July 15, 2024

Health Benefit

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10 high-value use cases for predictive analytics in healthcare

13 min read

As healthcare organizations pursue improved care delivery and increased operational efficiency, digital transformation remains a key strategy to help achieve these goals. Many health systems’ digital transformation journey involves identifying the value of their data and capitalizing on that value through big data analytics.

Of the four types of healthcare data analytics, predictive analytics currently has some of the highest potential for value generation. This type of analytics goes beyond showing stakeholders what happened and why, allowing users to gain insight into what’s likely to happen based on historical data trends.

Being able to forecast potential future patterns has game-changing potential as healthcare organizations aim to move from reactive to proactive, but those looking to leverage predictive analytics must first define relevant use cases.

In this primer, HealthITAnalytics will outline 10 predictive analytics use cases, in alphabetical order, that health systems can pursue as part of a successful predictive analytics strategy.


Improved care coordination can bolster patient outcomes and satisfaction, and predictive analytics is one way healthcare organizations can enhance these efforts. Predictive analytics is beneficial in hospital settings, where care coordination staff are trying to prevent outcomes like patient deterioration and readmission while optimizing patient flow.

Some healthcare organizations are already beginning to see success after deploying advanced analytics to reduce hospital readmissions.

In June, a research team from New York University (NYU) Grossman School of Medicine successfully built a large language model (LLM) known as NYUTron to predict multiple outcomes, including readmissions and length of stay.

The tool, detailed in a Nature study, can accurately forecast 30-day all-cause readmission, in-hospital mortality, comorbidity index, length of stay, and insurance denials using unaltered electronic health record (EHR) data. At the time of the study’s publication, NYUTron could predict 80 percent of all-cause readmissions, a five percent improvement over existing models.

According to a December 2023 NEJM Catalyst study, predictive models deployed at Corewell Health have seen similar success, keeping 200 patients from being readmitted and resulting in a $5 million cost savings.

In a 2022 interview with HealthITAnalytics, leadership from Children’s of Alabama discussed how real-time risk prediction allows the health system to tackle patient deterioration and pursue intensive care unit (ICU) liberation.

Alongside its applications for inpatient care management, predictive analytics is particularly useful for other preventive care uses, such as disease detection.


Effective disease management is vital to improving patient outcomes, but capturing and analyzing the necessary data only became plausible with the advent of predictive analytics.

Using predictive analytics for disease management requires healthcare organizations to pool extensive patient data — including EHRs, genomics, social determinants of health (SDOH), and other information — to identify relevant trends. These insights can then be used as a starting point to guide early disease detection and diagnosis efforts, anticipate disease progression, flag high-risk patients, and optimize treatment plans and resource allocation.

The promise of big data and predictive analytics is valuable in infectious disease monitoring.

In a February 2024 PLOS One study, researchers from the University of Virginia detailed the development of an online big data dashboard to track enteric infectious disease burden in low- and middle-income countries.

The dashboard is part of the Planetary Child Health & Enterics Observatory (Plan-EO) initiative, which aims to provide an evidence base to help geographically target child health interventions.

The dashboard will pull data from various sources to map transmission hotspots and predict outbreaks of diarrheal diseases, which public health stakeholders can use to better understand disease burden and guide decision-making.

The impacts of infectious disease are often inequitable, which may lead some to question the role that predictive analytics plays in concerns about health equity. Like any advanced data analytics approach, these tools must be used responsibly to avoid perpetuating health disparities, but when used responsibly, predictive tools can positively impact equity efforts.


Care disparities, bias and health inequity are rampant in the United States healthcare system. Researchers and clinicians are on the front lines of efforts to ensure that patients receive equitable care, but doing so requires healthcare stakeholders to gain a deep, nuanced understanding of how factors like SDOH impact patients.

Predictive analytics can help draw a wealth of information from the large, complex data needed to guide these efforts.

The health of those in marginalized communities is disproportionately impacted by housing, care access, social isolation and loneliness, food insecurity, and other issues. Effectively capturing data on these phenomena and designing interventions to address them is challenging, but predictive analytics has already bolstered these efforts.

Recently, researchers from Cleveland Clinic and MetroHealth were awarded over $3 million from the National Institutes of Health (NIH) to develop a digital twin-based, neighborhood-focused model to reduce disparities.

