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The Health Benefits of Synthetic Data

The Health Benefits of Synthetic Data


The Health Benefits of Synthetic Data

How much money does your organization waste on collecting, storing, and maintaining real-world data? Many companies are finding that they simply don't have the resources to collect and store the data necessary to solve their unique problems. Thankfully, there's an alternative solution: synthetic data. Synthetic data looks like real-world data but requires less maintenance and allows you to focus on other aspects of your project or product. Find out how synthetic data can help your organization save time and money today!

What is synthetic data?

So what exactly is synthetic data, and why should you use it? According to Accenture, synthetic data is a copy or simulation of real-world data that can be used for analysis and testing purposes. Not only does synthetic data save organizations time, but it also reduces costs by requiring less setup time when performing testing. In addition to cost savings, synthetic data can also increase a business's privacy compliance. For example, instead of using real patient information when carrying out test iterations involving sensitive health information, synthetic copies allow researchers to freely experiment without worrying about potentially violating privacy laws or compromising patient information. Overall, synthetic data presents a huge opportunity for healthcare organizations worldwide to harness its benefits and improve their daily operations. Here are some ways synthetic data can positively impact your organization:

Accenture states that synthetic data has significant potential to enable research and development in an increasingly digital world. Organizations such as pharmaceutical companies, medical device manufacturers, hospitals, insurers and software developers are currently turning to synthetic data because of its many advantages over traditional methods. It's estimated that each year between $15 billion and $20 billion is spent on healthcare R&D alone (which doesn't include product development). Given these staggering numbers, it makes sense for companies in any industry vertical to take advantage of all tools available—including synthetic data—to get results more quickly while reducing costs at every step along the way.

Why use synthetic data?

The answer to one of data scientists' biggest concerns in healthcare is right here, right now. It might sound like a cruel joke, but a growing amount of synthetic data is needed to train and improve artificial intelligence (AI) programs used to diagnose cancer and other diseases, according to companies and executives working in both technology and medicine. The problem is that synthetic data isn't just hard to come by—it doesn't exist. And it needs to be made as realistic as possible because increasingly sophisticated machines can be thrown off by even tiny differences between real patient information and their training sets. 

This means any new digital tool aiming for FDA approval must have synthetic data developed specifically for it—meaning it has never been used before on another project. If you're thinking about getting into health IT or AI development, then you need to know about synthetic data. You need to know how much it costs, who makes it, and what kind of obstacles stand in your way. If you want more info on how to make or use synthetic data, then you need our white paper: How To Use Synthetic Data In Healthcare: A Guide For Medical Device Developers. Download your copy today!

Case Study - The Advantages of Synthetic Data in Healthcare

According to recent research in US hospitals, 50% of all adverse patient events are a result of human error. The trend is not unique to America and is becoming a major problem across industrialized nations. Many countries have adopted synthetic tools as part of their healthcare strategy, but few understand what they can do or how they can help improve quality and efficiency. Because patient care relies on an effective connection between doctor and machine, any failure within that system can mean life or death for some patients. One way to ensure reliability is by using synthetic data. By replacing real patient data with synthetic data when practicing complex procedures, medical professionals can train without endangering lives.

 It's important to note that synthetic data doesn't replace real-world training—it enhances it. Using synthetic data means doctors and nurses get more practice time in less time, which translates into better skills overall. As we move toward a future where medical technologies are increasingly automated, reliable connections will be paramount to ensuring safe operation. Using synthetic data during training allows us to test these connections before they're put into place. In addition to improving patient safety, testing, these connections also help reduce costs related to injuries caused by human error during surgery. To learn more about how your organization can benefit from synthetic data technology, contact us today!

Artificial Intelligence and Synthetic Data

With medical data and technology advancing at an unprecedented rate, we have a lot to look forward to. This includes making better and more accessible healthcare for everyone in all corners of the world; improving on things like cancer treatment or decreasing diagnoses times for patients, and making it easier to find a cure for diseases that kill millions of people every year. The possibilities are endless. With these technologies, we can also start getting down to other more complex issues, like mental health care. However, before we do that there is one important matter that needs addressing: how AI and synthetic data work together to power medical research in ways we never thought possible.

 What's even better is that using these two revolutionary technologies together saves lives and time - making healthcare safer than ever! The health benefits of using artificial intelligence and synthetic data are endless, but we'll start with a few you can probably guess. One benefit is it could help eliminate human error in diagnosing. And that could have huge implications. According to CDC data , misdiagnoses lead to more than 7 million extra days spent in hospitals every year, and they cost between $17 billion and $29 billion annually just in healthcare costs. But AI and synthetic data don't just solve one issue—it solves several. Imagine being able to make better treatment decisions at lightning speed by having access to all available data?