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Predictive Analytics in Healthcare: Top Benefits and Most Popular Applications

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Predictive analytics in the medical field has many applications — from managing electronic health record (EHR) systems to healthcare data automation to real-time monitoring of intensive care units. But although this tech trend is still new, it is already transforming the industry. 

Because of the impressive predictive analytics advantages, the relevant market was valued at $32.08B in 2021 and can reach $121B by 2030

Healthcare analytics market size, 2020 to 2030 (USD billion) 
Figure 1. Healthcare analytics market size, 2020 to 2030 (USD billion) 

Affected by the COVID-19 pandemic, healthcare is hungry for analytics technologies to predict and prevent the worsening and spreading of diseases and life-threatening health conditions. That only makes predictive analytics in healthcare on the rise.

Predictive Analytics in Healthcare

Predictive analytics is also known as predictive modeling. The technology aims at providing actionable insights through analyzing historical or real-time data. For example, such data covers information on patients and medical facilities that you can derive from EHRs.

By finding common patterns, data-driven applications can predict and alert about potential events and anomalies, like changes in patients’ conditions or the facility’s performance. That, in turn, transforms into opportunities for improving healthcare using predictive data analytics, such as providing care of higher quality and better decision-making.

How Does Predictive Analytics Work?

Predictive analytics technology works through 5 steps:

  1. Data collection (which involves extracting historical data from data storage or gathering real-time data via data streaming)
  2. Data clean-up (for instance, filtering and combining)
  3. Predictive model development
  4. Analytics system integration
  5. Data validation
How predictive analytics in healthcare works
Figure 2. How predictive analytics works

At the same time, predictive analytics in healthcare uses a combination of these techniques:

As you can see, predictive analytics starts with data mining. In healthcare, it uses current data streams as a source for further predictive model building and data analysis. So let us discuss the role of data streaming next.

What Is Data Streaming in Healthcare?

Data streaming is the implementation of streaming analytics, a form of predictive. As a type of data analytics, it requires a different approach than when working with historical data.

In particular, data streaming implies a continuous process of big data transmission. This endless flow of data is called a data stream and consists of data elements that are frequently updated and ordered in time. 

The data elements are events that have taken place and are significant in a business, like data from connected devices or patient conditions. Then, the particular software processes such information to derive valuable insights from it. 

Stream data processing architecture
Figure 3. Stream data processing architecture

Due to other data analytics technologies, the information is analyzed, visualized, and further used for business development and growth. So the goal of data streaming is to provide relevant and recent information to the user.

At the same time, data streams differ from traditional data due to specific characteristics. A distinctive feature of data streaming is that it is transmitted in real-time. Also, data streams are:

  • time-sensitive — they lose significance over time
  • real-time — report events as they occur
  • continuous — streams of data are endless
  • multi-sourced — collect data from several sources
  • imperfect — may contain damaged, unordered, or missing data

You can use data streaming in various industries, from ride-sharing apps to clinical decision support systems. 

Healthcare AI Modeling & Predictive Analytics Advantages

Like other businesses, data streams provide many opportunities for healthcare. Here are some of the predictive analytics advantages and benefits of adopting AI and streaming analytics:

1. Making Use of IoT Devices

Internet of Things (IoT) is a relatively new technology that extends our capabilities, including those in the healthcare sector. Smart wearables, sensors, and other devices are essential data sources. But we cannot use the information they generate and collect without a suitable data analytics technology. Because IoT devices generate immense loads of data in real-time, technology like streaming analytics is essential to process and interpret information with minimum losses and over a short period.

2. No Need to Store Unnecessary Data

Companies must consider data storage or warehouses when working with traditional historical data. This way, you must keep the information until you later access it for analysis. But this leads storage to contain unfiltered data, meaning that some of the space is taken in vain with insignificant information.

With predictive data analytics and streaming, information is processed in real-time as it is generated from relevant sources. Therefore, it is analyzed immediately and can be defined as unnecessary before entering storage. That allows for maximizing the data warehouse or cloud storage capacity and the quality of stored data.

How data streaming works
Figure 4. How data streaming works

3. Combining Several Sources of Data

Data streaming enables information from several devices simultaneously, which allows a broader image of the patient’s condition. For example, the data can originate from medical images, wearables, and onsite medical devices streamed into the patient’s EHR systems.

4. Instant Personalized Insights

Streaming analytics provides data on events that occur in real-time, which allows for ensuring personalized and timely care. Based on the information streamed, clinicians get alerts if any changes occur in their patients’ conditions and can act upon them to take timely measures.

