Synthetic Data: What It Is, Why It Matters and How It’s Changing Healthcare for the Better

Much of the hype surrounding generative AI has focused on its use in content creation and offering personalized shopping experiences. But generative AI is also making a very real impact in fields such as healthcare.

I recently had the opportunity to interview Jon Read, co-founder of Confidant Health. We spoke about what AI-powered synthetic data is, why it matters and how it’s having an impact on healthcare. Here is a closer look at how AI is making a difference.

What Is Synthetic Data In Healthcare?

Synthetic data — information that is computer-generated to either replace or augment real data — is used broadly in AI. It protects sensitive information, mitigates bias from real-world data and can improve an AI model.

Synthetic data allows clinicians to use prompts to generate a conversation between a patient with depression and a therapist where they are discussing the onset of symptoms.

Healthcare providers can also use partially synthetic data, which takes a real-life transcript and has AI adjust it to remove personally identifiable information or private health information, while still telling a cohesive story. This data can then be used to train AI models to develop transcripts, training materials and so on.

Regardless of whether the data is fully or partially synthetic, the data can (and often is) adjusted as needed with additional prompts until it reaches the desired result.

Why the Use of Synthetic Data Matters

Healthcare is subjected to a variety of privacy rules through HIPAA. Eliminating these privacy concerns is a primary reason Read feels synthetic data is valuable in training models.

With synthetic data, healthcare providers don’t need to use real people’s data to train models. Instead, they can generate a conversation that is representative of a specific therapeutic intervention without involving anyone’s protected health information.

As Read explains, “Synthetic data also makes it easy to calibrate what we’re looking for — like to generate different examples of how a healthcare provider could say something explicitly or implicitly. This makes it easier to provide different examples and tighten up the information we provide to AI models to learn from, ensuring that we can teach it the right data for providing training or feedback to real-world clinicians.”

Synthetic data also democratizes the ability of different healthcare organizations to train and fine-tune their own machine learning models. Whereas previously, an organization might need to provide hundreds (or even thousands) of hours of transcribed sessions between patients and clinicians as well as other data points, synthetic data erases this barrier to entry.

Synthetic data allows for models to learn and build out responses at a much faster rate — which also makes it easier for new players in healthcare to enter the field.

How Synthetic Data Is Making a Difference for Healthcare

As Read explains, while synthetic data can be exciting in theory, its true usefulness comes from its ability to directly impact the quality of patient care, ensuring that clinical decisions are supported by real-world validated data sets.

“This can be especially important when providing feedback and training to clinicians,” Read explains. “For example, the learning model could use the standards developed through its data to ask a clinician if they asked about the onset of depression when they made the diagnosis. If the clinician didn’t ask important questions like when depressive episodes started or what their duration was, we would have less confidence in the diagnosis, and we would want to make sure that followup sessions addressed those questions so that the patient received the proper diagnosis and care.”

In this situation, the AI is able to provide quick and effective feedback to the clinician to ensure they are asking the right questions so that patients receive the proper level of care. The clinician can save time and energy by using their training to ensure they are following best practices and meeting appropriate criteria for providing quality care — even at scale.

Read also sees value in the potential for continuous improvement with machine learning systems.

“As you start to generate real world examples, you run the data to determine if the training from the synthetic data worked the way it was supposed to in terms of improving the standard of care provided. Are there additional criteria we need to look for when making the diagnosis? Are we actually asking all the right questions? As the platform learns and improves, we can offer even better guidance to clinicians by providing a quality, standardized system to work with.”

The Future of Healthcare

As Read’s insights reveal, the use of AI and synthetic data isn’t going to replace clinicians’ value or decision-making authority. But with the help of synthetic data, AI can help push clinicians in the right direction to ensure that there is greater standardization and adherence to best practices.

As more providers begin to utilize synthetic data to ensure they are following best practices in all patient interactions and to get feedback on their sessions, they can elevate the quality of care for all.

The post Synthetic Data: What It Is, Why It Matters and How It’s Changing Healthcare for the Better appeared first on ReadWrite.