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Posted by on January 30, 2021

The healthcare industry is now at the forefront of fighting COVID-19 and has to leverage the latest technological advances to overcome the pandemic quickly. Cloud-based platforms slowly replace on-prem software as the mainstream healthcare data storage and processing approach. Hence, significant investments are made in updating the healthcare systems and applications.

The digital healthcare industry is evaluated at $206 billion in 2020, which is nearly 40% more than the year 2018 revenue of $122.66 billion.

Such a positive tendency paves the way for integrating even more cloud-native technologies to further improve the outcomes and deliver more value to all the parties involved. Predictive analytics is one of such technologies, an area of statistical analysis where Machine Learning algorithms are used to predict the outcomes of some events based on historical data.

Predictive analytics can be applied to three major areas in healthcare:

  • Identifying the palliative in-home patients’ journey pain points with smart IoT sensors
  • Monitoring hospital admission, therapy, ICU (Intensive Care Unit), rehabilitation operations, all the way to discharging a patient.
  • Providing timely predictive maintenance of equipment, minimizing the risk of costly shutdowns, and operations interruption.

One of the most prominent applications of predictive analytics is monitoring the clinical patients’ vitals and general condition to alert the caregivers of the earliest signs of emergency events. Using predictive analytics leads to improving the patient experience and healthcare outcomes for all the parties and stakeholders involved.

This approach’s efficiency is backed by an ample data set. The 2019 survey of healthcare executives by the Society of Actuaries shows that 60% of them use predictive analytics in their day-to-day routines. Furthermore, 42% of the approach’s proponents reported greatly increased patient satisfaction, and 39% stated it turned out to be a rather cost-efficient method to apply.

How Does Predictive Analytics Work?

Experienced clinicians have been into the manual version of predictive analysis for centuries. They used to predict the treatment outcomes based on the sum of the patient’s symptoms. Next, they analyzed them based on their experience, eventually assigning treatment that seemed to be the best for a particular patient. However, the doctors’ capabilities were limited by human physiological and mental limitations.

Training Machine Learning algorithms to use vast arrays of healthcare data (Electronic Health Records, real-time input from smart Internet of Things sensors, medical imagery, etc) allows discovering general patterns for various conditions and outcomes. Once the model is trained on historical data, it can be deployed to real-time environments and help save lives.

Predictive analytics’ main benefits are:

  • Access to vast amounts of data;
  • Processing said data quite quickly;
  • Detecting the earliest signs of emergencies, like identifying a potential heart stroke hours before the incident.

For example, AI algorithms have proven very efficient in diagnosing cancer in mammaries, cervical tissues, and internal tumors, as per the report from the National Cancer Institute, USA. Thus, predictive analytics allows improving the outcomes for a wide variety of medical conditions using a wide range of available data.

4 Real-Life Cases of Predictive Analytics Use In Healthcare

Since we’ve already discussed from a theoretical perspective the pivotal areas of applying predictive analytics to the healthcare industry, it seems relevant to transcend into the world of its practical application. Here come four practical examples of how predictive analytics can be used in healthcare.

  1. Detecting early signs of post-operational complications in the ICU or in the general ward. Due to integrating predictive analytics algorithms in real-time patient condition monitoring systems, the physicians and caregivers are able to prevent emergencies, instead of merely reacting to their consequences.
  2. Predictive outpatient care. Using wearable sensors or IoT systems to remotely monitor patients for the possibility of a heart attack, ulcer, and other conditions, including severe post-COVID complications, helps medical care units react to all incidents in a timely and reliable manner.
  3. Monitoring equipment performance to enable predictive maintenance. Malfunctions and shutdowns of life-critical medical systems are a nightmare for any hospital team, especially during the COVID-19 pandemic. This can be solved by implementing AI algorithms trained on the historical data of every module’s breakdowns. Pairing these ML models with smart IoT sensors alerts the hospital staff in advance, thus enabling timely repair and maintenance to avoid shutdowns.
  4. Modeling and managing patient admissions to optimize hospital workflows. The COVID-19 outbreak has prominently demonstrated the inadequacy of the existing patient admission management tools. Hence, some hospitals from all around the world suffered from being overloaded with patients, while some stood nearly empty. Applying the AI algorithms to monitor the patient admissions allows highlighting the spikes quickly and rerouting the patients to other hospitals, thus normalizing the workflow.

It is crystal clear that predictive analytics company can provide powerful and reliable tools that help foresee and prevent emergencies, optimize hospital workflows and avoid costly equipment repairs.  


Predictive analytics in healthcare is a rapidly growing trend that has showcased its usefulness in terms of time and cost savings, especially during the COVID-19 outbreak. As we are yet far from overcoming the pandemic, the demand for such solutions will definitely grow, which turns them into a promising and lucrative market sector. All you need is a bold vision and reliable technical expertise, and if you have the latter, SPSoft can provide the former!


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