What is predictive analytics in healthcare and why it is important

Predictive analytics can seriously change the medical industry shortly. Based on the use of big data, this technology will help, for example, prevent the occurrence of chronic diseases in patients and correctly allocate doctors’ resources. For the industry, the load can be reduced, and, in general, its work can be made more predictable.

What is predictive analytics in healthcare?

Predictive analytics in general allows company management to make more accurate and correct decisions and reduce risks based on predictable data.

At the same time, the predicted estimate does not indicate the future for sure but only shows the probabilities of the occurrence of certain events. Still, forecasting accuracy can be improved by using machine learning and enriching the accumulated data with additional data, such as weather, seasonality, and so on, depending on the analytics object.

In healthcare, patient data analysis and risk assessment, image analysis, and diagnostics are the areas of most significant interest. For example, predictive analysis in healthcare can help prevent chronic diseases based on data from medical records and other indicators.

According to the MIT Technology Review, 93% of healthcare organizations name artificial intelligence and predictive analysis as one of the most critical development priorities, and 78% of healthcare organizations in the world are already implementing or planning to implement such solutions.

What is the role of forecasting analytics?

Predictive (or forecasting) analytics, as it comes out of its name, predicts unknown events in the future, answering the question “What can happen?” based on the analysis of accumulated information. Many methods are used here: mathematical statistics, modeling, machine learning, and other areas of data science, as well as data mining.

For example, predictive analytics of current and past performance of production equipment will determine in advance the time of its preventive maintenance to avoid the breakdown of expensive equipment.

In medicine, predictive analytics in healthcare plays a significant role. It can make a diagnosis based on the symptoms of the disease, identify factors that provoke the disease, determine the propensity for the condition in the future, form recommendations, and prescribe medicines for the treatment and prevention of diseases. However, in this case, the incorrect configuration of the machine learning module can lead to tragic consequences.

Related: Benefits of predictive analytics in healthcare

The coronavirus pandemic has given a massive impetus to the development of medical technology. This area was previously considered promising, but now its relevance has become apparent to the whole world. Health care has to adapt to the situation and overcome its rigidity. Therefore, in the next few years, we should expect explosive growth in the MedTech market. The possibility of remote sale of medicines has already appeared, and some restrictions on telemedicine have been lifted. Predictive analytics in healthcare in the fight against the epidemic plays an important role: it helps analyze data and develop better treatment regimens.

It is unlikely that artificial intelligence will replace the doctor in the foreseeable future. Still, the fact that it will give a considerable impetus to the development of medicine is already apparent. Today, it is already expanding the capabilities of geneticists, developers of medical equipment, and drugs.

What is the value of forecasting analytics?

Today we have unique, previously inaccessible tools and data. We can digitize and quantify virtually any aspect of the human body.

By analogy with Google Maps, which provides a satellite view of the area, viewing traffic jams and virtual walks through the city’s streets, today predictive analytics in healthcare can create a “medical map” of the human body. It will contain information about its external features, anatomical structure (these data is obtained by scanning), physiological processes (sensors tell us about them), DNA, RNA, and biochemical indicators.

We can quantify the environment. Previously, this was basically impossible, but today this information is relatively easy to obtain. All this provides excellent value for predictive analytics in medicine, as it can significantly improve healthcare quality in the foreseeable future.

Potential options for using predictive analytics in healthcare

In many countries, predictive analytics in medicine today already solves the problems of receiving and distributing calls, tracking the location of ambulance teams and their condition, gives recommendations on the shortest path to patient addresses. It also takes on the tasks of supporting decision-making during hospitalization, statistical processing of information, and conformity assessment standards of medical care to assess the effectiveness of the entire service in the region.

Specialized applications also keep records of medicines and the cost of gasoline for ambulances. They calculate the cost of air ambulance flights and provide information support for the work of medical teams.

Related: How is predictive analytics used in healthcare

Here are the main benefits that are already available today in the work of ambulance services using predictive analytics in healthcare:

• Predicting the number of dispatchers servicing incoming calls. This is important and necessary because efficiency depends on it when accepting a call and processing it. The faster this is done, the faster the call will be transferred to the ambulance team, and, as a result, the total time for servicing an ambulance call will be reduced.
• The territorial distribution of calls and their quantitative change in the time of calls. This information can be presented in a map with thermal layers according to the number of calls for emergency medical care. This will make it possible to predict the number of calls in the city’s new micro-districts being built up and the time it will take for an ambulance crew to reach the place of the call, as well as to receive proposals for the redistribution of ambulance resources or, if necessary, issue proposals for the formation of new ambulance substations.
• Forecasting the consumption of medicines to form a sufficient amount for their purchase. It is vital to accurately create the amount of necessary medication since significant overspending leads to inefficient distribution of funds, and their insufficient amount is even more critical since a shortage of medicines in the work of ambulances is unacceptable.

Thus, in specialized applications, in a few clicks, you can receive information with a call forecast by the hour, the required number of dispatchers, and a heat map of call distribution. This solution will reduce the processing time of all incoming calls.

What is the future of forecasting analytics?

Healthcare companies are beginning to use the opportunities provided by predictive analytics in healthcare actively. Here are the benefits they can get in the near future.

Cloud platforms are starting to emerge for processing big data from the analysis of patients’ clinical, social, and behavioral characteristics. Over time, a common platform will bring such data together and be able to share it with all participants in the system: hospitals and healthcare providers. One company cites the following benefits of using predictive analytics in healthcare:

• Health care planning for individuals and populations, including predictive disease management.
• Identification and implementation of the most effective practical measures that help reduce the number of readmissions.
• Early intervention reduces the risk of blood poisoning and kidney failure to reduce negative consequences.
• Optimizing the management of treatment outcomes and drug costs.
• Development of tools to improve the quality of patient care.

Equally important is the use of predictive analytics in processing prescriptions for home-delivered and retail pharmacies. Companies will use powerful analytics tools to process big data. They analyze information about individual patients 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.

This will lead to significant positive changes in the health of people:

• Health care providers will 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.

As we can see, predictive analytics can really change healthcare for the better very soon. It is a powerful tool that we have yet to learn how to use effectively. For it to bring maximum benefit, we recommend contacting professionals who have sufficient experience and expertise in this area. To learn more about our team and the projects we have implemented in the field of predictive medical analytics, don’t hesitate to get in touch with us. We will gladly answer all your questions.

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