What Is Predictive Analytics in Healthcare?

Predictive analytics in healthcare is the process of analyzing current and historical health data to identify patterns and trends that help predict future clinical and operational healthcare events.

Early use of predictive analytics in healthcare was time-consuming and limited by data availability. However, the growing volume of digitized patient data—from electronic health records (EHRs), medical imaging, devices, and other sources—combined with advancements in computing power and technologies like AI —have now unlocked rapid access to deeper insights that can be used to accelerate and improve patient care and reduce costs. This has led to widespread adoption of predictive analytics in healthcare, with the global market estimated at US$18.49 billion in 2024 and projected to reach US$67.26 billion by 2030.¹

Key factors driving the use of predictive analytics by healthcare organizations and providers include:

• Adoption of value-based payment models: To address rising costs, an aging population, and the prevalence of chronic conditions, payment models are shifting from volume-based to value-based, where hospital and provider compensation is tied to improving patient outcomes. Predictive analytics is well suited for this shift, giving healthcare professionals the ability to predict outcomes before they occur and helping them deliver more personalized proactive care.
• Emergence of the smart hospital: Predictive analytics is a critical capability of smart hospitals, where advanced technologies are implemented to improve the healthcare experience for patients, providers, and staff. Using predictive analytics in smart hospitals enhances patient care by leveraging real-time data to inform clinical decisions and streamline operations.
• Technical capabilities: Advancements in AI, machine learning (ML), natural language processing (NLP), cloud and edge computing, along with the ability to integrate data from disparate systems have helped make predictive analytics a viable tool for organizations that may have not considered it before.

Predictive analytics is now a key piece of any healthcare technology strategy. It’s a critical tool for measuring, aggregating, and making sense of behavioral, psychosocial, and biometric data that, until recently, was not available or was exceedingly hard to capture.

At the individual level, predictive analytics can help health providers deliver the right care to the right patient at the right time. On a larger scale, it can enable health systems to identify and understand larger trends, leading to improved population health strategies.

Predictive Modeling in Healthcare

Predictive modeling is what makes predictive analytics in healthcare possible. It combines statistical techniques with ML algorithms to create a model that forecasts future outcomes based on real-time and historical patient data. Data is gleaned from various sources, such as EHRs, imaging, and genetic testing results, and the model is applied to identify patterns and relationships within the data, allowing for predictions of future events.

Artificial Intelligence in Predictive Healthcare

AI and ML are critical in healthcare to enable:

• Automated complex processing of massive datasets from disparate sources in near-real time.
• Identification of patterns and correlations not apparent to human observers.
• Continuous refinement of insights based on new data.

While predictive analytics can be performed using traditional statistical methods and without the use of AI tools, this approach severely limits an organization’s ability to scale or even leverage real-time data analysis—both of which are essential in healthcare. For healthcare organizations to maximize their use of predictive analytics, incorporating AI and AI-powered technologies has become a nonnegotiable.

Benefits of Predictive Analytics in Healthcare

Healthcare organizations that harness the power of predictive analytics can realize benefits in multiple areas, including patient care, operational costs, efficiency, and overall population health.

Accelerated and More-Proactive Patient Care

Predictive analytics helps clinicians remain one step ahead of healthcare events. Whether identifying patients at risk for chronic conditions or obstacles to care-plan adherence, predictive analytics insights empower providers to deliver proactive care to patients, deliver treatments sooner, and speed time to recovery.

Improved Patient Outcomes

Predictive analytics helps improve patient outcomes by enabling personalized treatment plans and medication optimization. Using patient data, including medical history, genetics, and lifestyle factors, predictive models can recommend personalized treatment plans that are more likely to succeed with each patient.

Reduced Costs

Predictive analytics can drastically reduce costs by accurately forecasting patient length of stay (LOS), enabling hospitals to plan and staff more efficiently. It can also be used to reduce readmission rates for individual patients by identifying those at the time of diagnosis who may need additional medical care. 
Currently, managing and treating chronic conditions like heart disease, diabetes, and cancer accounts for approximately 90 percent of healthcare costs in the US.² By using predictive analytics, clinicians can identify early signs of these chronic conditions and implement targeted interventions. This approach helps keep patients healthier, ensures more stable management of their conditions, and prevents the need for costly, intensive treatments and prolonged hospital stays.

Predictive Analytics in Healthcare Use Cases

Healthcare systems, providers, and other industry professionals are exploring ways to apply predictive analytics within their organizations and practices. Use cases and examples include:

• Identifying patients at risk of decline: Hospitals use predictive analytics to continuously monitor a patient’s vitals, such as blood pressure, oxygen levels, and heart rate, while simultaneously analyzing data coming from labs, EHRs, and other systems to detect early warning signs of decline before symptoms become critical. Warning signs can include variations in heart rate or oxygen saturation. Once the warning signs are detected, the physician is notified so proactive measures can be taken.
• Reducing hospital readmissions: Predictive analytics allows health systems to identify patients at a high risk of readmission through risk stratification. This allows healthcare teams to shift more resources to these patients, resulting in more proactive and highly targeted postdischarge care.
• Predicting patient LOS: By analyzing enormous datasets covering both historical and real-time data— including patient behavior and diagnosis information, hospital operational data, treatment plans, and more— predictive analytics can forecast how long certain patients will need to stay in the hospital. This information can then be used to help reduce overcrowding, improve patient flow in the hospital, and reduce delays in treatment and discharging.
• Detection of critical conditions: By monitoring real-time patient data, such as vitals, lab results, or other clinical information, predictive analytics can identify the onset of sepsis or heart failure—two life-threatening conditions that must be treated quickly to improve a patient’s odds of recovery.
• Personalized medicine: Using a patient’s medical history, genetics, or reactions to previous treatments, predictive analytics can assess and recommend specific medications or treatments that are less likely to cause side effects and more likely to improve a patient’s outcome.
• Staffing and resourcing optimization: Using historical hospital admission data, predictive analytics can forecast patient demand to optimize staffing, ensuring there are enough nurses, physicians, and staff members on-site to treat patients. When staffing is fully optimized, patient wait time is reduced, patient experience improves, and clinician and staff satisfaction increases.

The Future of Predictive Healthcare

As AI and other technologies continue to advance, so too will the use of predictive analytics in healthcare. Predictive models will become more accurate with the ability to process even more data from disparate sources. Using historical and real-time data will evolve patient care to be more proactive, personalized, and efficient, helping to improve patient outcomes. This will have a transformational effect on the healthcare ecosystem, leading to new innovations; more informed, data-driven decision-making; and an overall better healthcare experience for everyone.