At St. Luke Community Healthcare in Montana, routine monitoring revealed a significant problem with contaminated urine cultures. In 2025, 59 of the 133 cultures collected were contaminated, a rate of approximately 44%.
Contaminated specimens can lead to repeat testing, delays in diagnosis, unnecessary antibiotic use, and frustration for patients and staff. A hospital might collect a specimen, send it to the laboratory, and wait for results, only to discover that the results aren't reliable enough to guide treatment.
Rather than accepting that level of contamination, a multidisciplinary team at St. Luke worked to improve the collection process. According to an October 29 article in the Lake County Leader, the hospital reduced its reported contamination rate to 30% in the second quarter of 2026.
That's an encouraging result. The hospital also received a Quality Improvement Award from the Montana Rural Healthcare Performance Improvement Network, recognizing the team's work.
The reported improvement deserves attention, and so does the question of whether the new performance can be sustained.
Improving the Process Instead of Blaming the People
The improvement team included Infection Preventionist John Dresen, Director of Nursing Abigail Byers, Quality Improvement Manager Katie Harding, and Med/Surg Clinical Nurse Manager Quincy Taylor.
Their investigation identified inconsistent collection instructions and difficulties obtaining reliable clean-catch urine specimens. These are process problems that can affect even conscientious, well-trained healthcare professionals.
The team worked on several countermeasures, including more consistent collection procedures, education for staff and patients, and a decision-making workflow to guide the selection of collection methods. The changes included reinforcing the use of intermittent catheterization when clinically appropriate.
Individualized coaching and ongoing monitoring helped reinforce the revised practices.
I like that this approach went beyond simply reminding people to be more careful. Contamination can result from how instructions are communicated, how specimens are collected, and whether staff and patients understand the process.
Standardization can help make the work more reliable, particularly when different employees or shifts might otherwise follow different methods. But a standard is only useful when it fits the actual work and people have the knowledge, supplies, and time needed to follow it.
The published account describes several changes implemented together, so we can't determine which contributed most to the reported reduction. That isn't necessarily a weakness in the improvement approach. Complex problems often require multiple countermeasures, although that makes evaluating individual contributions more difficult.
What Can We Learn from the Reported Results?
The hospital's original goal was to reduce contamination to 30% or lower. The team reports achieving that target by the second quarter of 2026.
The published figures show a decline of approximately 14 percentage points, from 44% to 30%. That's a meaningful difference in the reported percentages, but is it evidence that the underlying process has changed?
I'd like to see more data before drawing that conclusion.
The article compares a full-year 2025 rate with a rate from the second quarter of 2026. It doesn't provide the monthly observations that produced those figures, nor the number of specimens collected during the second quarter.
This is a distinction I often emphasize when teaching leaders about performance measurement. Two numbers can tell us what was reported at two points or periods in time. They don't necessarily tell us whether a process has improved.
Every process has variation. Even if nothing about the collection process changed, the monthly contamination rate would fluctuate as different patients were treated and different specimens were collected.
St. Luke collected 133 cultures in 2025, an average of about 11 per month. At that volume, the percentage could move quite a bit from one month to another based on just a few additional contaminated or uncontaminated specimens.
We need to understand the variation before deciding what the numbers demonstrate.
A Process Behavior Chart Would Help
Here's where I'd suggest adding a little more statistical thinking to the story.
Rather than comparing the 2025 average with the second-quarter 2026 result, it would be better to examine the contamination rate month by month, starting before the improvement work began and continuing afterward.
A Process Behavior Chart (PBC) plots performance over time with an average and statistically calculated natural process limits. It helps distinguish routine fluctuation, or noise, from evidence that the process has actually changed.
For example, the monthly data might reveal a sustained shift toward lower contamination after the countermeasures were introduced. That would be encouraging evidence that the process was performing differently.
Or the chart might show that contamination rates had already fluctuated considerably, with the second-quarter result still within the range of routine variation. In that case, the team would have reason to continue testing and refining the changes before declaring that the improvement had been demonstrated.
