How AI Is Improving Predictive Analytics in Healthcare?

How AI is Improving Predictive Analytics in Healthcare?

Medical professionals have always done their best to provide the best care for their patients.

However, they are only human, with only so much time and energy, and there is so much information to consider at one time, including a patient’s medical history.

This is where AI (Artificial Intelligence) comes into play.

AI has already contributed to our lives in a number of ways. People use it to ask a question on their smartphone via Siri, protect their family with home security, and even operate self-driving cars.

However, AI has a large role to play in healthcare.

In this article, we will explore AI-based healthcare solutions, particularly with regard to improving predictive analytics. Let’s start with the first example.

Predict Risk of Stay-At-Home Patients

The COVID-19 pandemic has caused many people to stay in their homes to avoid the risks of contact and spreading the virus.

However, this does not mean their conditions don’t need to be monitored just the same.

Luckily, patients can feed data to apps and other reporting platforms.

This can then be analyzed to predict the risk of these patients being readmitted, allowing healthcare institutions, especially those that are at the forefront of tackling the pandemic, to predict how much capacity will be needed to handle the possible influx of patients.

Prevent Equipment Maintenance Issues

Modern hospitals and healthcare providers have large amounts of equipment to maintain.

This equipment is necessary to diagnose, treat, and improve patient conditions. However, it is also quite expensive.

AI can be integrated with this healthcare equipment to analyze components.

By doing so, you can predict when certain parts need to be maintained or replaced.

This prevents downtime, while also ensuring you don’t put patients in danger by lacking access to the proper tools in a timely manner.

Covering up Defects

Improper allocation of healthcare resources is one of the biggest problems that organizations face.

Medical practitioners, while experts in their field of medicine, are not often equipped to judge the demand for particular resources, including staffing.

This can cause an overflow in certain departments, such as the emergency ward, which places additional stress on the staff and reduces the quality of patient outcomes.

Thanks to AI predictive analytics, healthcare institutions can better manage their resource allocation. The end result is fewer fluctuations and more stable patient care.

Predicting Spread in Case of Pandemics

Even just a few years ago, it would have been impossible to predict an epidemic. However, AI’s influence on predictive analytics has made it a reality today.

Health organizations can accurately predict infectious diseases.

This is due to data such as reported cases, population density, and even economic factors.

The learning models have now been improved from the data surrounding COVID-19. It can even be translated into predicting what kind of care and equipment will be necessary.

Optimizing Drug Distribution

Healthcare institutions need to have access to the proper medicine on-site.

However, previously this was a difficult equation to manage. Now, machine learning can help guide decisions surrounding the distribution of these life-saving drugs.

For instance, analytics may predict a rise in heart-attack patients, which will instruct institutions that offer cardiological care to stock up on certain medications for treatment.

This raises the patient care level and improves the long-term quality of life.

Predict Chronic Disease Probability

The world population continues to rise, and that means that the well-being of the public is ever-more important when it comes to preventing chronic diseases.

Having more patients with such chronic ailments contributes to rising costs across the board.

AI can be used to accurately predict the possibility of developing chronic diseases such as ALS.

Discovering these conditions early on can have profound effects on patient health.

Machine learning combined with predictive analytics can help determine the best course of care for these patients using biometrics and health records.

IoT (Internet of Things) and Wearables

AI can be used in combination with IoT devices and wearables today to enable the health industry to gather more information.

The data can also be analyzed in large amounts more quickly. This includes heart monitors, smartwatches, and more.

Through this analysis, professionals can offer guidelines to optimize the patient experience.

These technologies are some of the greatest benefits of Industry 4.0 and show promising applications going forward.

Conclusion

As the world becomes faster, so too must our healthcare institutions. However, this is often easier said than done.

Medical professionals only have so much time and attention on their hands.

AI can be utilized to relieve a lot of stress and more mundane tasks of healthcare.

It can also identify and predict issues more accurately than humans in many cases. Finally, it is predicted that AI will save the healthcare industry $150 billion by 2026.

The examples above show the importance of using AI to improve health outcomes and save money, as well as the importance for organizations to leverage this technology in providing better service.

Author:

Joe Peters is a Baltimore-based freelance writer and an ultimate techie. When he is not working his magic as a marketing consultant, this incurable tech junkie devours the news on the latest gadgets and binge-watches his favorite TV shows. Follow him on @bmorepeters

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