Besides, patient data streaming helps create detailed and up-to-date profiles that contain valid information and are updated with relevant insights as they occur. This way, patients can receive care of the highest quality — each can have a personal approach based on their unique case.

Applications of Predictive Analytics in Healthcare

Because of the vast amounts of data being generated and circulating in the industry, predictive healthcare analytics can find many helpful applications. Medical facilities can enhance their activities across all teams and departments, from patient care to operations management.

Here are some processes that predictive analytics using data streaming can transform.

Monitoring Intensive Care in Real-Time

Today, intensive care units (ICUs) are equipped with many devices connected to a single system in healthcare software. That allows medical staff to access information on patients in intensive care at all times and get alerted in case of any anomalies to take timely measures. 

In other words, patient monitors installed in ICUs generate much real-time data. But while this provides opportunities for more attentive patient care, the issue is that up to 99% of the alerts signal safe changes for the patients and do not require medical intervention. 

That leads doctors to be in constant tension that is unnecessary, provoking fatigue that makes a negative impact on their ability to help patients when they need it. Data streaming, in turn, helps analyze incoming data and omit it to ignore alerts of little significance. 

How monitoring intensive care units in real-time works
Figure 5. How monitoring intensive care units in real-time works

Streamlining Precision Medicine

Predictive analytics helps improve precision medicine significantly. That implies finding the most suitable drugs for a particular person considering their genetics, medical history, and lifestyle. 

To achieve such personalization in medicine, vast data is a must. Also, improving healthcare using predictive data analytics and data streaming allows for the most use of those data loads. Based on the information about the patient or entire patient groups, you can identify patterns illustrating the most suitable medicine for specific types of patients. 

And with further analytics, algorithms can predict the patient’s most likely response to a drug based on their medical history, similar patient cases, and other unique factors.

That is especially helpful in complex cases of patient diseases like cancer. Doctors may be in a situation where they must try different approaches to find what scenario will work best to stop the disease from progressing, but they do not have enough time to do that. Predictive analytics, in turn, helps eliminate the less effective treatment plans for each particular patient and suggests scenarios that promise more chances of success.

Clinical Decision Support Systems

Clinical decision support systems (CDSS) imply software (sometimes AI-powered) that helps doctors make the best medical decisions regarding patient care. While the last word belongs to the clinician, the system handles much work over a short period. 

The system processes lots of patient information based on incoming data, which can be both historical and streaming. Then, with the help of predictive analytics, the software provides relevant alerts, assisting insights, and actionable suggestions on the subsequent measures to take regarding the patient’s treatment.

Naturally, this application of predictive analytics in healthcare directly leads to:

  • Better patient treatment outcomes
  • More successful medication prescriptions
  • Reduced medical errors.
How clinical decision support system works
Figure 6. How clinical decision support system works

Patient Risk Scoring

For people suffering from conditions that cannot be cured or are chronic, who make up 60% of US adults, it is crucial to monitor the state of their health at various times to spot any risks and potential deteriorations. Similarly, patients recently dismissed from the ward are also at risk. Also, it is critical for other groups at risk, like the elderly, who faced a more significant threat during the emergence of COVID-19. 

In this case, predictive data analytics can be beneficial. Based on the information about the person’s lifestyle and real-time health data, the software assesses risks and scores potential scenarios worth paying attention to or addressing to prevent worsening. 

For example, diabetes is one of the most widespread health conditions affecting more than 400 million adults globally. And, to a vast extent, the patient has to manage it independently. It is the person’s responsibility to pay regular visits to the doctor and keep track of their blood sugar levels and balance them.

Powered by data streaming and predictive analytics, software connected to devices like glucose meters and other wearables can assess current and consider potential patient risks throughout the day. This way, the doctor can be alerted in case of an alleged threat and act upon it to minimize health risks for the patient.

In addition to the state and progress of complex or chronic health conditions, AI modeling can also analyze historical data to predict dangerous patient events, such as falls in the elderly. And with current data streams, the software predicts when the potential fall can happen as patients go on with their day, sending alerts to warn them and, hopefully, prevent it.

Preventive Care

Another application of predictive analytics in healthcare implies analyzing genetic information to identify the person’s predisposition to certain diseases. Since more than 10% of adults have some genetic irregularities, that can be effective for preventive care.

