Progress on HAI progress: CDC portals and data

I spent about 2 hours with the new CDC HAI Progress Report – loosen your belt, it’s a big meal!  We probably had mixed perceptions about the recent alert to the 2016 HA progress report. Loads of information and analysis condensed down to a handful of bullets, all pointing to improvements in patient safety! The (very pleasant) surprise to me was that the format and delivery of the report has advanced to digital!! CDC has added the HAI progress report to the existing (and now updated) HAI AR Patient Safety Atlas Portal If you stop reading now – at least click on that link and explore and I will call this blog a success!

This report is several steps forward.  First, it pushes all of us to go to a place where we, being inquisitive minds, can wander and perhaps connect some dots within and between datasets. With the digitalization and visualization provided in the portal, the novice and experienced can more easily access and utilize these data. One can click through four distinct datasets, which now include state-summary statistics HAI infection rates/SIRs, inpatient stewardship activities, outpatient antibiotic prescribing rates, and inpatient antibiotic resistance metrics.

Now – the email alert. This year, well 2016 data, is the first to use the 2015 “re-baseline” efforts.

Unstated, but implied – the re-baseline effort includes the use of MBI (mucosal barrier injury) LCBSI as an event excluded from reported CLABSI rates, exclusion of yeasts (or low colony counts) from CAUTI rates, exclusion of “infection present on admission” for SSI (along with better patient-level risk adjustment), first use of risk adjusted metrics for VAE, and maybe slightly better models using more contemporary data for MRSA and CDI. 

With that said, 2016 performance suggests nationally patients are safer overall compared to the experience of 2015. Other than VAE which decreased by only 2%, everything else declined about 7-10% (I am rounding) compared to 2015. I understand many of the problems with risk adjustment and reporting bias that make these surveillance events poor performance measures for individual hospitals – but on the national level – I think these data do suggest fewer infections (o.k., perhaps some  widespread under-reporting—mixed reports on validation efforts in place).

Next – the overview of the current HAI Progress Report.   Although at first glance it seems the same as the info posted in the emails – CDC is offering more ways to track progress nationally – the number of states showing improvements (or worsening) compared to 2015, as well as the number states currently performing at levels better (or worse) then their 2016 contemporaries: 12 state perform better on at least 3 infection types compared to other states at the same time (2016). Now positive deviance nerds need to learn from these states to help the other states (or perhaps identify accuracy and validation issues at these states). The contemporary juxtaposition of SIRs is new, perhaps confusing (especially with CDC’s arcane explanation on how to interpret this: “SIRs statistically significantly lower than the 2016 national SIR are considered better than the 2016 national SIR”; curious if it ends up being useful to state programs. This year also is the first with more detail on inpatient rehabilitation facilities and long-term acute care facilities. Fewer data mean fewer statistical significant results, but these data are ripe for academic partners to latch onto as they try to partner with ARHQ, CDC, and state-programs to branch out into stewardship and prevention efforts in these types of facilities.

Finally, the portal – access it here.  Use the table view. No graphics to export for HAIs, only for other datasets. My pet peeve is that CDC still refused to list the no. of SSIs reported next to the number of surgical procedures reported to allow a crude attack rate. We still need to go to the technical tables for these values and calculate ourselves (see below). CDC, please stop making us jump through this hoop to be able to use attack rates for other purposes like planning studies, clinical trials, vaccine research! To all researchers and data nerds - the detailed technical tables should be downloaded examined (here), perhaps parsed out to our students and trainees, and used for different purposes that simply a “reporting requirement”.  I know there are many limitations to the accuracy of any one facilities reports and likely aggregate data up to the state or national level. However, as a long time national surveillance nerd all too familiar with the warts and ugliness of surveillance data, they do inform us, approximate the truth, and can help us ask the right questions and target the right populations. The more eyes using these data the more transparent the process will become, more uses of the data will be identified, patient safety should improve, and CDC will become more accountable to update (c’mon, where’s 2015 and 2016 NHSN AR data?! update the portal please!!),  maintain, and advance the public accessibility of useful data in our field.

Identifying Barriers to Hand Hygiene Audit and Feedback

This is a guest post by Daniel Livorsi MD and Heather Schacht Reisinger PhD from the University of Iowa Carver College of Medicine and the Iowa City VA Health Care System

Our research group recently published a qualitative investigation of hand hygiene in JAMA Network Open. The study describes real-life barriers encountered by 8 VA hospitals in their use of audit-and-feedback to improve hand hygiene compliance. For anyone involved in hand hygiene monitoring and improvement, the barriers we describe will probably not come as a surprise. In brief, we found that auditing hand hygiene compliance by direct observation was perceived to collect inaccurate data and created tension with frontline staff; the feedback process did not encourage positive change.

Although the importance of hand hygiene is widely acknowledged, our field’s understanding of how to improve hand hygiene compliance is still relatively primitive. All of the hospitals we visited had implemented audit-and-feedback, which was the focus of the study, but many were also using other strategies, such as environmental engineering and education. Despite these well-intentioned efforts, hand hygiene compliance rates at the participating sites were 49.8% (n = 9791) at room entry and 63.9% (n = 10 135) at room exit. It seems safe to assume that such poor compliance rates are common across all of healthcare. To meet regulatory standards (e.g. the Joint Commission), hospitals need to keep going through the motions of auditing, but wouldn’t it be great if all this effort could be directed towards interventions that actually work?

An accompanying editorial argues that all of these audit-and-feedback programs were missing one key component: immediate personalized feedback coupled to individualized action planning. In general, feedback is more effective if it is provided in real-time and is individualized, so this strategy is appealing. However, at the hospitals we describe, giving personalized feedback would likely face some logistical challenges, including limited personnel to perform these separate individualized audits and potential pushback from labor unions on the collection of individualized performance data. In addition, collecting individualized data still does not address larger questions about the accuracy of direct observations.

Like many processes in healthcare, improving hand hygiene will require both technical interventions and socio-adaptive changes. Clearly, compliance rates are not where they need to be, and the current strategies are not effective. I don’t pretend to know the answers, but as a field, let’s not give up on trying to find some solutions.

image credit: honoring University of Iowa Veterans