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10 Predictive Analytics Use Cases & Examples In Healthcare Industry

The healthcare industry is ripe for predictive analytics. Companies are looking for ways to improve care and reduce costs, but they have...

Written by Niel Patel · 5 min read >
example of predictive analytics in healthcare

The healthcare industry is ripe for predictive analytics. Companies are looking for ways to improve care and reduce costs, but they have yet to have all the answers. Predictive analytics can help them do just that. It’s difficult to say exactly how this will play out in practice; there are many unanswered questions about what kind of data companies need to analyze, how much time it takes (and whether it even makes sense), and whether the technology will prove itself efficient enough at improving patient outcomes given its cost-benefit ratio.

Examples and Use Cases of Predictive Analytics in Healthcare

Electronic health record systems (EHRs) can show which patients are most likely to not show up for their appointments. A study from Duke University found that using clinic-level EHR data for predictive modelling in healthcare could catch almost 5,000 more patients who don’t show up each year with more accuracy than previous attempts to predict patient patterns.

1. Dynamic Pricing

Using predictive analytics for healthcare, companies can set dynamic prices, which is a way to maximize revenue. Dynamic pricing can improve patient care by predicting demand for a product or service and boosting revenue. 

For example: If you sell insurance plans based on your customer’s age and gender patterns, you could use predictive analytics to determine how much they should pay for their coverage. You’ll know what kind of financial incentive they need—and then adjust your prices accordingly!

Dynamic pricing models are the best example of predictive analytics in healthcare. It is often used with data from other areas of the healthcare industry as well—such as marketing campaigns or patient satisfaction surveys—to generate insights that help guide decisions about offering products at different prices depending on who’s buying them (or not).

2. Helps Healthcare Fraud Analytics

Healthcare fraud analytics can detect and report fraudulent claims, overpayments, underpayments and improper billing practices. These include:

  • Improper payments
  • Improper billing practices (e.g., billing for services not provided or rendered by unqualified personnel)

Current techniques for detecting healthcare fraud are ineffective. Finding it before claims are paid is the more efficient way to stop fraud and abuse. And because other sectors of the economy have already proven predictive analytics’ effectiveness, healthcare payers have started to do the same.

3. Boosts Personalized Medicine

By employing predictive analysis, healthcare workers can make improvements in the personalized medicine field also. Personalized medicine uses information from a patient’s DNA to guide their care. It’s a new way to treat disease and an emerging field of medicine that uses genetic information to make treatment decisions.

Predictive analysis can make personalized medicine more accurate in providing the right treatment based on an individual’s genetic makeup at the right time. For example, some patients may be prescribed medications that work better in others with similar traits. With predictive analysis, personalized medicine can reduce side effects or even cure certain diseases altogether.

4. Guides To Provide Ideal Treatments 

Predictive analysis can help personalize treatments for some conditions, such as cancers, and achieve the best outcomes.

Predictive analytics technology can analyze all the data needed to make those treatment decisions because no one person can. Although still in its infancy, this technology has become able to analyze, for instance, the genomics of particular cancer and the patient diagnosed with the disease to foretell the best course of treatment. This ability becomes especially important for treating advanced diseases because doctors don’t need more time to test various treatment options before selecting the most effective one.

5. Predictive Analytics Can Improve Healthcare Operations

Predictive analytics can help healthcare organizations with a variety of processes. One of these processes is improving general healthcare operations. It is the one of the best predictive analytics in healthcare examples that has brought benefit to the healthcare orgnizations. For example, predictive analytics can help healthcare organizations with customer service and marketing. It can improve sales, marketing, and supply chain management.

6. Analyzes The Risk Of A Chronic Disease

Risk scoring is a way to predict the likelihood of a patient developing a particular disease or condition. This can work as part of decision support, where it identifies patients at risk of developing certain diseases and conditions.

7. Helps Avoiding 30-Day Hospital Readmissions

One of the most important things that predictive analytics can do for your hospital is to help you avoid 30-day readmissions. This is especially true because it’s estimated that a patient who has been discharged from a hospital and then returns within 30 days is three times more likely to die than someone who stays healthy after leaving the hospital.

