Predictive analytics is a way to use data to predict different outcomes. Historical data helps to provide the information that can be plugged into an algorithm machine. The machine utilizes the given factors to show a plethora of different outcomes that could happen.
What companies use predictive analytics?
There are many different types of companies that utilize predictive analytics. Businesses all over can make use of the data that is collected. It can help to scale infrastructures and operate data pipelines to excel results. What a business does with predictive analytics is up to them.
What is the Goal of Predictive Analytics in Healthcare?
Many trends and technologies have come and gone through the sector of healthcare management software development. In this decade, most healthcare software development companies have been tied up in incorporating data analytics, Big Data, and artificial intelligence in areas of practical application.
The data from predictive analytics has allowed doctors to be equipped with powerful tools for better and more accurate diagnoses, treatments, and prognoses. One technology that has come up quite frequently in healthcare software solutions is predictive analytics. Predictive analytics is the perfect marriage of the three significant areas mentioned above, making it a powerful weapon in data storage, mining, and processing systems.
A field with enormous potential and scope for advancement by applying predictive analytics is the medical sector. The predictive analytics market is already at $6.6 billion and is projected to grow at a CAGR of 19% to $22.2 billion by 2027.
A study by the Society of Actuaries done in 2017 reveals that an overwhelming 93% of the major players in the healthcare sector believe that predictive analysis would play a massive part in the industry. 47% of healthcare providers were already using solutions that rely on predictive analytics by 2017, while 89% were either using it or would use it in the five years after.
Why Use Predictive Analytics In Healthcare?
Right now, medicine, surgery, therapy, and pretty much any medical technique depend largely or entirely on the doctor. A person can make mistakes; it is not something we can hold upon them. But to the patient who is affected, this can be the difference between life and death. They will wonder why it had to be them.
But what if a system could be designed that would allow the aggregated intelligence and data from a large number of sources to be applied to every individual case for increased chances of recovery?
Highlighting the data and providing many options is precisely what predictive analytics-based healthcare software solutions do.
Sometimes, even when a doctor is going in the right direction, they may not have enough data to work with to provide a satisfactory solution. They might not have all the variables affecting the patient’s condition or may not have tools powerful enough to detect or scan those factors.
For example, there is a high prevalence of co-morbidity of GERD and lung diseases in patients. However, more often than not, it is difficult to determine when the right time is to send a patient with a chronic acid reflux problem for a lung examination, especially with how common it is for many people to just pop antacids.
A predictive analytics model will access the wealth of data generated for all patients in the same scenario. The software then factors a patient’s medical history, food and lifestyle habits, population genetics, family history, and any other applicable variables to determine the right time for the recommendation.
The healthcare industry generates over 30% of global data. They are capturing and analyzing this data to recognize minute details and panoramic patterns that humans cannot. This detailed analysis is what makes predictive analytics so accurate and helpful.
For quite some time now, custom healthcare software development companies have been building disease-specific applications and video games to facilitate faster diagnosis, accurate prognosis, tailored treatment plans, and better recovery options.
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Even though the cost of development, installation, training, and onboarding is expensive, the lifetime savings are significant enough to interest stakeholders actively. In fact, the same SOA study also found that 57% of healthcare executives anticipate a savings of at least 15%, while 26% expect savings of at least 25% in the five years after 2017.
Ways Predictive Analytics is Used in Healthcare
One of the advantages of combining Big Data with data analytical methods is that it opens up avenues to achieve the advantages of both in one. Thus, predictive analytics can bring the following improvements in healthcare:
#1. Accuracy And Speed Of Diagnoses
Predictive analytics gives a doctor access to a wealth of data and a system that can use that data to better pinpoint the affecting condition. When a patient comes to the doctor with a particular symptom, there are two ways healthcare software solutions using predictive analytics can be utilized.
One, the system might look into family or population medical data to predict what the person is most likely to have. Two, when the patient’s medical history is fed into the system, it shows the most probable diagnoses. This diagnosis is faster and more accurate than what a human or even a team can diagnose.
#2. Effective Preventive Healthcare
Having extensive medical records of a patient allows the healthcare software solutions to run all permutations and combinations of possibilities, choosing the most likely ones that might strike a person. Accordingly, the person can make the most suitable measures to prevent, delay, or at least reduce the chances of developing a disease or condition.
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#3. Precise Public Health Measures
A predictive analytics solution can utilize more than just medical data. They could apply any medically relevant data to better predict diseases, epidemics, seasonal conditions, causes of ailments, and the rate of prevalence to execute the right public health policy at the right place and time before it takes on higher proportions.
#4. Tailored Treatment And Medicine
Even when you have two patients with identical conditions and medical history, there will always be differences simply due to the variation in their genetic makeup. This will result in different responses to the same treatment. Predictive analytics can be used to consider those inherent differences and suggest treatment plans that will be most effective for each individual.
#5. Insurance Cost Predictions
There is much merit in healthcare software solutions using predictive analytics when it comes to insurance cost optimization. These solutions can help insurance providers, employers, and hospitals develop the right health plan for each individual and correctly forecast the cost that will be incurred for them.
#6. Learning Models To Cater To The Right Group
In most cases, vaccinations and public health measures implemented across communities without any inequity can be wasteful and unnecessary. There may be zero chances of a disease in a place. A population might be vulnerable to a vaccine or medicine. Perhaps only a small neighborhood in the community needs to be protected against a disease. Predictive analytics can provide such details that will make public healthcare more targeted and efficient.
#7. Improved Patient Outcomes
Predictive analytics models can be used to plan the course of treatment of each patient based on their specific needs. Instead of using broad-spectrum treatments that often cause many side effects, treatment plans can be tailored based on accurate predictions made by the machine. This reduces both recovery time and readmission rate.
#8. Chronic Disease Management
Chronic disease sufferers have to deal with their conditions for a lifetime or years. They would be thankful if a solution can be prescribed to better manage and ease their symptoms based on predictions.
#9. Timely Health Crisis Mitigation
Having access to public health records allows predictive models to forecast health crises that may arise at a future point. Accordingly, preparations can be made to deal with the crisis and mitigate its effects to a minimum.
Predictive analytics has the power to entirely change how healthcare software solutions are created, and healthcare issues are handled. It should not be too far in the future when the healthcare departments are fully equipped with software working on predictive analytics to provide better treatment and care.
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