numbERs

Figure: medical data, computersFigureAiden Sims was a 9-day-old who presented with congestion and a fever. He had nly been sick for oa day, but he had declined rapidly. Mildly hypoxic, he was grunting and retracting on my exam. Unfortunately, Aiden never got better. That is because Aiden is not a person but an artificially-generated datapoint. The Problem with Data Real medical data have a number of problems: They are messy, often unstructured, contained in multiple formats, expensive, and impossible to share openly. In contrast, synthetic medical data can be generated to a user's specifications with the goal of interoperability, and sharing is not limited by HIPAA. Real medical data also must be collected, but synthetic medical data can be generated, sometimes in large volumes. One major question, though, exists in synthetic medical data: Can we use it to gain real insights into medical problems? The Project I built a web application that uses procedurally-generated pediatric patients with infectious respiratory symptoms to see if it might be possible to use synthetic medical data to generate real insights. Each of these patients has a name, past medical history, complete history, and physical exam. Procedural generation means that all of these features were created by a randomizing algorithm in the background rather than being pulled from a predesigned dataset, making no two patients identical. I piggybacked off original research in the field and used specific historical and exam findings that were relevant to the presence of pneumonia as features for my dataset. (Pediatrics. 2022;149[1]:e2021051405.) Patients were generated, and physician-users could then decide whether a chest x-ray was indicated. Then the process would start again with a new patient. To encourage participation, I offered a $20 reward to the user who scored the highest using a score based on accuracy and the number of patients seen. I used the collected data to build a random forest model that appraised the accuracy of each individual's guess based on a model built on all other guesses. I used this same model to build a facet of the application that allowed users to adjust settings on their own hypothetical patients to predict the likelihood of getting a chest x-ray.FigureFigureThe Use Case Determining the true utility of our synthetic data is only possible by testing it in a real-world setting. Approximating physician practice patterns, however, is a short mental leap from this dataset; using it to construct hypotheses for best practices or clinical decision rules is slightly larger leap but still a real possibility. As an example, let's look at how a patient's age and vital signs help us to determine if a chest x-ray was obtained (using patients older than a year). (See graphs.) It is easy to see that some vital signs change with age while others do not. Just as easily, we can see that deviation in some vital signs is much more correlated with performing a chest x-ray than deviation in others. If we combine a patient's pulse oximetry and respiratory rate, we can isolate a group of individuals likely to receive a chest x-ray—those with high respiratory rates and low pulse oximetry readings (although, surprisingly, some in this cohort did not). We can additionally match the probability of receiving a chest x-ray against the reported duration of fever using logistic regression and see that the two appear to be well correlated: The longer you've had a fever, the more likely you are to get an x-ray. This is by no means an exhaustive exploration of the factors that lead to performing a chest x-ray; rather, I am demonstrating that such an analysis can be performed on wholly artificial data. A total of 864 patients were treated in this project, and 41 percent of those received a chest x-ray. The Big Winner Who won $20? Andrew Bui, DO, an attending in Fort Worth, TX, treated 60 patients with an excellent algorithmic concordance. As the application's biggest user, I asked him his thoughts on the eventual utility of such synthetic data. “Everybody has their own risk tolerance,” he said. “But it could help you see if you are completely off with what everybody else is doing.” He said he sees potential in using such systems to evaluate and develop clinical decision rules or to assess regional practice differences. Why did Dr. Bui contribute such a large quantity of data? “To be honest, I kept going because I didn't know if there was going to be an endpoint,” he said. Intrigued by synthetic data? Interviews with companies using synthetic data to make a difference in our world will be featured in this column in June. Dr. Belangeris secretary of the American College of Emergency Physicians Locum Tenens section and an emergency physician in McKinney, TX. Read his past articles athttp://bit.ly/EMN-numbERs.