Predictive analytics in healthcare: use cases, benefits, and challenges

The question of predicting the future, be it the prophesies left to us by Nostradamus or the Mayan calendar, always arouses a ton of skepticism and heated discussions. And although it may be doubtful whether anyone or anything is able to predict the future, well-thought-out and complex analytics allow us to look ahead for a while. Plus, technological progress does not stand still, and modern digital tools allow companies from any industry to carry out large-scale analytics of huge databases, automating and accelerating the process through the use of AI and Machine Learning technologies. Of course, such an important industry as healthcare has not been left out.

So, in this article, we will tell you about how predictive analytics tools are used in the healthcare industry, what benefits it brings, what risks are associated with their use and what to expect in the near future.

What is predictive analytics in healthcare?

Although we have compared the main thesis of this article with classical analytical processes, there is a significant difference between them. Thus, classical analytics involves the analysis of known data and processes in order to identify patterns. The predictive analytics approach, in turn, uses all available data to try to predict what will happen next.

Predictive analytics tools find the widest range of applications in the healthcare industry. We will talk about specific examples below, but for now, it must be said that there are no contradictions in this pattern. There is nothing more important than human health, and therefore many healthcare institutions devote a lot of time to predicting any trends of the coming years, preventing the occurrence of chronic diseases and epidemics, and improving the patients’ health in general.

Predictive analytics in healthcare using Big Data, data mining, AI and ML capabilities, predictive analytics tools analyze large arrays of patient medical data taken from Electronic Health Record systems, insurance proceedings, patient records, medical imaging, and so on. All this is done in order to find out what diseases a particular patient is more prone to, how his body will respond to various types of treatment, whether he is inclined to skip regular visits to the doctor, and whether he should be expected to call for medical aid in the future. And that is precisely why deep learning in healthcare is so important. 

In general, the combination of such technological capabilities with all the knowledge about medicine, epidemics, and diseases that have been accumulated by specialists over the years, allows doctors to better understand the interaction between all external factors and the human body. Ultimately, it leads to a complete rethinking of healthcare as a whole and truly personalized treatment provision.

Healthcare predictive analytics use cases

Let's review some predictive analytics healthcare examples. We identified 7 main areas that are of the greatest interest and perspective.

Predicting no-show cases

Let's start with a seemingly insignificant factor, which nevertheless bears great losses for healthcare institutions. No-show cases are often compared to situations where the patient refused to show at the appointment. However, it is worth distinguishing between situations when the patient informed the doctor about his refusal in advance (at least a couple of hours) and when this happens 30 minutes before the appointment. In the first case, medical institutions are able to minimize administrative losses. In the second, there is too little time left to save yourself from losses. In such a situation, as in the case if the patient simply did not appear at the appointment without any notification, medical institutions can suffer an average administrative loss of $200-300 for each no-show case. And if in a specific example this may seem like a trifle, then taking the entire healthcare system of, for example, the United States, and calculating the losses for a year, the problem becomes much more frightening. And although it is not possible to calculate accurate and specific data regarding this issue, some studies indicate an incredible amount of $150 billion/year losses from no-show cases.

Within the framework of this problem, healthcare predictive analytics software is used in order to predict which patients are prone to not showing up for an appointment. By studying the behavioral factors of patients and their medical history, such a predictive analytics tool will help prevent no-show cases, reduce losses and, increase the level of medical services in general.

Moreover, some companies even use the received data in order to contact the patient and make sure that he will arrive for the session.

Faster handling of insurance claims

The issue of interaction with insurance companies is also an area of predictive analytics applications in healthcare. Oftentimes, healthcare providers have to manually define the correct codes required for an insurance claim. Predictive analytics for healthcare providers like Apexio can automate and significantly speed up this process by providing the specialist with the most appropriate code for an insured event.

Improving data security

The issue of cybersecurity and secure data storage has long gone beyond the confines of individual industries and today it’s one of the main topics in negotiations at the highest level. In an industry like healthcare, this issue is getting even more attention.

Predictive medical solutions serve to improve patient data security. So, predictive analytics tools can be used to calculate the risks of online transactions in real-time and recommend solutions for certain events. In addition, such software can analyze the history of incoming requests for access to data and EHR systems. In the future, an alarm will be announced if the system recognizes some unusual request.

Finally, predictive analytics software works according to all the rules of other security-released software. So, technicians in medical institutions can use custom healthcare solutions to check the hospital systems for any weaknesses and stress, as well as to analyze potential threats from the outside.

The issue of cybersecurity remains one of the dominant challenges in the healthcare industry, as it carries huge losses for healthcare organizations.

Analysis and prevention of suicide cases

The problem of suicide remains acute not for the United States only, but for the whole world. Somewhere, the percentage of suicides from the total number of deaths is not critical, but there are places on Earth where it goes off the scale. Nevertheless, the official data from the NIMH (National Institute of Mental Health) shows that suicide is the second most common cause of death among people aged 10 to 34 and it raises serious concerns.

Analyzing patient data from EHR systems using high technologies, professionals from Vanderbilt University managed to create a predictive analytics system that is capable of identifying individuals prone to suicide. For the sake of convenience, all analyzed patients were divided into 8 groups according to the degree of suicidal tendencies risk.

There is an ethical and moral issue here - we cannot check all people for their suicidal tendencies. Nevertheless, such a predictive analytics model will allow us to weed out specific individuals from the general mass of people, scanning and talking with whom wouldn't be a bad idea.

