Overinterpretation of Sterilization Guidelines Could Interfere with Patient Safety

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In Greek mythology, Sisyphus is a character punished with the task of eternally rolling an enormous boulder up a hill, only to see the boulder roll back down as it nears—but never reaches—the top. Staying on top of compounding regulatory requirements often feels like a Sisyphean endeavor.

Otolaryngology clinic equipment reprocessing, and the increased regulations associated with it, is a topic that has provoked many passionate conversations. Opinions have been discussed online, through various American Academy of Otolaryngology–Head and Neck Surgery (AAO–HNS) committees, and certainly through private conversations expressing the frustrations many practitioners experience with the regulations while delivering patient care.

The problem is that it’s unclear what constitutes a “demonstrated safety risk.” There are data to support many of the safety practices recommended and/or required by various regulatory or accrediting agencies. However, many of these recommendations are based on “common sense” or expert opinion taken from extrapolation of laboratory data about how we know microorganisms can be transmitted. This often means doing things because of a theoretical rather than an actual risk. So, what is the return value for enacting measures that hopefully stave off an ill-defined safety risk? At one extreme, if we swing toward complete risk aversion and advocate for a “more is more” approach when it comes to safety measures, then perhaps all of us should take Hibiclens showers and wear space suits when seeing patients.

Limited Resources

Balancing both cost and risk is a good measuring stick for determining actions and guiding principles. While our idealistic guiding principle is to do everything possible not to harm the patient, in reality, there are limited resources—not just monetary, but also limits of time and personnel. If a given intervention isn’t inconvenient and it isn’t costly, then it’s a no brainer: It should be implemented. Any other permutation is an economist’s dilemma.

For example, seat belts: easy to put on, low cost. Not exactly a straightforward example, as I’m sure it isn’t inexpensive to install seatbelts, but the risk of death is quite costly. I also remember that in the 70s and 80s people railed against automatic seatbelts as an infringement on freedom. However, if safety measures aren’t too much of an infringement and are of acceptable cost, then benefits may outweigh the risks. Sure, you can always cite an anecdotal example where it may not be good to have your seatbelt on (if your car is stuck underwater and you can’t free the belt to get out). However, these rare examples should not squash the good.

Many agencies and institutions have adopted safety measures based on conjecture, legal repercussions, and manufacturer recommendations—not data.

Regulatory agencies such as the Centers for Medicare and Medicaid Services, state bodies, and accrediting agencies such as The Joint Commission wield much influence in the direction of standards in hospital-based medical environments. These bodies will try to seek “evidence-based” guidelines to structure their guidance. And yes, “evidence-based” is often an extrapolation of in vitro experiments and not necessarily derived from clinical outcomes. However, attributing infectious disease transmission to an outpatient clinical instrument such as a nasal speculum is going to be very difficult to prove, unless you implement a system that closely tracks each instrument from the stages of reprocessing through to specific patient contact. Big data may provide associations, but they may not be granular enough to provide a pinpoint-specific cause unless you have a widespread outbreak or an outbreak of a rare infection. Therefore, many agencies and institutions have adopted safety measures based on conjecture, legal repercussions, and manufacturer recommendations—not data.