Surgeons Are Revealing Their Wildest And Scariest Moments They’ve Had On The Job, And I’m Screaming

21.

“I’m a surgeon, and I also do litigation work. So, most ‘errors’ in surgery are not related to someone slipping and accidentally cutting the wrong thing, although that can happen with misidentification of anatomy such as bile ducts during gallbladder removal (a surprisingly common error) — it’s usually errors in decision-making, such as the timing of surgery. There are, however, sentinel or never events that you hear about (retained objects, operating on the wrong organ, wrong side, etc.) — that also are surprisingly common. These are considered systemic errors — on root cause analysis, they usually aren’t due to just one person, but a series of mishaps involving multiple people and multiple processes.”

“Despite time-outs (everyone verbally agreeing to the demographics, operation, allergies, laterality, etc.) before starting, double counting instruments and surgical sponges, and even RFID-embedded instruments and surgical sponges, surgeon marking of the site, and patient marking of the site, objects get left behind, and wrong sides get operated on, still. The field has not figured out how to get that number to zero. As you can imagine, ORs can be extremely busy and complicated places. I’m super paranoid about these types of errors since I’ve done research and litigation work in them, but the mistakes that haunt me are the ones regarding decision-making — particularly waiting too long to operate on someone because you’re trying to decide if they truly have the diagnosis that you suspect, or even if they do, if it can be better managed non-operatively because they are a poor surgical candidate and might die from the operation anyway. But it’s tough because you can’t just make a personal policy — like, in ‘X’ situation, I’m always going to operate!’ just to avoid the mistake of waiting. Because you will have performed morbid procedures on more patients that required it. This is also why pure algorithms or objective data from labs and imaging studies, even with machine learning, can’t always make the right decision. There are subtle, nuanced factors that experience, instinct, feeling out what the patient wants, and guessing what will cause more damage (operating or not operating) that play into it.”

—Anonymous