A Peripherally Inserted Central Catheter (PICC) is a long, thin tube that delivers medication through the arm to veins in the heart. More than 3 million Americans receive PICCs every year. At present, the PICC placement process is painstaking and potentially inaccurate. Nurses mark spots on the arm and chest, and then measure between them to estimate how far the PICC will travel. Some cases even require a chest X-ray. This process can result in serious complications; for instance, 17% of improperly positioned PICCs are placed into the right atrium, which can be life-threatening (Hostetter, Nakasawa, Tompkins, Hill 2010*).
Piccolo Medical has developed Smart PICC™: an innovative, low-cost device for PICC placement which does not require X-rays and manual measurements. In 2019, Piccolo was awarded $1.4M by the National Institutes of Health for research and development.
Stratus Data has worked with Piccolo since the device’s early stages: refining goals, improving algorithm accuracy, and helping achieve FDA clearance.
The early days: ideas and raw data in place, but not yet ready for prime time
The Smart PICC™ uses innovative sensors and Machine Learning algorithms to provide the nurse with a real-time guide to the correct destination.
Piccolo began its data science voyage in 2017 with a 300-line Python script which provided a basic framework and methodology to evaluate sensor data to determine PICC line location and direction. However, the journey to a foolproof solution would require specialized resources. Not only did Piccolo have to refine the algorithm, but they also had to gather evidence to prove the device’s safety. Moving forward, Piccolo needed a Data Science team to help make sure that the proprietary methodology worked every time, all the time.
Says COO John McKenzie of the interview process: “We had five different recommendations for independent contractors or small firms. […] Everyone else said, ‘It’s going to be hard to see what techniques we’re going to use until we get into it.’ Stratus said, ‘Here are options a, b, and c for approaching the machine learning algorithm. Let’s dive in and see.’” According to McKenzie, Piccolo was impressed by Stratus’ methodical approach to every detail, as well as great diversity of knowledge.
“We needed a data science team that was nimble, who could work with us, not someone who needed us to clean up the millions of data points ourselves and put it on a silver platter for them to do the work. In the medical device industry, when it comes to getting algorithms approved by the FDA, it is often greenfield territory. However, the agency clears products based on valid hypotheses that are tested numerous times to demonstrate efficacy with statistical significance. Stratus showed a willingness to work around these constraints.” – John McKenzie, COO, Piccolo Medical