Predictive analytics is a kind of analytics where data from various sources are used to predict the future through activity, trends, and behavior. It makes use of machine learning algorithms and artificial intelligence to understand what the future might bring. This is done through data mining, analytical techniques, and predictive modeling techniques. The analytics system finds out the risks and opportunities for the future by recognizing a particular pattern.
Through predictive analytics, the relationships among several factors are analyzed before a particular risk factor or opportunity for the future is identified. This allows organizations, especially enterprises in the healthcare sector, to becoming proactive and forward-looking, because the outcomes and behavior are anticipated more on the basis of data (both historical data and transactional). In the past, it was all done through assumptions; that’s been changed now.
Predictive analytics can be a major help in solving a number of difficult problems that plague organizations. In modern healthcare, a number of analytics methods are combined to detect a pattern, and any change in the pattern will be immediately noted. This helps in detecting fraudulent activities and vulnerabilities that may hamper the patient’s privacy. And that’s not all. Predictive analytics can really make the world different for patients. Here are some use cases that prove that:
1. Effective Treatments
If you know you are going to be sick 5 years from now, what preventive measures would you take today? You could try to make changes in your lifestyle, perhaps include a gym regime and adopt a change in diet. Suppose somebody tells you that you are likely to get Alzheimer’s 20 years from now, wouldn’t you do everything in your power to prevent it? That’s where predictive analytics can help you.
It appears there are some major indicators hidden deep within our genes that can be identified when medical systems scan a patient’s genome. If the scan reflects an early onset of Alzheimer’s disease in the patient’s family tree, then you can start preventive treatments immediately. And that includes nutritious food, memory enriching activities and exercises.
The doctor can use this treatment pattern to treat another patient, and for this purpose, it will all be stored in EMR or Electronic Medical Records. This is how the doctor starts treatment in the first place – by collecting the data and planning the treatment activities for the future. The patient’s improvement or response to treatment will also be recorded. This way they don’t have to experiment on the patient, but rather focus on what actually works for a patient with certain symptoms and following a certain lifestyle.
2. Better Understanding of Patient Deterioration
The patient can take a turn for the worse anytime, even while in the hospital. Often times, the doctors may not be able to predict the change in a patient’s vitals, and patient deterioration may occur before it’s too late to reverse the situation. But this can change through predictive data analytics. The care providers will be alerted to the patient’s vitals, and they will be able to identify and prevent any deterioration as and when it occurs. And sometimes, even before they become visible to the naked eye.
3. Predicting Employee Insurance Costs
The healthcare insurance industry has become quite prominent in recent years. Healthcare insurance providers check on the algorithms based on the employee data and databases to come up with specific and cost-effective plans that they really have use for. For example, if a company employs women of childbearing age, they can be offered a plan that includes doctor visits and prenatal care.
4. Diagnosis Accuracy
It is always important to come up with accurate diagnosis, especially when there is an overlapping of symptoms. Certain symptoms could be representative of a heart attack, anxiety attack, indigestion or even an early symptom of coronary artery disease. The diagnosis could be vastly different. In some cases, the doctors may just tell you to pop a pill and sleep it off, and in some situations, depending on the gravity of the illness, the doctor might demand hospitalization.
With predictive analytics, the doctor just runs through all the symptoms the patient has and then feeds it through the system. This does help him reach better clinical judgments. It need not always be a foolproof answer, but it does back up his original clinical judgment.
5. Preventing Self Inflicted Injuries
The incidence of suicides is getting alarmingly high. So how about having some technique to predict if a particular individual is on the brink of taking his or her own life? EHRs come to the rescue here. They flag the individuals who are likely to harm themselves with the help of Predictive analytics. KP and Mental Health Research Network conducted a study regarding this in 2018. This has made it possible to accurately identify those individuals who are suffering symptoms of standard depression, and find out if they possess an elevated risk for suicide attempts.
Predictive analysis is a major help when dealing with diseases that are quickly advancing, or diseases that have almost non-existent symptoms. It also helps rein in certain diseases that have common flu-like symptoms but is not really so because they could actually turn out to be something more serious.
For example, sepsis. It is a disease that can have fever, shortness of breath, confusion, fatigue and similar symptoms. These could be the symptoms of less-deadly diseases, but if it is sepsis, then the diagnosis has to be really fast because the patient’s life is at stake, if not treated quickly.
Predictive analytics is a growing field, and it has a much-needed impact in the field of healthcare. Implementing healthcare software systems that include predictive analytics would do a whole lot of good for patients, doctors, medical organizations, insurance agencies and the entire industry as a whole.