Predictive Analytics in Healthcare: Top Benefits and Use Cases

What’s the purpose of adopting modern technologies and tools in the healthcare sector? The obvious answer will be achieving excellence in workflows and improving patient care. But what comes to your mind when you hear the word modern technologies? Your answers may differ here.

Predictive analytics is a subset of AI that utilizes machine learning techniques and data mining. Using these techniques, it analyzes historical data and predicts future events. It is quite beneficial for the healthcare sector as it enables providers to offer improved patient care. Not only does it raise the standards of patient care, but it also reduces the costs.

According to Fortune Business Insights, the global healthcare predictive analytics market was $12.96 billion in 2023 and is expected to grow and reach $184.58 billion by 2032. These numbers show the growing interest of organizations in predictive analytics solutions.

In this article, we will learn about predictive analytics benefits and use cases in healthcare. So, without further delay, let’s get into it.

What are the benefits of predictive analytics in healthcare?

The healthcare industry deals with a massive amount of data. None of us would have thought this data could offer numerous benefits. Predictive analytics utilizes this data and offers innumerable benefits to healthcare organizations. However, partnering with an AI healthcare company is crucial to reap predictive analytics benefits.

Here are some benefits of predictive analytics that you can get for your healthcare practice:

1.   Better Patient Care

You must be wondering what predictive analytics have to do with patient care. Let us tell you what predictive analytics do. Predictive analytics analyzes current and past data to predict future scenarios.

Healthcare providers can use this technology to understand health patterns and know which patients require instant care to eliminate any unfortunate scenarios. Once the caregiver has the insights, they can make data-driven decisions.

Moreover, it allows providers to offer personalized medicines and know which treatment will yield the best patient health outcomes. According to BMJ, AI-based predictive analytics tools facilitate clinical decision support for improved patient care and reduce caregiver burnout.

2.   Medical Equipment Maintenance Prediction

Equipment is of significant importance in the healthcare industry. Many industries, including telecommunication, leverage predictive analytics to identify any potential issues before they occur.

Today, healthcare can also leverage predictive analytics to ensure that medical equipment is in good condition and won’t fail during treatments. Predictive analytics in healthcare helps replace or repair the types of medical equipment before failure. It enables continuity of care for better health outcomes.

3.   Fraud Detection

Fraud is happening in almost every industry. Scammers have found new methods of scamming to evade detection. Moreover, humans can make errors and overlook pivotal trends that result in substantial financial losses. To overcome such scenarios, healthcare organizations require a technological solution to analyze patterns and identify potential fraud.

Predictive analytics emerges as a solution to address this healthcare challenge. Healthcare providers can use predictive tools to identify suspicious transactions and patterns. These flagged transactions can then be further investigated by a human agent before proceeding with transactions.

4.   Cut Costs

Suppose you are running a healthcare organization; won’t you want to reduce costs while delivering improved patient care? Not only you but even the professionals in the business want to achieve both.

Predictive analytics helps to reduce massive amounts of healthcare costs. Things are connected together, which results in reduced overall costs. For example, it allows the prevention of fraud, allocates resources efficiently, reduces human errors, improves logistics and supply chain management, reduces patient readmission, etc. All this will help to save costs and generate massive revenue.

According to a report by BMJ, the rate of patient readmission is significantly lower when using predictive analytics in healthcare. The fewer the patient readmission, the lower the expenses.

Top 4 Use Cases of Predictive Analytics in Healthcare

The healthcare industry is changing the way it operates. One big reason behind this transformation is predictive analytics. Here are some use cases of predictive analytics in healthcare:

1.   Providing Clinical Decision Support

One of the primary applications of predictive analytics in healthcare is clinical decision support. Predictive models assist clinicians in making more informed decisions.  But how do they do so? They analyze vast amounts of patient data, including medical history, lab results, and genetic information.

Using insights from these datasets, these predictive models forecast conditions’ likelihood or suggest optimal treatment plans and anticipate complications before they arise – improving not only the accuracy and efficiency of diagnoses but also patient outcomes with tailored medicine solutions.

2.   Reducing No-Shows

Patient no-shows can be an enormous drain on healthcare facilities. It leads to wasted resources and extended wait times for other patients. Healthcare practices require an efficient solution to overcome this challenge.

Predictive analytics offers one possible solution by identifying patterns and risk factors associated with missed appointments. It analyzes past attendance records and demographic information to empower healthcare providers to develop targeted interventions.

An effective proactive solution may include:

• Sending reminders.
• Offering flexible scheduling options.
• Offering transportation assistance for patients at high risk for no-show appointments.

Such actions may help lower missed appointment rates significantly while improving resource utilization and patient care continuity.

3.   Preventing Readmissions

Readmission prevention should be the number one goal of healthcare providers, as readmissions can be both costly and cause logistical challenges that make hospital beds scarcer and complicate care provision.

Predictive analytics equip physicians and clinicians with the ability to quickly identify patients at high risk for readmission and those who may need extended hospital stays.

Armed with these insights, doctors can tailor their treatment strategies accordingly and ensure patient recovery proceeds smoothly while decreasing complications.

4.   Accelerating Insurance Reimbursements

Insurance reimbursement processes can be lengthy and complex. Moreover, it often comes with delays and errors. Predictive analytics is a useful technology for shortening this process by predicting the likelihood of claim approval based on historical data analysis of factors.

Predictive models can identify potential issues before claims are filed. It allows healthcare providers to correct errors, ensure documentation is complete, and optimize coding practices before claims are even submitted. All this reduces the likelihood of claim denials and speeds reimbursement, thereby improving cash flow and financial stability for healthcare organizations.

Conclusion

By now, you might be familiar with the potential of predictive analytics in healthcare. Not only does it have benefits for the patients but also the providers. Providers can now foresee the future and make informed decisions to achieve excellence in operations and patient care. We can confidently say that the future of the healthcare industry looks promising with further advancements in technology.

Suppose you are a startup or an organization that is looking to raise its standards. In that case, you must get in touch with reliable AI development companies to address your challenges and succeed.