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Predictive Maintenance for Laboratory Equipment

The pandemic changed the public’s perspective on many things. One healthcare aspect that was pushed into the spotlight was diagnostic testing and the speed at which it can be achieved. As such, it was critical for the machines and devices used for these processes to remain up and running at all times.

Fortunately, there are companies working on solutions to this challenge. They are using sensing technologies to enable efficient predictive maintenance schedules and determine when certain components need replacement, maintenance, or some other attention. In taking a proactive approach, the downtime for these systems is kept to a minimum and breakdowns do not take place during peak usage times.

Sharing insight into the value of this type of predictive maintenance are Scott Baum, VP of Strategy and Growth at Elbit Systems of America LLC, and John McCool, Executive Director, Business Development at KMC Systems (the medical division of Elbit Systems of America). In this Q&A, they share their expertise and explain the value of leveraging sensing technologies within laboratory equipment.

Sean Fenske: What are some of the most common causes of medical device malfunctions, and how do you avoid them?

Scott Baum: When you think about device malfunctions, in other industries as well as healthcare, there are a variety of issues that can affect the overall performance of the instrument. We focus on analyzing changes in instrument characteristics during normal operation because those spikes or anomalies in performance are usually an indication of a symptom that something in the instrument is not working properly.

In any complex instrument, there will always be measurements of temperature, vibration, current, humidity, pressure, and electrical draw, but it is those deviation points from normal operating levels that are what we are most interested in identifying so that we can actively work on solutions for our customers. We are also looking at other factors around unique signatures from different detection methods and sample handling that allow us to better predict system performance.

John McCool: Interestingly enough, this was particularly relevant throughout the pandemic. What we saw across healthcare in early 2020 until mid to late 2021 was diagnostic machines were running round the clock, every day, to process test results. In many of those cases, instruments were being run at higher throughputs for extended periods of time than what they were originally designed for and, due to the surge in demand, more pressure was placed on the instrumentation. Because of this demand, daily, weekly, and monthly maintenance on the device may not have been performed to adequate levels. Over-running or overloading the equipment paired with not performing those preventative maintenance activities is a perfect example of a scenario that may cause premature deterioration of a device and/or premature failure of equipment.

We are concentrating on sensor technology to drive data collection in helping us determine for our customers which components provide higher risk for unplanned machine downtime. Then, looking at those components that are the most vulnerable, we can monitor those and, through data analytics over time, begin to be able to predict outcomes so maintenance can happen on our customer’s schedule, instead of under pressure with implications across the entire healthcare ecosystem.

Fenske: What are the cost implications for the healthcare industry in machine downtime?

Baum: When you consider the cost implications of machine downtime, it’s important to remember the entire healthcare organization feels the effects of that—large scale OEMs, labs, technicians, prescribers, hospitals, public health and government entities, all the way down to the patients.

When the pandemic first hit, early patient diagnosis and management were critical to long-term health implications as well as cost factors. If patients were waiting longer than 24 hours on a positive COVID-19 result, you have two to three days of potential community spread, which could grow exponentially, putting the patient at risk for a more serious illness. This delay not only has public health implications, but potential costs related to hospitalization, medications, and additional treatment and/or services.

McCool: There is also tremendous cost implications to the testing center or lab itself. Labs receive reimbursement for each result the equipment produces. We manufacturer a device used to diagnose COVID-19 among other illnesses and cancers, and if the machine goes down during a run, all those samples need to be recollected and rerun, which as we mentioned, has implications for everyone in the healthcare system as well as the patient.

Looking at numbers, if the machine is able to run 1,000 samples a day at $50 a sample and it takes 48 hours to come back online, that is $100,000 in lost revenue for the lab, lost time for the patient, additional costs in equipment service and parts, and in the case of a pandemic, additional public health implications and cost for a given area.

From the perspective of the OEM, many times they supply their customers with lab equipment as a rental agreement. Often in this model, they are also receiving a portion of the reimbursement cost, so if the instrument is down, the OEM also isn’t receiving the profit they would be expecting to see.

Fenske: Who is impacted by machine downtime in healthcare?