We don't know which pattern the data would show.
The number of specimens collected each month also matters, especially with relatively small monthly volumes. I'd want to see both the number contaminated and the total collected, then select a charting approach appropriate for those data.
This doesn't need to become an elaborate statistical project. The value comes from using the information to make better decisions.
I've covered this approach in a KaiNexus webinar on managing improvement metrics, including a bonus video showing how to construct a Process Behavior Chart. There's also a step-by-step article and video for readers who want to try it themselves.
One caution applies regardless of the chart: even a statistically meaningful improvement after an intervention doesn't prove that the intervention caused it. Teams still need to examine what changed in the work and what other factors might explain the results.
My interest in the additional data isn't to challenge the hospital's accomplishment. It's to help the team understand the results well enough to build on them.
Sustaining Improvement Requires Ongoing Management
One detail in the St. Luke article is particularly encouraging. The hospital plans to continue monthly monitoring, share the results with nursing staff, and reinforce the improved practices through orientation and ongoing training.
That's exactly the sort of follow-through I'd want to see.
Too often, organizations complete an improvement project, celebrate a favorable before-and-after comparison, and move on. The revised process may work well initially, but employees change roles, new people arrive, and competing priorities make it harder to sustain the new standard.
Ongoing monitoring helps identify when a process may be drifting. But monitoring should be paired with regular conversations with the people doing the work.
Are the collection instructions clear to patients? Do staff have the supplies they need? Are there situations in which the revised collection workflow is difficult to follow? Have new employees been trained on the process, and do they understand why the changes were made?
These questions matter because compliance with a standard isn't the ultimate goal. The goal is more reliable care.
I'd also be interested in examining measures beyond contamination itself. Are fewer specimens being recollected? Are patients receiving useful test results sooner? Has the hospital observed any change in unnecessary antibiotic treatment associated with contaminated cultures?
The published article identifies these as potential benefits of reducing contamination. It doesn't provide outcome data demonstrating that those benefits have already occurred.
Tracking a few related measures could help the team understand whether the improvement is producing the intended benefits for patients and staff.
Don't React to Every Monthly Fluctuation
There's another reason to use a Process Behavior Chart as the team continues monitoring.
Suppose next month's contamination rate rises from 30% to 35%. Does that mean the improvement is slipping? Should leadership immediately ask what went wrong?
Not necessarily.
That increase might reflect routine variation in the process. Reacting to every monthly increase by adding another reminder, retraining everyone, or changing the procedure again can create frustration without improving performance.
The opposite mistake is possible, too. A lower rate next month might lead the team to conclude that its latest countermeasure worked, even when the result is still within the expected range of variation.
Process Behavior Charts help leaders avoid both mistakes. When the data show a meaningful signal, investigate what changed. When the data show routine variation, focus on improving the underlying system rather than explaining every individual result.
This approach doesn't mean ignoring a specific contaminated specimen or a patient safety concern. An individual event may require immediate attention regardless of what the aggregate chart shows. The statistical question concerns how we interpret overall performance.
Recognition Is Good. Continued Learning Is Better.
St. Luke's improvement team deserves recognition for identifying a problem, investigating the collection process, implementing changes, and reporting its results. The work also demonstrates how infection prevention, nursing, and quality improvement professionals can collaborate on a problem that crosses functional boundaries.
I appreciate that the hospital is planning ongoing monitoring rather than treating the improvement project as finished.
The next step I'd recommend is making the monthly performance visible in a way that distinguishes signals from noise. Include enough historical data to understand how the process behaved before the changes, identify when the interventions occurred, and continue reviewing performance as new observations become available.
If the data demonstrate a sustained reduction, that's a result worth celebrating and learning from. If they don't yet demonstrate a shift, the team still has useful information to guide its next PDSA cycle.
The goal isn't to produce a chart that proves an improvement project was successful. It's to understand whether the work is actually getting better and what the organization needs to do next.
An improvement award recognizes work already done. A reliable management system helps ensure the benefits continue long after the award ceremony.
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