Are "One-Offs" Becoming Routine

one–off

play

adjective \ˌwən-ˈȯf\

After eight years investigating hospital outbreaks, and about 15 years trying to make the best possible use of surveillance data while at CDC, I still struggle with the tensions inherent in mixing surveillance and performance measurement. The past decade has been a roller-coaster of thrills and perhaps some spills in terms of attention, resources, refinement, and usefulness of HAI surveillance led by CDC; yes, you could probably blame me for several aspects of NHSN reporting you may find unsatisfying (take your pick – perhaps I will expand another day). However, I having recently retired from CDC and am transitioning to Emory Healthcare and Emory University. Although It has been almost eight months. It has been a fascinating transition. The learning curve is steep, and not just for re-entering clinical medicine (that is another story), but also navigating the pathway which integrates the business of healthcare delivery, quality of healthcare delivery, and research opportunities. Slightly easier was learning how to navigate the Emory Parking situation (took 3 months). Much easier was recognizing that the performance quality metrics linked to HAI prevention are getting a lot of attention and a lot of action. It only took a few sessions listening to the quality improvement teams reporting on their target HAIs to understand two things. First, the C suite leaders really care. I had assumed this while at CDC, but it was illuminating to see up close how hard these teams worked to influence HAI prevention. Second, it was becoming somewhat routine to report out on “exceptions to the rules” of HAI reporting. There are many names for those scenarios when an HAI is justifiably reported, but either considered not preventable with evidence based prevention practices or not clinically the infectious event represented by the HAI. While at CDC we routinely heard about these: CLABSIs that “shouldn’t really be counted”, MRSA BSIs that really “weren’t ours”, CAUTIs that really don’t represent an infection. Now these reported HAIs were being called “one-offs.”

The NYT reports the term ”one-off” comes from earlier industrial beginnings with the quantity of items produced in manufacturing process, such as taking one-off, two-off, or twelve-off the line to sample or give-away. However nowadays it can refer to any exception of the rule – such as a recent one-off boxing match that really should not ever have happened. 

In an HAI paradigm where we aim for 0 infections, one-offs may either be unavoidable (not preventable) or wrongly attributed to the device, location, procedure. I have historically known of these in terms of byproduct of using proxy measures. I had previously published an editorial on the value of proxy measures of infection as a tool for quality improvement (Meaningful measure of performance: A foundation built on valid, reproducible findings from surveillance of health care-associated infections).

In that editorial, we outlined necessary steps to reduce the inaccuracies inherent in using such an approach. Now that progress has been made in HAI prevention since 2010/2012, many of these HAI events that conspicuously remain and continue to plague our patients, often don’t fit neatly into the intent of the surveillance definitions. Left with these “one-offs,” it is often difficult to know what to do more to prevent them. Surgical patients with fistulas and central lines that don’t have an infection related to insertion or maintenance processes, neutropenic patients that don’t quite meet the definition of MBI-BSI, I have even heard of tissue transplantation related bacteremia categorized as CLABSI. No doubt, changes have occurred since 2011 to improve CAUTI reporting, and neutropenia-related bacteremia. However, the pace is slow. The one-offs are starting to pile up. Perhaps improved risk adjustment of HAI data will mitigate the influence of the one-offs on healthcare facility performance measures. Until then, kudos to the quality folks and infection control teams making prevention progress. However, I hope we can reward them soon with improved performance measures. Perhaps there are surveillance lessons that can be learned from these one-offs after all. 

If you are interested in sharing one-off stories I have started a registry here - maybe we can fill in some gaps and accelerate the process of changes in surveillance methods.

Surveillance bias and public reporting

This week’s JAMA has a commentary from Haut and Pronovost that’s worth a read, on the topic of surveillance bias. This is Epidemiology 101 for those of us who live and breathe surveillance and prevention, but unfortunately it is not very well understood by patients and public policy makers. So when data like this garbage dump from Consumers Reports are released and generate media attention, more harm than good result. Good hospitals are unfairly maligned, and undoubtedly some hospitals that game their rates or just perform poor surveillance are rewarded. Call me a stupid consumer, but if I require ICU care and have to choose between Vanderbilt, Virginia Commonwealth, University of Maryland and one of these hospitals, I’m choosing one of the first three. Now I'm sure this list includes excellent hospitals, but I have no (zero) confidence in the data presented (i.e. I'm 'getting to zero' confidence).

We’ve blogged plenty about the challenges of public reporting and establishing a level playing field, so I’ll refer readers to these prior posts. As Haut and Pronovost point out, to ignore standardized, accurate and fair measurement (to include external validation) is both “reckless and unjust”. It is also true that “to be done appropriately, quality measurement is expensive”. Cheap shortcuts, like using ICD-9 coding data, simply prove the maxim that ‘you get what you pay for’.