In this case, predictive analytics will use data collected over time to identify patients who may need additional care or monitoring to successfully avoid their second trip back into the medical system—thus reducing costs associated with those visits.

8. Better Resource Allocation 

Resource allocation has become challenging for administrators due to healthcare organizations’ size, scope, and complexity. Patient utilization patterns, the organization’s overall capability, and resources — those used to be distinct places now coming together in a really productive way to help organizations manage their operations. Predictive analytics assists organizations in greatly improving the management of their operations

However, using predictive analytics, administrators can acquire or relocate the appropriate resources to the appropriate location at the appropriate time by spotting patterns in resource allocations and anticipating future needs.

9. Helps Healthcare Providers To Stay Ahead Of Patient Deterioration

Patient deterioration is a common occurrence in healthcare. It can occur due to several things, including:

  • Illness or injury
  • Age and lifestyle changes that make the patient less able to care for themselves (e.g., loss of mobility)
  • New diagnosis or treatment for an existing condition

A new diagnosis or treatment for an existing condition can significantly impact patients’ ability to care for themselves. This is especially true if they are dealing with multiple chronic conditions and taking many medications.

10. Reducing No-Shows On Appointments

If you’re a healthcare provider, it’s important to know when your patients will stay absent from appointments. This can affect the quality of care and lead to financial losses for your practice. It’s also important for the patients themselves. If they don’t show up for an appointment, they might miss out on potentially life-saving treatment or other services that could help them manage their health better.

If this sounds like something that interests you, consider looking at predictive analytics as a way of forecasting no-shows in your patient population. Duke University recently published research showing how EHR data can help predict future appointments by identifying those most likely not to show up when it matters most (e.g., during the flu season).

11. Benefits In Insurance Claims

Employing predictive analysis, healthcare organizations can examine the supporting documentation they send insurers to spot claims denials and those that might result in higher payouts. The technology optimizes financial performance by looking for missing or insufficient reimbursement codes and identifying an opportunity to upcode.

12. Minimize Suicide And Patient Self-Harm

One of the most important predictive analytics healthcare examples is suicide and self-harm prevention. Healthcare providers can assess a patient’s risk of suicide or self-harm using predictive analytics. It is possible to specify patients at risk for these behaviors and intervene before they take action.

In this case, predictive models are built using historical data on past events to predict future ones based on the same variables. Clinicians can then use these models to identify potential mental health issues that lead a patient down a dangerous path toward committing suicide or harming themselves while under treatment at your facility.

This process also works well with other tools, such as behavioral analysis. It allows clinicians to work with complicated cases like substance abuse or dementia. Right now, there isn’t a need to be more resources available in order to monitor patient behavior over time properly. But, still, keep tabs on how these individuals are doing overall. With the help of this constant monitoring, you know when things start getting out of control without needing constant supervision from someone else all day!

13. Bolstering Patient Engagement And Satisfaction

Patient engagement has become the core of healthcare business success, and predictive analytics can help you leverage it. If you want your patients to engage with your organization and its services, getting them to see the value in what you do can be one of the most significant factors in their decision-making process.

Patient satisfaction plays a part as another key factor that needs serious attention when it comes to patient engagement. Predictive analytics has an integral role here too. By using smart data analysis techniques such as machine learning or artificial intelligence (AI), companies can gain insight into how satisfied their customers are with specific products or services they offer. It will then allow them to improve upon those offerings over time. Hence, as not only keep up with demand but also create new industries within themselves!

14. Aids In Supply Chain Management 

Just like businesses in most other industries, healthcare organizations use predictive tools to improve the management of their supply chains. Supply chain management helps organizations gain visibility in the healthcare industry and provide effective services. Healthcare organizations could gain visibility into their enormous and complex supply requirements and forecast. Predictive analysis guides them to streamline purchases and vendor consolidations resulting in cost savings, waste reduction, and efficiency gains.

Wrapping Up!

Hoping that you enjoyed learning about some of the use cases for predictive analytics in healthcare. This is all about this guide. The Healthcare industry has seen more and more organizations adopt these technologies, which will help them improve their operations and save time and money. So, now it is your turn to use this technology.

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