Predictive Analytics in Healthcare Use Cases & Examples

In 2023, the global market for healthcare predictive analytics was valued at $14.58 billion and is projected to grow to $67.26 billion by 2030, with a compound annual growth rate (CAGR) of 24.4%. This rapid expansion presents significant opportunities for both healthcare providers and health technology companies.

Unlike traditional analytics that mainly report on past events, predictive analytics analyzes both historical and real-time data to forecast future outcomes and detect trends in patient care. It relies on a range of techniques and technologies, including data mining, statistical methods, artificial intelligence (AI), generative AI (Gen AI), and machine learning (ML). Simply put, predictive analytics solutions answer the question of what might happen next?
In this article, we will explore practical use cases and real-world examples of predictive analytics in healthcare.

How Predictive Analytics Works in the Healthcare Sector?

In healthcare, predictive analytics combines real-time and historical data to forecast future health trends, anticipate patient needs, and improve operational efficiency. These solutions process large volumes of data from diverse sources, including electronic health records (EHRs), insurance claims, administrative systems, and other components of the healthcare ecosystem. 

By applying techniques such as statistical modeling, data mining, and machine learning, predictive analytics generates actionable insights. Healthcare organizations can use these insights to support a wide range of initiatives, from managing chronic diseases more effectively to reducing hospital readmission rates.

7 Examples of Predictive Analytics in Healthcare?

Predictive analytics is rapidly transforming healthcare by enabling providers to anticipate challenges and improve patient outcomes. To better understand its practical impact, here are seven real-world predictive analytics in healthcare use cases that highlight how this technology is driving meaningful improvements across the sector.

1. Reducing Readmissions

Hospital readmissions come with significant costs. In the United States, approximately $52.4 billion is spent annually on readmissions. Moreover, Medicare’s Hospital Readmission Reduction Program imposes substantial financial penalties on hospitals with high readmission rates, providing strong motivation to reduce them. Research indicates that 82% of hospitals participating in this program have faced such penalties.

At Corewell Health, a research team implemented AI and predictive analytics to identify patients at high risk of readmission. They analyzed patients who struggled to recover after hospitalization and developed a comprehensive recovery plan focused on three key areas: behavioral health, clinical challenges, and social determinants of health. 

When the predictive analytics tool flagged a patient likely to be readmitted, an interdisciplinary team stepped in to address these factors proactively. This targeted approach helped Corewell Health prevent readmission for 200 patients, resulting in $5 million in cost savings. 

Predictive analytics in healthcare helps identify patients who are at a high risk of being readmitted. This allows healthcare providers to allocate extra resources for follow-up care and customize discharge plans to minimize the chances of rapid readmission.

2. Managing Population Health

Medical research organizations use predictive analytics to oversee population health more effectively. This technology enables the identification of public behavior trends and predicts their potential effects on overall health. For example, a study published in Lancet Public Health warned that if alcohol consumption habits in the US remain unchanged, there will be an increase in alcohol-related liver diseases and deaths.

In addition, predictive analytics can identify disease outbreaks early, allowing governments to respond and prepare in a timely manner.

When it comes to predicting outbreaks, many wonder if predictive analytics could have anticipated the COVID-19 pandemic. The answer is yes. BlueDot, a Canadian company specializing in predictive analytics and AI, detected unusual pneumonia cases in Wuhan and issued an alert on December 30, 2019. Just nine days later, the World Health Organization officially announced the discovery of the novel coronavirus.

3. Strengthening Cybersecurity

Cyberattacks are a growing concern in the healthcare industry. The HIPAA Healthcare Data Breach Report shows that more than 23 million individuals were impacted by medical data breaches in just the first five months of 2025. In May alone, there were 60 separate breaches, each compromising the data of over 500 people. The following graph illustrates breach statistics over a one-year period.
Predictive analytics can play a crucial role in improving cybersecurity within the healthcare sector. By combining predictive models with artificial intelligence, healthcare organizations can assess risk levels for various online activities in real time. This enables them to assign risk scores to transactions and respond proactively to potential threats based on those scores, helping to prevent breaches before they occur. 