The Digital Twin Neighborhoods project uses de-identified EHR data to design digital replicas of real communities served by both organizations. Experts on the project indicated that by pulling geographic, biological, and SDOH information, researchers can better understand place-based health disparities.

Models developed using these data can simulate life course outcomes in a community. Tools that accurately predict the outcomes observed within a population’s EHRs can inform health equity interventions.

In 2021, United Healthcare launched a predictive analytics-based advocacy program to help address SDOH and improve care for its members. The system uses machine learning to identify individuals who may need social services support.

These insights are incorporated into an agent dashboard that member advocates can use, alongside more traditional tools like questionnaires, to gather more information from the patient about their situation. If necessary, the advocate connects the individual with support mechanisms.

Efforts like these also demonstrate the utility of predictive analytics tools in patient and member engagement.


Patient engagement plays a vital role in enhancing healthcare delivery. The advent of big data analytics in healthcare provides many opportunities for stakeholders to actively involve patients in their care.

Predictive analytics has shown promise in allowing health systems to proactively address barriers to patient engagement, such as appointment no-shows and medication adherence.

In a 2021 interview with HealthITAnalytics, Community Health Network leadership detailed how the health system bolsters its engagement efforts by using predictive analytics to reduce appointment no-shows and conduct post-discharge outreach.

A key aspect of this strategy is meeting patients where they are to effectively individualize their care journeys and improve their outcomes.

Appointment no-shows present a significant hurdle to achieving these aims, leading Community Health Network to implement automated, text message-based appointment reminders, with plans to deploy a two-way communication system to streamline the appointment scheduling process further.

The health system took a similar approach to post-discharge outreach, successfully deploying an automated solution during the COVID-19 pandemic.

To further enhance these systems, Community Health Network turned to predictive analytics.  By integrating a predictive algorithm into existing workflows, the health system could personalize outreach for appointment no-shows. Patients at low risk for no-shows may receive only one text message, but those at higher risk receive additional support, including outreach to determine whether unmet needs that the health system can help address are preventing them from making it to appointments.

Data analytics can also support medication adherence strategies by identifying non-adherence or predicting poor adherence.

One 2020 study published in Psychiatry Research showed that machine learning models can “accurately predict rates of medication adherence of [greater than or equal to 80 percent] across a clinical trial, adherence over the subsequent week, and adherence the subsequent day” among a large cohort of participants with a variety of conditions.

Research published in the March 2020 issue of BMJ Open Diabetes Research & Care found that a machine learning model tasked with identifying type 2 diabetes patients at high risk of medication nonadherence was accurate and sensitive, achieving good performance.

Outside the clinical sphere, predictive analytics is also useful for helping organizations like payers meet their strategic goals.


Payers are an integral part of the US healthcare system. As payer organizations work with providers to guide members’ care journeys, they generate a wealth of data that provides insights into healthcare utilization, costs, and outcomes.

Predictive analytics can help transform these data and inform efforts to improve payer forecasting. With historical data, payers can use predictive modeling to identify care management trends, forecast membership shifts, project enrollment churn, and pinpoint changes in service demand, among other uses.

In June 2023, leaders from Elevance Health discussed how the payer’s emphasis on predictive analytics is key to improving member outcomes.

Elevance utilizes a predictive algorithm to personalize member experience by addressing diabetes management and fall risk. The predictive model pulls clinical indicators like demographics, comorbidities, and A1C levels to forecast future A1C patterns and identify individuals with uncontrolled or poorly controlled diabetes.

From there, the payer can help members manage their condition through at-home lab A1C test kits and increased member and care team engagement.

The second predictive tool incorporates data points — including past diagnoses, procedures, and medications, the presence of musculoskeletal-related conditions and connective tissue disorders, analgesic or opioid drug usage, and frailty indicators — to flag women over the age of 65 at higher risk of fracture from a fall.

Elevance then conducts outreach to these individuals to recommend bone density scans and other interventions to improve outcomes.

These efforts are one example of how predictive analytics can improve the health of specific populations, but these tools can also be applied to population health more broadly.


