How many students pulling all-nighters right now in nursing school dream of becoming a device interface when they graduate? What would the course description look like anyway? Maybe something like: Device Interfaces in Modern Nursing — In lieu of automated systems, today’s nurse is expected to spend an inordinate amount of time manually typing and/or writing information down on a sticky notes from medical devices to electronic health records (EHR). While it’s not a course they teach, the task of manually and inefficiently managing data between devices is what nurses are being tasked with in the real world every day, and it comes with high costs.
Most of us are familiar with automotive recalls like the ones that are frequently in the news, like Takata’s airbag recall that affected over 33 million vehicles. Those working in healthcare technology management (HTM) are also acutely aware of medical device recalls, which happen more frequently than most people realize. In the four years between 2005 and 2009, the FDA reports that manufacturers conducted 87 recalls on infusion pumps alone. It’s not news anyone likes hearing, but when a hospital is notified of a recall like the recent one on Alaris pumps, it can affect thousands of devices that are scattered across the hospital. Just finding these devices in a hospital can be a massive undertaking. After sending out emails to nursing staff, informing them of the recall, it’s not uncommon for HTM departments to have a dedicated staff member scouring the hospital in search of the devices. Once they are taken out of service for repair, the nursing staff and operations teams immediately feel the squeeze of having less equipment on hand. It is an arduous, time-consuming process, but there’s a way to turn that pain into gain.
I was visiting a new client recently, discussing a project we were doing at their hospital to clean up their equipment data. While looking at some of the data in their legacy system, we saw many inconsistencies and mistakes that had managed to creep into the database through the day-to-day activities of the users over the years. For example, they had several device types, manufacturers and models describing a single piece of equipment, which was causing inventory reports on that equipment to be incorrect. With multiple naming conventions for one specific piece of equipment, it was difficult to manage recalls and alerts, because the database had to be manually screened to find everything affected. It was even more concerning that the inconsistencies in the data caused problems in preventative maintenance scheduling for the equipment.
We live in an information age where reliable data is everything. Informed decision making with technology and the data behind it is not just a smart choice, but a way of life for most of us. Better and faster information is available at our fingertips, and it’s making our lives easier. Technology plays a major role in delivering that information, but we often forget that in most cases, technology relies significantly on other humans to provide that information in the first place.
Here’s an understatement for you: Evidence-based capital replacement planning isn’t easy. As we’ve discussed before, developing a replacement forecast requires hospitals to assess thousands of devices across as many performance metrics and replacement criteria—lots of data crunching. Furthermore, trying to combine those criteria to determine the overall performance of a piece of equipment is exponentially more difficult.