How to use predictive analytics in the healthcare industry?

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Healthcare institutions have undergone several changes that were brought on by the COVID-19 pandemic — an increase in patient volumes, a lack of staff, and new medical administrative procedures. The pandemic served to expose the medical industry’s weaknesses. Since 2020, healthcare organizations across the industry have been actively adopting modern technologies to create a critical and robust infrastructure by focusing on patients.

According to the Deloitte report, a vast majority of healthcare companies have already gone digital and adopted digital technologies in various functional areas. Healthcare innovations are here to streamline the work of physicians, optimize the healthcare system, improve patient experiences, reduce the chances of human error, and bring down costs using web and mobile software applications. 

Today we’re going to shed more light on the use of predictive analytics in the healthcare industry. 

What is predictive analytics?

Predictive analytics refers to the use of modeling techniques and statistics to predict future results and performances. Custom software solutions loaded with predictive analytics look at present and historical data patterns to identify if such patterns are likely to occur again. A spectrum of business industries use predictive analytics to make important decisions in industries spanning insurance, marketing, manufacturing, and healthcare. 

More and more healthcare institutions are adopting predictive analytics into their conventional workflows. In 2018, these included just 47% of organizations. Today that number has risen to more than 60%. That works out to a 13-point increase. 

Though predictive analytics helps forecast future performance, it shouldn’t be confused with machine learning. They aren’t the same. Predictive analytics leverages the power of artificial intelligence, data mining, machine learning, modeling, deep learning, and statistics. 

The predictive modeling process in healthcare 

The main goal of predictive analytics in healthcare is to answer the question, “What is likely to occur in the future based on the patterns of behavior and overall trends?” The overall process of predictive analytics is iterative, depending on the required outcomes. The predictive modeling process consists of four key stages:

  • Data collection and cleansing 

At this stage, collecting data from all available sources is required to sift out the required information by cleansing operations to remove unnecessary data for more accurate predictions.

  • Data analysis

Before the model is built, you need to create a data chart to study the collected data. With the chart, you’ll be able to analyze the data behavior, relationships between variables, and even find solutions based on the overall trends. 

  • Develop a predictive model

At this stage, you generate a baseline forecast, review it, and add assumptions to develop a consensus demand plan. It’s better to run many algorithms and compare their results to find the most appropriate classification model.

  • Integrate the model into your workflows

You have to integrate the model into your internal processes to make it valuable for your healthcare organization and improve patient care. 

Benefits of predictive analytics in the healthcare industry

  • Improve care for patients with chronic diseases
  • Reduce the likelihood of hospital readmissions
  • Power up medical and clinical research 
  • Optimize internal processes and performance 
  • Reduce per patient costs

With custom-built predictive analytics models, healthcare institutions can improve clinical care, management performance, their overall efficiency, and save up to 44% of their budget.

Top five use cases for predictive analytics in the healthcare industry

Flag the early signs of condition deterioration

Healthcare institutions use predictive analytics to flag patients whose health conditions are prone to worsen. For example, doctors can use the model to identify which diabetic patients are most likely to develop renal disease or predict which patients are progressing into sepsis. This is critically important and predictive analytics can save lives when utilized in the ICU. 

Reduce the likelihood of hospital readmissions

On average, 15.7% of Medicare beneficiaries are readmitted to a hospital within 30 days. And predictive analytics can significantly lower such rates by identifying patients with a high risk of readmissions and providing better preventative care. 

Lower the risks of suicide and self-harm 

Mental health conditions require even more attention from healthcare experts than chronic illnesses. Self-harm and suicide seemingly occur at random without any previous signs, but predictive analytics are able to identify dependencies and flag specific patterns. Thus, professionals will have more time to prevent mental breakdowns and save the lives of their patients. 

Manage the supply chain 

Managing a hospital supply chain is a real challenge even for seasoned professionals. It’s a complicated system that depends on multiple variables such as patient load and patient cases. With predictive analytics, hospitals can make more informed decisions based on the overall trend of cost-effective purchasing. 

Detect insurance fraudsters 

According to stats provided by the National Health Care Anti-Fraud Association, healthcare fraud costs amount to around $68 billion annually, which works out to 3 percent of annual healthcare spending. With enough data on fraudulent claims, predictive analytics algorithms can be trained to identify all fraudulent activities and reduce the amount that is lost.  

Are you ready to power up your healthcare institutions with predictive analytics?

Challenges associated with the adoption of predictive analytics in healthcare

Along with benefits, predictive analytics comes with certain challenges. According to the Society of Actuaries study, the adoption of predictive analytics has slowed down due to:

  • Insufficient funding 
  • Compliance and regulatory issues 
  • Lack of necessary data 
  • Absence of skilled and experienced specialists
  • Lack of technologies 

There are some challenges that we cannot solve or influence, but at Unicsoft, we have experienced AI and ML developers, Big Data specialists, and analysts who can build a powerful predictive analytics model customized to your needs and requirements. 

Unicsoft’s experience with predictive analytics in the healthcare industry

Wanda is one of the projects we’re working on at Unicsoft. It’s an AI-powered cloud platform for predictive remote patient monitoring. The app can identify 90% of adverse events seven days before they occur — keeping at-risk patients safer and even saving their lives. 

As a key part of the solution, we built an ML model that analyzes patient histories, baseline risks, health trackers, symptom surveys, and health trajectories for alerting health professionals about any significant deteriorations in patient conditions. 

Final words 

Predictive analytics coupled with machine learning and artificial intelligence services in Healthcare can completely change the healthcare landscape. In fact, it has already started paving the way for tech-driven healthcare. New technologies can improve patient care, find more effective treatment, develop new medications, and reduce costs. 

Are you ready to power up your healthcare institutions with predictive analytics? Contact us and we’ll help improve your patient workflows and achieve the desired outcomes.