“Chaos was the law of nature; Order the dream of man.” Henry Adams wasn’t thinking of sterile processing when he said it, but he hit the nail on the head all the same. With pathogens, broken processes, and human error, our field is well suited for chaos. That’s why if you want to run a lean, mean operation, you have to Sort, Straighten, Shine, Standardize, and Sustain. The “5S” method is one aspect of the Lean management approach. Using Lean techniques allow us to reduce waste, implement process improvement, and build systems that ensure safe patient care.
The Lean mentality has taken a deep hold in sterile processing, and for good reason. Providing instruments for patient care requires high compliance, low error rate, and rapid production. The nature of the industry means that there is a significant level of chaos to be addressed. As kids we watched the cartoon hunter Elmer Fudd stumble around the forest trying to find that ‘pesky wabbit’. That’s chaotic. If poor Elmer had to hunt a million microscopic Bugs Bunnies with a fly swatter and the OR calling every few minutes, that would be sterile processing.
When issues crop up in your department, you might pull a boatload of numbers from your tracking system, put them into an Excel spreadsheet, and start making “data driven decisions”. We work in a data rich environment, and knowing utilization rates, error trends, and staff productivity can help us bring order out of chaos.
So here’s the question; when was the last time we applied that same Sort, Straighten, Shine, Standardize and Sustain approach to our data? Below are the five questions we need to ask, and the methods we can employ to get lean data in sterile processing.
1. Sort: Is the data we are collecting useful, and necessary?
a. One method of sorting in a physical space is to place a red sticker on any item that is not being used for its intended purpose. We can do this with data as well. Are you getting reports that are going unused, or tracking a KPI for an event that happened one time, three years ago? Lean data means only collecting information that is purpose driven. Before we start collecting data, we need to have a clear cut rationale for the work you’re going to be putting in.
2. Straighten: Is the time you are putting into collecting data worth the solution you hope to arrive at?
a. In sterile processing, we have facility, federal, and best practice standards for almost everything we do, and it’s normal to want to cite the irrefutable evidence for all the calls we make. Unfortunately, we can also reach a point of diminishing return. Is it worth twenty-two hours of labor to have a spreadsheet tell you that you need to buy ten more mosquito clamps a month? A general rule of thumb is that if the time you are spending on the decision costs more than the decision itself, you may need to straighten out your data and move on with your day.
3. Shine: Is your data presentable and does it convey information at a glance?
a. We’ve all been in meetings where after endless charts and data entries there is a room of heavy eyelids confirming that not everyone finds your data as fascinating as you do. Data needs to be packaged in a way that is easily digestible. In the era of the endless scroll, and have become very adept at determining what is relevant at a moment’s notice. If the viewer is jarred by the nine different colors and three fonts on your graph, you’ve lost them before you’ve even started.
Pro-tip: Almost 10% of all people are colorblind, so when creating graphs, opt for red/blue color combinations and avoid red/green combos like a Christmas plague.
4. Standardize: Are you collecting data with integrity?
a. Even if your data is eye catching, purposeful, and necessary, if it collected poorly or using inaccurate methods, it isn’t Lean. If your data leads to unrealistic conclusions, you’ve gone into pure chaos. We see an analog of this from the Cold War. On October 5, 1960, an early missile detection station in Greenland alerted the data collectors of impending doom. It only took a bright scientist to step outside and see that the state of the art technology had not detected incoming warheads, but a full moon rising on the horizon. Even good data can lead us in the wrong direction if we aren’t validating it with audits and observations. Every data collection project needs to have instructions that include the following elements:
i. A problem statement that justifies the purpose of collecting the data.
ii. A defined source for the data (tracking system, time studies, etc.).
iii. Instructions on how the data should be collected.
iv. Just like the person who stepped outside and saw the moon, you need a secondary source to audit and validate your data.
5. Sustain: How long do we need to collect this data?
The final piece of 5S, Sustain, is perhaps the most critical. We sometimes think for something to be sustained, it has to be perpetual, but that is not the case. Sustained progress means having a timeframe for how long we are collecting the data. Our goal should be to collect enough data to be accurate and actionable.
Example: “We are going to see how often we have more than fifty clinic trays dropped off at one time. We are going to check this every day for a month and then complete a root cause analysis based on our findings.”
Bonus S: Strategize: Is your data helping you get what you need in your department?
- If we think strategically about what is required to make the patient experience safer, more reliable, and error free, data can become a powerful tool in our arsenal against disease. Many healthcare systems have data analysts, improvement specialists, and consultants that can help us leverage the data to meet our unique department needs. With 5S principles, the outcomes are multiplied exponentially and our lean data turns the chaos of sterile processing into the order we dream of.
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What are the five S's (5s) of lean. ASQ. (n.d.). Retrieved September 23, 2022, from https://asq.org/quality-resources/lean/five-s-tutorial
Color blindness facts & statistics: Prevalence. Color Blindness. (2015, April 13). Retrieved September 23, 2022, from https://www.colour-blindness.com/general/prevalence/
Stevens, M., & Mele, C. (2018, January 14). Causes of false missile alerts: The Sun, the Moon and a 46-cent chip. The New York Times. Retrieved September 23, 2022, from https://www.nytimes.com/2018/01/13/us/false-alarm-missile-alerts.html#:~:text=In%201960%2C%20Norad%20was%20sent%20to%20its%20maximum,it%20had%20detected%20dozens%20of%20inbound%20Soviet%20missiles.
Choustoulakis, S. (2021, August 5). 5S system for Lean Manufacturing: 5S principles. OpsBase. Retrieved September 24, 2022, from https://opsbase.com/5s-system-lean-manufacturing/