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:
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.
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:
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.
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.
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!