Author: Hank Balch | Beyond Clean
- Understand the primary data minefields SPDs struggle with today.
- Learn how technology can improve important sterile processing data points.
- Identify how technology can learn from SPD members with industry experience, wisdom and instinct.
Ask most Sterile Processing professionals if it’s been a “busy” day in their department and they will be able to tell you without looking at a computer dashboard or printing off a report. Ask them the average time it takes for a single tray to move from decontamination receiving through the reprocessing workflow and into sterile storage, and the place of data analytics becomes apparent as well. The reality is, great technicians and department leaders need both technology and awareness to do their jobs well. Too much dependence on either end of the spectrum has far reaching implications for team dynamics, quality output, and the overall health of a Sterile Processing department. This article will look at how industry professionals can and should be learning from their department data and, just as importantly, how our department data can learn from us.
Misconceptions and Minefields around Sterile Processing Data
For those outside of the Sterile Processing industry, it is often difficult to understand the complexities involved in medical device reprocessing in a healthcare facility. While there are overarching concepts that serve as the foundation of decontamination, cleaning, and sterilization (such as bloodborne pathogen standards, universal precautions, and basic microbiology), much of what happens during a normal shift is filled with variables, critical thinking, and in-the-moment decision trees. “Is this a handwashed item?” “Which patient are these loaners for?” “Can this tray be sterilized for 10 minutes at 270 degrees?” These questions require context, and most of the answers can’t be found in a standard textbook.
So what does all this have to do with data? One of the biggest misconceptions around Sterile Processing data is that it’s the exclusive purview of department leaders, and only applicable when considering topics like budgets or productivity. While data is a critical component of those things, and a great asset to department leaders, it is just as valuable to frontline technicians across the entire department – whether they are monitoring water temperature during manual cleaning or calculating the number of unused returns for the day. Our departments are filled with data points from the moment we clock in until the moment we clock out, and each of us have parts to play in contributing to this data as well as using it to improve our teams.
As important as data is to each member of the Sterile Processing department, our industry is still at the very beginning of our journey toward data excellence. The primary data minefields we struggle with today are incomplete data (which only tells us part of the story) and dirty data (which tells us a story that may only be half-true). Overcoming both of these challenges should be a critical goal of every frontline technician and Sterile Processing leader. If we ignore these minefields, anything else we learn from our data cannot be trusted.
Learning From Data: How Technology Teaches Us
Because our departments are filled with data points, the potential to learn more about our workflows is seemingly endless. In generations past, the only way to gather these data points in Sterile Processing (for instance, noncompliance with point-of-use cleaning) required a manual process of handwriting, transcribing, and time-consuming report building. With the advent of computers and eventually industry-focused surgical instrument management software, technology gave us the ability to intake these data points far quicker, and convert them into actionable reports much more easily.
Gaining access to all of this data is a massive win for our departments. Through technology we can now quickly verify things like reprocessing cycles on robotic arms, insulation tests on laparoscopic devices, missing instrument trends by specific service lines, and countless other critical information about how our department workflows are actually working. All of this teaches us important lessons that can actually change the way we work within and lead our teams. Here are some concrete examples from the list above:
- Reprocessing cycles on robotic arms: Capturing these data points can cue us in on utilization rates of these limited-use devices, which can then be used to modify our case picking, packaging, and ordering processes for robotic procedures. High reprocessing cycles compared to a low “life count” signals that these arms are being regularly pulled & opened for cases, but not used.
- Insulation tests on laparoscopic devices: Capturing these data points at the point of inspection/assembly of laparoscopic trays can surface care & handling issues in the Operating Room and/or during the transport and decontamination workflow. Reoccurring damage to devices which can be linked back to the same surgeon, procedure, or OR team members allows for targeted follow-up which would otherwise be unavailable.
- Missing instrument trends by specific service line: Capturing these data points can identify training gaps or compliance issues related to post-op instrument practices in specific service lines, and allow for strategic budgeting for replacement instruments. Additionally, this data provides important information for instrument coordinators to proactively build a replacement (or “backup”) inventory so that trays do not remain incomplete as they move through the workflow.
The Strength of Paying Attention: How Data Can Learn From Us
Even though our departments are full of data points which can be captured and analyzed to improve our processes, our departments are also full of something even more valuable than data: People. Unlike technology, our people have the unique ability to interpret data points from the context of external experience, wisdom, and instinct. Put another way, our technicians and leaders “can sense” when a process isn’t working. Our supervisors “can tell” when decontamination is getting busier than usual. Our preceptors can “have a feeling” that the new hire isn’t quite ready to work a weekend shift by themselves. In all of these instances, it’s not the data that prompts the attention of staff members, it’s their natural intuition and awareness which informs their assessment of the situation.
This awareness is absolutely crucial for cultivating a department where the existing data minefields are navigated and our data can actually learn from us. Here’s just one example: Imagine a department leader decides they want to know how long it takes their technicians to process their standard Major Tray. Before they pull a tray assembly report out of their tracking system, they are already aware of three important contributing factors:
- They have recently hired a handful of new technicians over the last couple of months who often train by assembling the Major Tray.
- The Major Tray is a prime culprit for instrument migration issues from the OR due to the high turnover of surgical technicians in the General Surgery service line.
- Most of the Major Trays are assembled on 2nd shift, which often has their assembly interrupted by phone calls, deliveries, and vendor drop-offs.
Most of this information would not show up in a standard report, but because the department leader is aware of the impact of these three factors on their assembly data, the assembly times they pull for the Major Tray can be properly interpreted. New technicians are understandably slower, trays with mixed up/missing instrument automatically take longer to reassemble, and interruptions will negatively impact total assembly time. Hence, the pure data is more valuable and more informed when the situational awareness of the Sterile Processing professional is added into the equation.
Seeking & Sustaining the Balance
Ultimately, there is no such thing as a department who can operate on data points alone, without any awareness of the potential impact of difficult-to-measure external factors. On the other hand, it is impossible to manage even the simplest of reprocessing workflows on the power of intuition alone, without some dependence on measurable data. To work and lead well in Sterile Processing, our teams must seek a healthy balance of pursuing greater data from our workflows, and intentionally bringing our deep awareness to the interpretation of what we are seeing in the numbers. Valuing data does not mean we must devalue the experience and “sixth senses” of our team members. In fact, our department data becomes even more trustworthy as those personal insights are brought to bear on the information we see in our dashboards and reports. The more data we interpret from our department processes, the smarter we get. The smarter we get, the more value our data has for improving outcomes for our patients.
As you come to understand the value of both ends of the spectrum of technology and awareness, make friends with those in your department who may be on the other end from you. You will likely have teammates who love data but struggle to pull their heads out of the proverbial cloud. Likewise, you will also have coworkers who can tell it’s a busy day as soon as they hit the parking lot, but don’t yet understand the value of scanning every case cart as it rolls into decontamination. In order to build a balanced department where both technology and awareness are valued, we will need each other’s strengths to make it happen. By collaborating, we will learn, and by learning we will improve ourselves, our departments, and our fundamental mission of safe patient care. And that’s one data point we can all get excited about.