A major hospital in the United States successfully prevented a significant ransomware attack thanks to its AI-driven predictive analytics system, which identified unusual activity within just 2.3 seconds and stopped the threat before any data was compromised. 

Milton Keynes University Hospital uses a self-learning AI system that continuously monitors typical behavior across its digital infrastructure. By recognizing even slight changes, such as those linked to ransomware or insider threats, the system is able to predict and prevent attacks early. This approach allows for around-the-clock automated threat response, fewer false alarms, and minimized system downtime. 

4. Predicting Disease Onsets

Hospitals can apply machine learning to anticipate the development of diseases even before patients display any noticeable symptoms or early warning signs.

In one study, researchers used predictive analytics to detect early signs of diabetes. By analyzing patient data from Hospital de Santa Luzia, the team demonstrated that it was possible to develop a reliable machine learning model based on patient profiles and their medication history. 

In another case, a research team created a machine learning tool designed to predict multiple types of multiple myeloma. This model used tumor genomic data and information about prescribed treatments. Through the study, scientists identified 90 genes commonly found in tumors that are highly prone to mutation. Using this data, the tool was able to predict 12 different forms of multiple myeloma.

5. Accelerating Insurance Claims Submission and Processing

Predictive analytics can streamline the insurance claims process in healthcare by identifying claims that are likely to be denied, based on historical data and common rejection trends. It can also flag missing or incorrect reimbursement codes and suggest appropriate corrections. These capabilities help healthcare providers submit claims more efficiently and with fewer errors. 

In a recent study, researchers used machine learning to forecast which healthcare insurance claims were at high risk of denial. The AI model flagged problematic claims in real time, allowing administrators to take corrective action before submission. As a result, the organization saw a 25% drop in claim rejections within just six months.

6. Forecasting Suicide Attempts

Suicide ranks as the tenth leading cause of death in the United States, claiming the lives of 14 out of every 100,000 people each year. By leveraging AI-driven predictive analytics, it is possible to assess the risk of future suicide attempts. These tools analyze factors such as a person’s history of attempts, clinical records, and social and economic background to estimate their potential for future risk.

A research team at Vanderbilt University Medical Center (VUMC) created a predictive analytics model that uses electronic health records to estimate a patient’s risk of attempting suicide. The model was tested over an 11-month period, during which it operated in the background while physicians saw patients, continuously assessing the likelihood of individuals returning for suicide-related treatment. 

The system categorized patients into eight risk-level groups based on various factors. The researchers found that more than one-third of all suicide attempts came from those in the highest-risk group. Based on these findings, the team recommended that patients identified in high-risk categories undergo screening for suicidal behavior.

7. Predicting Appointment No-Shows

Missed appointments cost the U.S. healthcare system approximately $150 billion each year. For individual practitioners, each missed visit results in an average loss of $200, along with additional administrative challenges. Predictive analytics can help reduce these losses by identifying patients who are likely to miss appointments without notifying the provider. This allows clinics and hospitals to take preventive action, which helps protect revenue and improve provider satisfaction.

In Chile, researchers collaborated with Doctor Luis Calvo Mackenna Hospital, a pediatric center facing a 29 percent no-show rate. They applied predictive analytics and machine learning to patient data, including demographics and social conditions, to identify individuals at higher risk of missing appointments. These patients received reminder calls. During an eight-week trial, this strategy led to a 10.3 percent decrease in no-shows.

Final Thoughts

If you are managing a medical facility, explore practical strategies to implement predictive analytics within your organization. Focus on identifying high-impact use cases, such as reducing no-shows, preventing readmissions, or improving patient outcomes. Work closely with doctors and executives to ensure they understand the benefits and are fully engaged in the adoption process. 

If you are the founder of a health tech startup, prioritize building algorithms that are accurate, unbiased, and tailored to the specific needs of your target population. Consider factors such as data quality, demographic diversity, and ethical standards to ensure your solutions are both effective and equitable.

Need assistance? Let our experts at Intelegain help with your implementation. If you need support, don’t hesitate to contact us. At Intelegain, we have deep expertise in AI, generative AI, machine learning, and data analytics, and we’re here to help you implement and deploy effective digital healthcare solutions.