Despite the critical role of IV therapy in healthcare, there are no widely used, cost-effective IV drip monitoring systems that provide real-time feedback and alerts. Manual monitoring remains the standard, increasing the risk of inaccurate flow rates, human error, and delayed interventions, which can compromise patient safety.
The first prototype was 3D-printed and utilized an LED and a photo sensor to detect drips passing through the IV line. This initial approach successfully demonstrated the feasibility of automated drip detection but faced challenges with sensor placement, light interference, and accuracy, requiring further refinement.
Extensive FDA and patent research was conducted to ensure the device aligned with existing regulations and industry standards. Predicate devices were analyzed to determine the appropriate FDA classification, guiding the design to meet 510(k) premarket notification requirements. Additionally, the team followed FDA guidelines for medical device development, focusing on biocompatibility, safety, and effectiveness. This regulatory groundwork ensures the system can be positioned for future clinical validation and potential market approval.
Beyond the device itself, packaging and shipping logistics were carefully evaluated to ensure safe transport and ease of use in clinical settings. CAD software was used to design initial box and packaging prototypes, focusing on protection, sterility, and efficient storage. The design considered tamper-evident seals, compact form factors, and clear labeling to align with medical device packaging standards and enhance user experience during
The final design incorporated infrared (IR) sensing for improved accuracy and reliability. The system is powered by an Adafruit Feather RP2040, with a TFT FeatherWing display for user feedback. A rotary encoder enables precise drip rate adjustments, while a buzzer and LED alert system provide warnings if drips stop or deviate from the set rate. The device enhances patient safety and can be seamlessly integrated into clinical settings.
The project won Best Demonstration of Engineering in the Biomedical Engineering Department at the University of Utah, recognizing its practical impact, technical execution, and potential for real-world application in improving IV therapy monitoring.