While much of healthcare is concerned with improving individual patients’ well-being, advancing the health of populations is extremely valuable for boosting health outcomes on a large scale. To that end, many healthcare organizations are pursuing data-driven population health management.

Predictive analytics tools can enhance these initiatives by guiding large-scale efforts in chronic disease management and population-wide care coordination.

In one 2021 American Journal of Preventive Medicine study, a research team from New York University’s School of Global Public Health and Tandon School showed that machine learning-driven models incorporating SDOH data can accurately predict cardiovascular disease burden. Further, insights from these tools can guide treatment recommendations.

The early identification of chronic disease risk is also helpful in informing preventive care interventions and flagging gaps in care.

Being closely related to population health, public health can also benefit from applying predictive analytics.

Researchers from the Center for Neighborhood Knowledge at UCLA Luskin, writing in the International Journal of Environmental Health in 2021, detailed how a predictive model successfully helped them identify which neighborhoods in Los Angeles County were at the greatest risk for COVID-19 infections.

The tool mapped the county on a neighborhood-by-neighborhood basis to evaluate residents’ vulnerability to infection using four indicators: barriers to accessing health care, socioeconomic challenges, built-environment characteristics, and preexisting medical conditions.

The model allowed stakeholders to harness existing local data to guide public health decision-making, prioritize vulnerable populations for vaccination, and prevent new COVID-19 infections.

Alongside large-scale initiatives like these, predictive modeling can also support the advancement of precision medicine.


The emergence of genomics and big data analytics has opened new doors in the realm of tailored health interventions. Precision and personalized medicine rely on individual patients’ data points to guide their care and improve their well-being.

From cancer to genetic conditions, predictive analytics is a crucial aspect of precision medicine.

In 2021, a meta-analysis presented at the American Society for Radiation Oncology (ASTRO) Annual Meeting showed that a genetic biomarker test could accurately predict treatment response in men with high-risk prostate cancer.

The test analyzes gene activity in prostate tumors to generate a score to represent the aggressiveness of a patient’s cancer. These insights can be used to personalize treatment plans that balance survival risk with quality of life.

Researchers from Arizona State University (ASU) revealed in a 2024 Cell Systems paper that they developed a machine learning model to predict how a patient’s immune system will respond to foreign pathogens.

The tool uses information on individualized molecular interactions to characterize how major histocompatibility complex-1 (MHC-1) proteins — key players in the body’s ability to recognize foreign cells — impact immune response.

MHC-1s exist on the cell surface and bind foreign peptides to present to the immune system for recognition and attack. These proteins also come in thousands of varieties across the human genome, making it difficult to forecast how various MHC-1s interact with a given pathogen.

The ASU research addressed this by analyzing just under 6,000 MHC-1 alleles, shedding light on how these molecules interact with peptides and revealing that individuals with a diverse range of MHC-1s were more likely to survive cancer treatment.

Using the model, providers could potentially forecast pathological outcomes for patients, bolstering treatment planning and clinical decision-making.

In addition to these successes at the microscopic level, predictive analytics is also useful on the macro level in healthcare.


Optimization of the supply chain and resource allocation ensures that providers and patients receive the equipment, medications, and other tools that they need to support positive outcomes. Data analytics plays a massive role in this, as supply chain management and resource use rely heavily on accurately recording and tracking resources as they move from the assembly line into the clinical setting.

Predictive analytics takes this one step further by helping stakeholders anticipate and address supply chain issues before they arise while optimizing resource use.

Seattle Children’s Hospital is using predictive modeling in the form of digital twins to help the health system streamline hospital operations, particularly resource allocation.

By using digital twin simulation to “clone” the hospital, stakeholders can model how certain events, strategies, or policies might impact operational efficiency. This capability was critical in the wake of COVID-19, as it allowed the health system to identify how rapidly its personal protective equipment (PPE) supplies would diminish, forecast bed capacity, and generate insights around labor resources.

Predictive analytics can also be used by distinct parts of the supply chain to help prevent shortages.

The 2022 infant formula shortage is one example of how supply chain disruptions can significantly impact health.

One potential way for parents to deal with the formula shortage was to turn to human breast milk banks, which distribute donated milk to vulnerable babies and their families. However, accomplishing this vital work requires milk banks to effectively screen donors, accept donations, process and test them to ensure they’re safe, and dispense them.

