Predictive Analytics in Healthcare: Use Cases & Examples

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From retail to healthcare, big data has left no industry untouched. This wealth of data has paved a path for predictive analytics, allowing modern organizations to make accurate predictions about the future and power their decision-making with actionable insights. 

What is predictive analytics in healthcare?

In healthcare, predictive analytics uses real-time and historical data to make predictions about future health trends, anticipate patient needs, and help healthcare organizations run more efficiently. Predictive analytics solutions ingest big data from electronic health records (EHR), insurance and administrative records, and other data sources that are a part of the healthcare ecosystem. 

This type of advanced analytics leverages statistical modeling, data mining, and machine learning to deliver new insights. Healthcare organizations can apply these insights to everything from chronic disease management to lowering hospital readmission rates.

Why is predictive analytics important for healthcare?

Predictive analytics is important for both clinical care and operational efficiency. When care providers can make informed predictions about health risks, they can offer better treatment plans that keep patients healthier for longer – and at a lower cost. 

Recent research has shown how predictive analytics that uses social and environmental patient data provides better risk estimates for cardiovascular disease. With these insights, clinicians can prevent at-risk patients from developing more severe symptoms that could lead to hospitalization.

On the operational side, predictive analytics enables healthcare providers to optimize capacity management and tackle staffing shortages. As many as 94% of registered nurses report a moderate to severe nurse shortage in their location, while 80% expect the shortage to get worse within five years. Predictive analytics can improve how hospitals schedule their staff to ensure they’re efficiently allocating all of their resources. 

Predictive analytics are also much needed to improve the response of public health authorities to viral outbreaks and prevent the spread of infections. In the case of influenza, for example, predictive analytics has already been used to forecast the spread of the virus on a state and local level.

The current state of predictive analytics in healthcare

Although 95% of physician groups and hospitals have access to advanced analytics to support their decision-making, 80% of healthcare managers report that the usage of these tools is “negligible.” One reason is the lack of understanding of how advanced analytics benefits the community and the healthcare organization.

Researchers and hospitals that have used predictive analytics, however, were able to gain important insights and see meaningful improvements in patient outcomes.

Examples of predictive healthcare analytics in use

The use of predictive analytics provides healthcare organizations with a better understanding of how a patient will respond to treatment and whether they are at a higher risk of developing complications. The three examples below illustrate the importance of advanced analytics for both patients and healthcare providers.

1. Predicting COVID-19 patient outcomes

A 2021 study investigated whether machine learning models can forecast the intensity of sickness in COVID-19 patients. The results showed that machine learning could accurately forecast clinical severity using just the data gathered within the first 24 hours after hospitalization, confirming that pH was the factor “most strongly associated with clinical severity.”

2. Reducing hospital readmissions

UnityPoint Health, a network of hospitals, clinics, and home care providers, reduced readmissions by 40% in 18 months with predictive analytics. In one example, a physician used predictive analytics to discover that their patient’s symptoms would likely return in 13 to 18 days and advised the patient to contact the practice when this happened. Once the symptoms returned, the physician immediately switched the patient’s medication and prevented rehospitalization.

3. Improving clinical outcomes

Home healthcare providers that use WellSky CareInsights, a predictive analytics tool, see lower hospitalization rates and better care efficiency. Providers that leveraged the tool on a daily basis over three years experienced 26% lower hospitalization rates and a 45% reduction in visits per admission.

Benefits of predictive analytics in healthcare

In addition to reducing readmissions and improving patient outcomes, predictive analytics models offer many other benefits.

1. Effective resource allocation

By effectively allocating healthcare resources, providers reduce burnout and ensure they’re maximizing their capacity – a critical benefit for any provider with a staff shortage. The Gundersen Health System, for example, used predictive analytics powered by artificial intelligence to increase room utilization by 9%. 

Predictive analytics solutions can also identify patients who are likely to miss their appointments to prevent that time slot from going to waste. Over time, effective resource allocation will lead to cost savings for the organization and improve profitability.

2. Disease prevention and early detection

Healthcare professionals and health insurance companies use predictive algorithms to understand the likelihood of a patient developing an illness. Once healthcare providers detect this risk, they can offer treatment plans to help the patient course correct. 

The same predictive models can be applied to detecting early disease symptoms to deliver care before the onset of more severe symptoms that require more expensive treatments.

3. Matching patients with providers and treatments

With access to robust patient data, predictive analytics tools can connect patients with medical professionals with the right skills and experience to treat their condition. Similarly, predictive models can analyze a patient’s medical history and support physicians in creating a tailored treatment plan.

Challenges and limitations of implementing predictive analytics in healthcare

Healthcare services have a lot of moving parts, from insurance to care delivery. Furthermore, the data is fragmented across various systems and devices – EHRs, remote patient monitoring devices, imaging equipment, etc. This makes data integration a challenging endeavor for healthcare organizations, requiring the help of data experts who will shepherd the data through the integration process and ensure that predictive models are ingesting only high-quality data.

Data storage is another potential challenge both in terms of security and compliance. Regardless of whether they choose cloud-based, on-premise, or hybrid data storage, healthcare providers must comply with HIPAA and protect patients’ Protected Health Information (PHI). In the case of cloud-based storage, for example, you must sign a business associate agreement with the storage vendor before uploading PHI to the cloud.

It’s also important to point out that predictive analytics tools are limited by the data they’re trained on. Historical data can be used to make accurate predictions about the future, as was the case in the machine learning models that predicted COVID-19 patient outcomes, but there is no guarantee that they will be correct 100% of the time.

Take patient care to the next level with Twilio Segment

Twilio Segment’s customer data platform (CDP) can help healthcare organizations leverage real-time data to improve the patient experience, while remaining HIPAA compliant. 

First, Connections unifies data from web sources, mobile apps, and offline channels to better understand people’s needs and behavior. Then, with Protocols, businesses are able to ensure data governance at scale by automatically detecting and classifying personally identifiable information (PII). Teams can also set rules to automatically block personal data from being collected, or implement role-based access control.

Learn more about The Future of Healthcare Technology in this free webinar.