Increasing the level of customer satisfaction

Customized services and patient management are extremely important factors if you want to improve the patient's condition in general. Medical institutions use predictive analytics tools so that after a thorough analysis of patient data, it is possible to create thoughtful profiles, automatically send customized notifications, and create an approach that will be more patient-tailored.

These same efforts help to distribute patients to those who really want to change their lives for the better and those who are inclined to make their condition worse.

Finally, such predictive analytics tools help to create more customized and relevant marketing campaigns.

Chronic disease prevention

Chronic diseases are not so much dangerous by the very fact of their effect on the human body (since it can be controlled by doctors) but by the fact that it is difficult to prevent their occurrence in time. That is why chronic diseases prevention is one of the most important areas in which predictive modelling healthcare tools are being used. By analyzing various data about patients, including their demographics, age, relatives, diseases, etc, doctors are able to identify people who are dangerously close to getting chronic diseases. Having identified such individuals, medical specialists can take preventive measures to ensure the absence of the disease development. In particular, this approach helps elderly people suffering from early stages of diabetes.

Global trends and infections outbreaks analysis

Finally, we would like to say about the broader application of predictive analytics tools, which allows not only to provide assistance to specific patients but to change the approach in the healthcare industry as a whole. So, thanks to the predictive analytics approach, medical specialists analyze a large amount of data and case histories of the population of an entire country, which makes it possible to prevent a shift in the incidence graphs for specific diseases in one direction or another. As an example, we can consider a situation in which the predictive analytics tool made it possible to assess global behavioral factors in the issue of nicotine addiction and adjust the work of the entire industry accordingly.

However, the possibilities of predictive analytics in healthcare are not limited to the framework of one country. For the second year now, the whole world continues to suffer from the COVID-19 pandemic, and many people dream that such diseases could be prevented. In fact, we learned about COVID-19 earlier than WHO officially announced it. We just didn't pay attention to it. As early as December 30th, some news outlets began rebroadcasting news published by the AI-based BlueDot system that cases of the new SARS virus were being reported in a market area in Hubei, China. WHO notified the world about the disease only 9 days later.

Well, it's no use crying over spilled milk, and if we did not manage to give a correct assessment of the events in Hubei in time, perhaps this bitter experience will allow us to take predictive analytics tools more seriously in the context of healthcare and respond to COVID-19 trends and any other epidemics before it’s too late.

What advantages do predictive analytics bring?

Having evaluated all the possibilities that predictive analytics tools provide, their advantages become obvious:

• Medical institutions get the opportunity to reduce their costs for readmission, no-show cases, and so on.

• Employees can be freed from routine and manual tasks by automating processes and thereby improving the quality of services provided.

• Cybersecurity goes to a new level with predictive analytics tools, allowing institutions to prepare their digital system for possible problems, loads, and attacks.

• The readiness of the healthcare industry as a whole to respond to new trends and outbreaks of new diseases is also increasing.

• Predictive analytics tools allow making medical services more personal and customized, which positively affects both the patient's success rate and the quality of marketing campaigns.

• Maybe chronic diseases cannot be taken under complete control, but at least the spread of such cases among patients can be reduced.

Healthcare predictive analytics software challenges

The healthcare industry is developing and medical institutions, increasing the process of their digitalization, accumulate more and more data. One way or another, the introduction of predictive analytics tools by medical institutions into permanent use is only a matter of time, because the benefits are undeniable. However, along the way, there are several challenges that predictive analytics tools cannot yet cope with. And although these challenges do not diminish confidence in a bright future for this technology, they nevertheless somewhat slow down the process of predictive analytics tools’ widespread implementation.

• Moral ethics. On the moral side, there is a problem that is inherent in the human race. It can be characterized by the fact that when a person counts on the help of technology, he relaxes. And in the matter of healthcare, this is a key point. Doctors cannot afford to completely rely on predictive analytics, no matter how safe they are. There is always a risk of a mistake, and it is worth remembering that predictive analytics are only auxiliary tools that help the main tool - the doctors - to do their job better.

• Bias in Results and Difficulties in Compliance with the Regulator. Today, the Predictive Analytics segment in healthcare is not regulated by any standard act or government agency. Given that this rule is strictly enforced in all other segments related to healthcare, this fact is surprising. There is no doubt that the appearance of a standard act regulating the development of predictive analytics tools is not far off. Nevertheless, so far all medical institutions have to rely only on the conscience of the predictive analytics healthcare companies. At the same time, the algorithm underlying any predictive analytics tool may remain biased and produce unqualified results. All this is also due to the lack of clear rules and laws regarding the development of predictive analytics software.

• The additional technical burden on doctors. A predictive analytics tool is sophisticated software that performs complex processes. And although it allows you to automate and get rid of some manual routine tasks, its work still depends on the technical knowledge of the medical specialists who work with it. Hence, there is an additional burden on doctors, since now they need not only to be professionals in their field but also to have serious skills in using computers and software. One of the ways out of this situation is the already applied development method, in which doctors are involved in the process of creating a predictive analytics tool from the earliest stages so that the final software exactly meets the needs of doctors and is understandable to them.

Bottom line

Predictive analytics in the healthcare segment is of great interest both for investors and software vendors, as well as for medical institutions and all representatives of the healthcare industry. There is no doubt that this technology will gain a foothold in the market for a long time and will only gain momentum, attracting more and more investments from both venture capital funds and rich countries. We continue to monitor the development of this market and advise you to take a closer look at it.