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How is predictive analytics used in healthcare: TOP 10 examples

May 06, 2022

6 minutes read

Smart healthcare is the future of the healthcare system. This revolution is already impacting the daily work of healthcare professionals and the practice of patient care. The changes taking place can provide solutions to many problems. Still, they also require us to rethink how we organize the health system, shifting the focus from treatment to primary prevention. The main feature of the predictive analytics market in the healthcare sector is that it is global and rapidly developing. In 2018 the market size was a modest $2.9 billion. However, it is expected to grow at an average of 28.9% per year, and, according to Meticulous Research, will reach $84.2 billion in 2027. Let’s see how is predictive analytics used in healthcare.

Main techniques in predictive analytics

Predictive analytics is forecasting based on historical data. Using statistical tools, you can identify patterns in changes in indicators in previous periods and predict how they will behave in the future. The foundation of predictive analytics is big data. These amounts of information cannot be processed using the usual tools.

Successful using predictive analytics in healthcare requires several key elements:

  • The Right Data Sources: Do You Have the Data You Need? If not, how do you find them?
  • Query-matched cleaned data: Build a model that includes data on the problem in question, cleaned of inaccuracies, duplicate entries, bad formatting, or other deficiencies.
  • Automation and Machine Learning: Large, complex datasets quickly surpass human processing capabilities and require massive learning computing power.
  • Link to business goals: Predictive analytics does not exist on its own. It should serve broader business goals.

The COVID-19 pandemic has proven the value of predictive analytics, including analysis of the spread of the disease. Using predictive analytics in less extreme environments might include predictive use of healthcare facilities, modifying pharmaceutical formulas, or writing an insurance plan.

There are two main types of predictive analytics.

Type 1. Controlled learning

It implies the construction (training) of a model according to the initial data and the output results. In the construction of the model, both the parameters of the event and the result on which they affect are known.

Type 2. Uncontrolled learning

In this type, predictive modeling occurs only on incoming data without reference to the answer. The answer is selected automatically in the learning process. This is required to search and analyze hidden patterns within the previously unknown information. The primary method is clustering.

Top 10 cases of using predictive analytics in healthcare

There are many ways to make predictive analytics work for healthcare optimization. Let’s consider the most interesting example of predictive analytics in healthcare.

Clinical predictions

Today millions of prescriptions for home-delivered and retail pharmacies each year are being processed. With so much data collected, the companies can analyze individual patient information so effectively that they will soon be able to notify medical staff of severe side effects of a drug long before it is even prescribed to the patient.

Resource allocations

Using predictive analytics in healthcare allows hospitals to easily visualize complex data so that any team member can easily interpret it. Thus, it is possible to predict the workload of the wards or the frequency of ambulance calls if we analyze historical data.

Programs can be used to identify and eliminate bottlenecks or unreasonable clinical changes. For example, an app used in a UK hospital with 20 operating theatres showed that a one percent increase in room occupancy would generate about $15,000 in extra revenue per week. If the operating room is not running at 100% capacity, you can use big dataset analytics to determine the cause of low usage and correct it.

Disease progression

Thanks to big data technologies in Germany, oncological diseases or a predisposition to them are already detected by analyzing the blood of patients and donors. As a result of timely diagnosis, the costs of the state and the people themselves are significantly reduced, and the effectiveness of treatment is also incredibly increased. After all, one of the main enemies of the patient who has launched the disease is time.

Diagnosis and selection of the desired treatment regimen can take precious minutes, which is crucial in a prompt response when malignant tumors are detected.

Hospitalizations and treatments

The latest technologies, capable of predicting and reliably predicting possible diseases based on the symptoms of diseases and laboratory and instrumental data, can significantly facilitate the decision-making of specialists in the medical field. Such technologies will help the doctor choose the most important results in a tremendous amount of information from the medical history to prescribe adequate treatment. This is another example of predictive analytics in healthcare.

Hospital readmissions

Health care providers would know that a patient is at risk for addiction before they can prescribe pain medication. In such a situation, it will be possible to choose a different treatment plan or more carefully control the consumption of drugs.

Analysis of written prescriptions, physiology, and other medical information will allow detection of the development of a chronic disease or a disease that has not yet been properly diagnosed.

Analyzing information about how a patient adheres to doctor’s orders after discharge from the hospital will help the medical institution predict the likelihood of readmission within the next 90 days and take appropriate measures to prevent it.

Related: Predictive analytics in healthcare: importance and future


In addition to the already known and everyday tasks, big data can also be used to fight diseases and track the growth of epidemics. The use of Big Data technology in epidemiology makes it possible to build both geographical and social models of population health and predictive models for the development of epidemic outbreaks.

So, nine days before an outbreak of infectious diseases such as Ebola was officially declared an epidemic, a team of researchers and scientists from Boston, using big data, detected the spread of fever in Guinea.


The creation, testing, and industrial production of drugs are costly. The development of a new drug costs an average of $500 million, which also includes processing a large amount of data about side effects and pharmacological properties. Companies that developed more than six new drugs in 10 years spent an average of $5.8 billion on development and research.

However, the end does not always justify the means. At times, mistakes in calculations, delays in research, and other hassles cause pharmaceutical companies to suffer severe losses. Big data technology can significantly facilitate their work and allows them to establish effective communication, improve sales of pharmaceutical products, and predict the effectiveness of medicines.

Insurance reimbursements

The largest health insurance companies can process the information on a big data platform. By analyzing big data with the help of advanced tools, companies form the complete picture of each of their millions of customers. This helps improve the quality of clinical care, track financial performance, and detect fraud.

This data is used to quickly identify the root causes of problems, develop innovative solutions, and quickly adapt to the changing conditions in which healthcare facilities operate.

Predictive analytics solutions for healthcare provided by VITech

There are many ways to make predictive analytics work for the benefit of medicine, and VITech is ready to offer some advanced solutions for using predictive analytics in healthcare.

There is an algorithm based on the data of people who come to the hospital, take tests in private clinics, undergo examinations, and consider a considerable number of indicators: living conditions in the area, number of stories of the house, average pressure, etc. For example, if there is a sudden change in the weather, patients A, B, and C with high weather sensitivity are more likely to go to the hospital. Predictive analytics in healthcare will help save lives, reduce the cost of after-temporary hospitalization and advance developments in this area for several years ahead.

It is also possible to develop a solution that allows making decisions based on a large amount of unstructured medical data, among which are: clinical, scientific, genetic, and personal data.

Prediction of risk factors in surgery can be organized based on a system that allows you to control important information about the patient during treatment and predict surgical risks using data from electronic medical records. The system automatically searches for treatment protocols and then displays the results with a calculated red, yellow, or green risk indicator. This technology provides an accurate and fast way to determine which patients are at high risk of developing sepsis, which is a critical task, and track the side effects of various drugs and outbreaks.

Contact us to learn more about what solutions we can offer your organization.

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