Predictive Analytics in Healthcare: Benefits and Use Case

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The article is written based on Unicsoft’s extensive experience in predictive analytics in healthcare.

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 also applied in 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 in healthcare, 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. 

Our experience in healthcare
Our experience in healthcare

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 7 example of the use of predictive analytics in healthcare

Flag the early signs of condition deterioration

The use of predictive analytics by healthcare providers is necessary to flag patients whose medical conditions are prone to deterioration. This is critically important, meaning predictive analytics can save lives when utilized in the ICU. 

Montefiore Medical Center of the Albert Einstein College of Medicine is leveraging AI to improve patient outcomes in exactly this way. With the help of Intel’s Healthcare AI team, they are creating a PoC project that employs ML algorithms to analyze extensive patient data. 

This data consists of structured information like patient demographics, lab results, and medication records, as well as unstructured data such as doctor’s notes and radiology images. By training machine-learning models on this diverse data, they can discover intricate connections and patterns that may not be immediately obvious to human doctors.

Although the project is still in development, it has already shown significant results. The model identifies patients at risk of sepsis up to 24 hours earlier than clinicians. It also detects signs of pneumonia in chest X-rays with 90% accuracy, compared to 75% accuracy for human radiologists.

Another example of predictive analytics use is the Phillips monitoring system, which automatically measures respiratory rate, blood pressure, heart rate, pulse oximetry, and patient temperatures to calculate the likelihood of state deterioration. According to a study at the general wards of the General Hospital in Bangor, this system reduced serious events like sepsis, respiratory failure, etc., by 35% and cardiac arrests by more than 86%.

Phillips systems are available on the market; with them, any healthcare institution can leverage the benefits of predictive analytics.

Predict and prevent long-term hospital stays

Long-term hospital stays do more harm than good — they increase the risk of hospital-related infections, worsen patient morale, and increase hospital spending. As more patients need long-term hospital stays, fewer new patients can access timely care, and staff become overloaded. Predictive analytics can address this issue.

In 2021, the NHS AI Lab’s AI Skunkworks team built a PoC AI tool to forecast long-term hospital stays. They leveraged a Generative Adversarial Networks (GANs) AI algorithm, which uses two neural networks — one to generate data and one to judge it — to predict the risk of a long-term stay as soon as a patient is admitted.

The algorithm was trained on a vast dataset of patient records, including demographic data, medical histories, and admission details. As a result, the AI tool could predict the risk of a long-term stay with an accuracy of 85% by identifying patients at high risk of a long-term stay early on in their admission. This allowed clinicians to intervene earlier and prevent unnecessary delays in discharge, reducing the average length of stay by two days and saving £1 million per year in the estimate.

The tool is open source, so any other healthcare organization can use it and adapt it to their needs.

Reduce the likelihood of hospital readmissions

On average, 15.7% of Medicare beneficiaries are readmitted to a hospital within 30 days. The use of predictive analytics can significantly reduce rates by identifying patients with a high risk of readmissions and providing better preventative care. One way to achieve this is by providing better treatment plans for patients based on their health and demographics, as GNS Healthcare does.

GNS Healthcare uses machine learning to match patients with the treatments that prove the most effective for them. They help clinicians to find the best treatment plans according to their patient data, including genetic information, lifestyle factors, and treatment outcomes. As a result, GNS Healthcare’s system prevents unnecessary hospitalizations, helps to optimize treatment plans, and identifies the risk of developing a chronic condition. 

By predicting which medications and interventions a patient is likely to respond to, the AI system increases care effectiveness and reduces unnecessary costs.

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 can occur seemingly at random or without prior indication, but predictive analytics can identify dependencies and flag specific warning patterns in patient histories. This way, professionals have more time to respond to mental health concerns before they become emergencies and potentially save the lives of their patients. The REACH VET program has already proven effective in this area.

REACH VET is a predictive analytics system used by the Veterans Health Administration (VHA) to predict which veterans are at high risk of suicide. The algorithm analyzes data within veterans’ health records, including demographics, prior suicide attempts, diagnoses, VHA use, and medications, to identify patients with a statistically elevated risk of suicide or hospitalization. Since 2017, when the REACH VET program was implemented nationwide, the number of inpatient mental health admissions and emergency department visits decreased, and documented suicide attempts were reduced by 5%

This algorithm can potentially be used for other target at-risk groups and for assessing patients’ mental health in general.

Manage the supply chain 

Applying predictive analytics to hospitals can help make more informed decisions based on the overall trend of cost-effective purchasing. The system is complicated, depending on multiple variables such as patient load and patient cases. 

Let’s look at a real-life example by Johnson & Johnson. By analyzing vast amounts of data, including historical sales data, customer demand patterns, and external factors, J&J enhanced inventory management and improved product availability. Namey, implementing predictive models optimized their inventory by up to 20%, resulting in cost savings and reduced carrying costs. Predictive analytics also helped them assess and monitor supplier performance to identify underperforming suppliers and take appropriate actions to prevent issues.

On top of that, the predictive models they used helped them forecast future demand with a high level of precision, reducing forecast errors by up to 30%.

Detect insurance fraudsters 

Based on data shared by the National Health Care Anti-Fraud Association (NHCAA), healthcare fraud leads to significant financial losses, amounting to tens of billions of dollars annually. These losses represent approximately 3 percent of the total healthcare expenditure, and it’s only a conservative estimate. However, certain governmental agencies suggest that the actual loss could be as high as 10 percent of the annual healthcare budget, potentially surpassing $300 billion in value.

With enough data on fraudulent applications, predictive analytics algorithms can solve the challenge by detecting fraudulent activity and reducing the amount of loss. For example, health insurance companies employ AI-powered anomaly detection systems to analyze vast amounts of data, including claims, medical records, and patient profiles. By adapting and learning from new data and evolving fraud patterns, AI enhances the ability to prevent and detect fraudulent activities.

Let’s look at how predictive analytics can help detect and prevent insurance fraud in the case of UnitedHealth Group, one of the largest health insurance providers in the United States. Their predictive analytics system helps detect irregularities such as billing for unnecessary procedures, upcoding (billing for a higher-priced service than provided), and phantom billing (billing for services not rendered). By flagging these potentially fraudulent claims, investigators can focus on high-risk cases, leading to more efficient and targeted fraud prevention.

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

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 in the healthcare industry. The Wanda 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. 

As a result, the client received a solution that allows for enhanced care plan adherence through timely alerts to doctors, empowers care teams to take preventive actions for patients’ well-being, and ensures efficient communication among care team members for coordinated care.

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.


  1. What are the benefits of implementing predictive analytics in healthcare?
  2. Successful cases of predictive analytics in healthcare
  3. Applications of predictive analytics in the healthcare industry
  4. What challenges can come from implementing predictive analytics in healthcare