In an interview with HealthITAnalytics, stakeholders from Mothers’ Milk Bank at WakeMed Health & Hospitals described how data analytics can help optimize aspects of this process.

A crucial part of ensuring that milk is available to those who need it is tracking milk waste. Milk can be wasted for various reasons, but the presence of bacteria is one of the primary causes. To address this, the milk bank began analyzing donor records to determine what factors may make a batch of milk more likely to test positive for bacillus.

The milk bank can then use the insights generated from the analysis to predict which donors may be at high risk for having bacillus in their milk, allowing milk from these individuals to be tested separately. This removes any bacillus-positive samples before the milk is pooled for processing.

Predictive analytics is also helpful in assessing and managing risks in clinical settings.


Patient risk scores have the potential to improve care management initiatives, as they allow providers to formulate improved prevention strategies to eliminate or reduce adverse outcomes. Risk scores are used to help understand what characteristics may make a patient more susceptible to various conditions.

From there, the scores can inform risk stratification efforts, which enables health systems to categorize patients based on whether they are low-, medium- or high-risk. These data can show how one or more factors increase a patient’s risk.

Risk stratification is one of the most valuable use cases for predictive analytics because of its ability to prevent adverse outcomes.

In February 2024, leaders from Parkland Health & Hospital System (PHHS) and Parkland Center for Clinical Innovation (PCCI) in Dallas, Texas, detailed one of these high-value use cases.

Parkland’s Universal Suicide Screening Program is an initiative designed to flag patients at risk of suicide who may have flown under the health system’s radar through proactive screening of all Parkland patients aged 10 or older, regardless of the reason for the clinical encounter.

During the encounter, nursing staff ask the patient a set of standardized, validated questions to assess their suicide risk. This information is then incorporated into the EHR for risk stratification.

These data are useful for stakeholders looking to better understand patients’ stories, including factors like healthcare utilization before suicide. Coupling these insights with state mortality could help predict and prevent suicide in the future.

Risk stratification is also crucial for improving outcomes for some of the youngest, most vulnerable patients: newborns.

Parkland also runs an initiative that uses SDOH data to identify at-risk pregnant patients and enable early interventions to help reduce preterm births.

The program’s risk prediction model and text message-based patient education program have been invaluable in understanding the nuances of preterm birth risk for Parkland patients. Major risk factors like cervical length and history of spontaneous preterm delivery may not be easy to determine for some patients. Further, many preterm births appear to be associated with additional risk factors outside of these – like prenatal visit attendance.

Using these additional factors to forecast risk, Parkland has developed clinical- and population-level interventions that have resulted in a 20 percent reduction in preterm births.

These use cases, among other things, demonstrate the key role predictive analytics can play in advancing value-based care.


Value-based care incentivizes healthcare providers to improve care quality and delivery by linking reimbursement to patient outcomes. To achieve value-based care success, providers rely on a host of tools: health information exchange (HIE), data analytics, artificial intelligence (AI) and machine learning (ML), population health management solutions, and price transparency technologies.

Predictive analytics can be utilized alongside these tools to drive long-term success for healthcare organizations pursuing value-based care.

Accountable care organizations (ACOs) are significant players in the value-based care space, and predictive modeling has already helped some achieve their goals in this area.

Buena Vida y Salud ACO partnered with the Health Data Analytics Institute (HDAI) in 2023 to explore how predictive analytics could help the organization keep patients healthy at home.

At the outset of the collaboration, the ACO’s leadership team was presented with multiple potential use cases in which data analysis could help with unplanned admissions, worsening heart failure, pneumonia development, and more.

However, providers were overwhelmed when given risk-stratified patient lists for multiple use cases. Upon working with its providers, the ACO found that allowing clinicians to choose the use cases or patient cohorts they wanted to focus on was much more successful.

The approach has helped the ACO engage its providers and enhance care management efforts through predictive modeling and digital twins. These tools provide fine-grain insights into the drivers of outcomes like pneumonia-related hospitalization, which guide the development of care management interventions.

These 10 use cases are just the beginning of predictive analytics’ potential to transform healthcare. As data analytics technologies like AI, ML and digital twins continue to advance, the value of predictive analytics is likely to increase exponentially.


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