In the vast majority of hospitals, nurses are required to check on patients at least once an hour. This practice, called hourly rounding , is designed to reduce the number of patient falls and pressure ulcers, which happen when bedridden people don’t move enough. But there’s no way to know whether nurses are checking every hour–or even at all.

Patient neglect is a nationwide problem. According to a 2016 study, medical errors–which include lapses in caregiving that lead to falls and injuries–are the third leading cause of death in the country, causing more than 250,000 deaths per year. “In nursing homes and hospitals, we hear horror stories about neglect,” says Michael Wang, an entrepreneur and registered nurse who worked in the cardiothoracic wing of New York Presbyterian Hospital. “Patients become injured or they die for the very simple fact that no one checked on them.”

Wang’s career began in the military, and he enrolled in Columbia’s nursing program to start his life as a civilian. After earning his degree as an RN, he worked in New York Presbyterian’s cardiothoracic unit at night while tackling an MBA from Columbia during the day. “That’s when I started to really combine what I learned in school and some of the practical issues I witnessed as a practicing bedside nurse,” he says. “I realized that there are huge gaps in the patient care process.”

In 2016, Wang founded the startup Inspiren with the goal of providing data about what actually goes on in hospital rooms. The company’s first product is a device that monitors everything that happens in a patient’s room, paired with an analytics platform to help nurses and hospitals understand how well they’re taking care of patients.

Called iN, the oval-shaped device sits on the wall in every hospital room and uses sensors to detect when a staff member is there. It also uses machine learning algorithms to understand what they’re doing–like turning a patient to prevent them from getting pressure ulcers. The device can recognize when patients are out of bed or if they fall, and raise the alarm.

It sounds a bit like Big Brother, hospital-style. But iN doesn’t use facial recognition to determine who is in the room. Instead, it senses motion and then measures the unique ratio of the hospital staff’s limb lengths, as if they were stick figures. Then, it looks them up in the system to identify who is in the room. To ensure that the device is HIPAA compliant, patients are identified only by their room number and bed number, and the processing happens on the device before the data itself, which is 95% accurate, is beamed up to the cloud.

The company went through 72 different iterations of the design, which is carefully crafted to mitigate the uncanny feeling of being watched. Initially iN was circular, but nurses told Wang and his team that it reminded them too much of an eye that was constantly staring down at them–so he tweaked it to be a friendlier oval. LED lights around its edge indicate a patient’s status–green for all good or orange if a patient hasn’t been seen. Based on feedback from nurses and doctors, the LEDs adjust to the amount of light in the room, ensuring that they’re not too bright when patients are trying to sleep.