search Engine! In the majority of cases, people leave the hospital with mended bones and a bag of medicines that can make their health better. However, that is not always true.
People tend to forget that the experienced and trained physicians are also human, and they can make mistakes as well. Of course, the chances are rare. However, in the current COVID era, where virtual consultations and telemedicine have to be in the play, there may be times when they miss the symptoms of a life-threatening disease until it’s too late. This is where predictive analytics comes in. It’s contributing a lot in saving lives, particularly in the changing medical scenario brought on post COVID 19.
What is Predictive Analytics in Medicine
So, what exactly is predictive analytics? Well, predictive analytics, commonly known as PA, uses statistical methods along with technology in order to search through a huge amount of information and analyzing it, so that the outcomes for individual patients can be predicted. That information may contain data from both the latest medical research that is published in journals and the data from past treatment outcomes.
Following are the two main ways in which traditional statistics and predictive analytics are different –
- Predictions are made not for groups but for individuals
- Predictive analytics does not depend on normal curve
Want to know how PA can help in the improvement of healthcare? Read below.
Increased accuracy of diagnoses with predictive analytics
If a patient is suffering from common symptoms, it can be difficult for you to understand whether you need to hospitalize him/her. Suppose a patient is having serious chest pain. So what can be the cause?
- An anxiety attack
- A pulled muscle
- A heart attack
These diagnoses are hugely different, ranging from mild to life-threatening. A doctor may think that this is just a mild chest pain that will go away after some time, but it may be the beginning of a larger problem. Doctors get to make a much informed decision with predictive analytics.
Faster diagnosis and treatment of life-threatening diseases
In this modern life, people have become more vulnerable to a growing number of life-threatening and complicated diseases. This has made the healthcare providers to always look for more effective as well as advanced solutions. It is often that these complicated diseases do not have a proper diagnosis until it’s too late. With predictive analytics, doctors can work as a team with genetic sciences. This way they can identify if a patient is having a life-threatening disease and help them in taking preventive measures.
Get predictions regarding insurance product costs
In order to obtain predictions regarding the medical costs in future, employers who are providing healthcare advantages for employees can use the features or characteristics of their workforce into a predictive analytic algorithm. Depending upon the data of the company, predictions can be made. Hospitals and companies that have partnered with insurance providers, can synchronize actuarial tables as well as databases so that they can create proper health plans and models.
Provides doctors with detailed information about the health of individual patients
Among doctors as well as individual patients, predictive analysis definitely has the potential to be a huge hit! It follows the Evidence-based Medicine (EBM) approach. This enables the doctors to get a large pool of personalized health information about the patient. Based on the different statistics that is learned using PA, the doctor will get to make the most appropriate treatment plans for each and every individual.
Predictive Analytics helps researchers in developing a better prediction model
Predictive analysis can help medical researchers to create such prediction models that can improve their accuracy gradually. It is essential that you notice, in the field of medical research, the difference between clinical significance and statistical significance. This fact determines that the studies of a huge population can act as a thing of great significance to the researchers. The reason behind this is the fact that statistically important differences can prove to be dangerous for a clinical study.
Helps pharmaceutical companies better fulfill the needs of the public
Considered as a huge money-maker, the pharmaceutical industry aims at bringing the largest amount of money possible with the lowest investment. This is quite unfortunate. In case of healthcare, the most lucrative solution is not always a good option. Most of the times, there remains no opportunity to test and develop medications for a smaller group of people. In this aspect of healthcare, if you use predictive analytics, then the advantages that it will offer will be remarkable. Such as –
- Finding markets for although less-used but much effective medication
- Wasting a lot less money on medicines that are ineffective
- Examine the need for certain medications
There are a number of medicines that were dropped. The reason is – they were not popular. Today, predictive analytics powers pharmaceutical companies in order to find out if those medicines are reusable in a different situation and if it would be economically beneficial. With research, it can is possible to predict which less-used medicines could be economically beneficial. Also it can be predicted if those medicines were dropped because they really were not effective.
Predictive Analytics offers the benefits of better patient outcomes
PA help patients to improve their overall quality of life and prevent them from wasting money on treatments that may not actually suit them. Suppose a person has a complicated genetic history. In that case, if a doctor gives them a prescription by using PA, then s/he will know that it has better chances of working. It saves the doctor from wasting time and experimenting with different prescriptions that have earlier worked for a large population.
Conclusively, the adoptions of predictive analytics in healthcare are going to be advantageous for all the parties – from doctors and healthcare agencies – to pharmaceutical companies and patients.
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