With the application of analytics on historical data about genetic conditions, their correlations to diseases, current data on patients’ health, image processing, and other AI-based technologies, patients can be warned about the potential health issues they can face. 

That can be done at any person’s age and as early as the neonatal stage. In such a case, predictive analytics can provide awareness and allow the person to monitor the condition further to catch early signs of the disease and intervene.

Preventive care with predictive analytics in healthcare
Figure 7. Preventive care with predictive analytics 

Better Resource Allocation 

Because healthcare organizations have an extensive and complex structure, allocating resources can be challenging. Medical machine maintenance costs, operation management, inpatient stays, and other areas require proper resource distribution. Otherwise, the facility may face limited capabilities and financial losses.

So how can predictive analytics in healthcare help make better resource allocation decisions? Because AI modeling based on ML algorithms allows for identifying patterns, analytics can suggest how much resource each area needs at a particular time. That reduces the chance of allocating too much or little resources to an area of operations or department. 

Also, that applies to patient overstays. Thanks to predictive analytics through data streaming, the software can determine scenarios when patients are most likely not to need the stay anymore, allowing both the facility and the patients to cut expenses on unnecessary wards.

Similarly, predictive data analytics contributes to machine maintenance. Like any other, tools and devices operating in healthcare facilities can wear out, so tracking their real-time condition via sensors enables spotting any issues as they occur. That allows fixing or replacing the details to ensure the machine keeps working instead of neglecting repair until it fails.

Lastly, human resource allocation is also one of the predictive analytics advantages. Based on data like the number of patients and medical staff, and other factors, AI modeling can determine when the facility is the busiest and requires the most specialists. Besides, the employees can also take their time off without sacrificing patient care when there are fewer visitors.

Enhanced Supply Chain Management

When it comes to the supply chain in the healthcare industry, it implies a complex network of collective processes and systems. From local manufacturing to global distribution, the chain has to ensure that medical supplies, including devices and medication, are provided to patients on time, even during natural disasters or pandemics, like COVID-19. 

Predictive analytics can help manage the supply chain in healthcare more effectively. Pattern identification and data analysis can help gain insights regarding supply demand and the most cost-effective vendor consolidations to cut expenses on favorable terms for the facility. 

Besides, AI modeling can help find the most challenging areas of the supply chain. That allows tackling issues or even preventing them, which minimizes their effect on the entire chain.

Types of supply chain analytics
Figure 8. Types of supply chain analytics

Safer Health Insuring

The area of health insurance can also face predictive analytics advantages. Algorithms can calculate accurate insurance costs for patients based on current data streams about the people, from their age to insurance history. That results in a cost-beneficial solution for the patients.

Then, fraud detection, which can help save up to $300B in losses caused by fraud, can also be powered by predictive analytics. Based on known cases of fraudulent health insurance claims, you can catch potentially malicious cases as soon as they occur, preventing them.

Similarly, upcoding is one more issue predictive analytics can resolve. That implies higher medical billing than should be considering the healthcare service provided. The technology can improve financial management in healthcare facilities by analyzing the billing data.

Automating & Securing Administrative Tasks 

Administrative tasks are probably the most prone to mistakes in healthcare because they require a lot of human intervention. Since HIPAA defines patient data as sensitive, it requires high security and protection. 

Such things as recordkeeping, managing data storage, scheduling, and other duties benefit from predictive analytics to a great extent. With this in mind, automating various mundane tasks in healthcare facilities helps:

  • Reduce the chance of error 
  • Improve data security
  • Optimize data storage 
  • Speed the completion of each task
  • Identify the level of security for specific patient data streams

Then, predictive data analytics can enhance data storage. Staff can be alerted when storages are likely to run out of space before they do and fail to perform. Also, data optimization will be performed automatically and tailored to the needs of a particular facility. Lastly, unnecessary data and duplicates will be removed automatically, prolonging the storage capacity.

Figure 9. Predictive analytics in data storage 

Automated software configuration and updates are some more areas where predictive analytics in healthcare can automate administration management. Again, by learning the trends of your organization, the software will suggest adjusting system software configurations according to them. The same applies to software updates — these will be installed automatically without intervening in the facility’s operations.


Predictive analytics in healthcare using data streaming provides excellent opportunities to transform a medical facility regarding its operations and capabilities. It helps make the most use of data streams across all processes and departments depending on trained algorithms. If you consider diving deeper into the opportunities you can get by implementing predictive analytics, contact us to discuss your project further and find a tailored solution.

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