Baum: Similar to cost implications, the impact of machine downtime in laboratory instruments effects the entire healthcare ecosystem from the manufacturer of the instrument all the way to the patient.

Fenske: How can predictive maintenance of medical devices add value for customers?Baum: We have a long history with our customers. We ideate, design, engineer, and manufacture the system. But we recognize the next step and say, “Alright, as the partner that has helped you design, engineer, and manufacture the system, how do we help you keep that uptime running when you need it?” It's not about no maintenance—it's about predicted and planned maintenance and enabling the system to operate longer.

For KMC, our goal is to change the maintenance model and enable our customers to predict when you replace certain parts to avoid costly downtime. From our perspective, we're spending a tremendous amount of effort thinking about how we better optimize that as well as how we sensor the systems, collect the data, and set our customers up in the best position to ensure they keep that system operating.

Fenske: How can data—or the collection of data—help prevent machine failure?

McCool: At KMC, every relationship we have with our clients is a multi-year relationship. We don’t step in and out of the relationship. We have to be prepared to tailor to our customers’ needs; one size does not fit all from a data model perspective. We are very cognizant of that and are talking to customers today about how much data they are willing to share and how much they are not willing to share. It is very much customer specific.

The analysis of the data is critical. The data itself doesn’t do much, but once it’s analyzed, we can recognize the trends and apply it in our design for reliability, selecting enhanced components and including recommendations on preventative maintenance schedules.

The volume of data we can also begin to capture on our instruments with regards to system performance may also allow us to assess individual parts. Some parts we previously didn’t recognize as having lower risk of failure may be able to avoid a maintenance change, reducing cost of spare parts for our customers. Or, for more expensive parts that require replacement more frequently, we may be able to re-evaluate the design and find a cost-effective product from day one of the next-generation product.

Baum: In all cases, our approach is to be flexible. Depending on the instrument, the technology, and the tests it performs, our approach to what data we are collecting from the different types of sensors will be customized to the specifications of the instrument.

Fenske: How do you monitor failure-prone components in a diverse range of healthcare instruments?

McCool: The benefit to our sensor technology and the biggest difference from testing that happens at suppliers and in test environments will be in our ability to collect data in real-life situations.

Testing of instruments and components is typically performed in lab environments, where every step, procedure, and process is followed precisely based on written SOPs. Users are also going to perform the maintenance daily, weekly, monthly, based on exact instructions because you are in an ideal environment and condition to do that.

We know that it isn’t always the case in real-life environments. We will not only be able to see our sensor data looking for spikes or changes of components outside their normal range, but we will also be able to see the maintenance logs that can be correlated to a potential instrument failure. It’s not just whatever indication you are looking for, vibration, or current, it’s that correlated with the activities or maintenance that are supposed to be done that potentially don’t happen.

For example, when you don’t wipe down the instrument as indicated, we may see a decrease in performance on part X. We are utilizing the sensing technology to monitor both the appropriate conditions a specific device is going to exhibit, while also reviewing and considering real-world case data.

Fenske: Do you have any additional comments you’d like to share based on any of the topics we discussed or something you’d like to tell medical device manufacturers?

Baum: Predictive maintenance and data analytics for many industries is not new, but in the medical technology space, the critical need for sensing and machine uptime is more important than ever. We have decades of experience in both commercial aviation and defense sustainment in keeping systems up and operational in some of the harshest environments. What we’ve learned across our business portfolio gives us a unique perspective that when you design and manufacture systems, you gain tremendous insight into how the system should operate and what needs to occur when systems begin to operate outside the norm or begin to fail.

At Elbit, we are in a unique place where we are taking those lessons learned in our other divisions and bringing them to our healthcare customers.

McCool: I think COVID has been a driving force for the urgency of diagnostic results paired with high volume and a market eager for additional rapid testing. The speed of testing is on every patient’s mind and fueling the need and demand for maximizing machine uptime.

The advances in technology we’ve seen in the last decade are also allowing us to capture and analyze data at rates prior generations of diagnostic development couldn’t take advantage of. We are just beginning to realize what we can leverage from other industries to continue to evolve and transform healthcare and medical technology today.

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