Quantifying ER misdiagnoses

Bob Latino blatino at RELIABILITY.COM
Mon Jul 24 10:19:59 UTC 2017


I know many will already be familiar with the  work of statistician Abraham Wald from WWII on assessing where armor should be reinforced on aircraft returning from battle, based on the bullet hole distributions. Could this bias play a role in Karen's message below about 'n' in diagnosis error...when we really don't know the number of cases not reported?  I will yield to those more knowledgeable in statistics to see if this is a fair analogy.

Survivorship bias or survival bias is the logical error<https://en.wikipedia.org/wiki/Logical_error> of concentrating on the people or things that made it past some selection process and overlooking those that did not, typically because of their lack of visibility. This can lead to false conclusions in several different ways. It is a form of selection bias<https://en.wikipedia.org/wiki/Selection_bias>.
Survivorship bias can lead to overly optimistic beliefs because failures are ignored, such as when companies that no longer exist are excluded from analyses of financial performance. It can also lead to the false belief that the successes in a group have some special property, rather than just coincidence (correlation proves causality<https://en.wikipedia.org/wiki/Post_hoc_ergo_propter_hoc>). For example, if three of the five students with the best college grades went to the same high school, that can lead one to believe that the high school must offer an excellent education. This could be true, but the question cannot be answered without looking at the grades of all the other students from that high school, not just the ones who "survived" the top-five selection process.

In the military (application)
During World War II, the statistician Abraham Wald<https://en.wikipedia.org/wiki/Abraham_Wald> took survivorship bias into his calculations when considering how to minimize bomber losses to enemy fire. Researchers from the Center for Naval Analyses had conducted a study of the damage done to aircraft that had returned from missions, and had recommended that armor be added to the areas that showed the most damage. Wald noted that the study only considered the aircraft that had survived their missions—the bombers that had been shot down were not present for the damage assessment. The holes in the returning aircraft, then, represented areas where a bomber could take damage and still return home safely. Wald proposed that the Navy instead reinforce the areas where the returning aircraft were unscathed, since those were the areas that, if hit, would cause the plane to be lost.[8]<https://en.wikipedia.org/wiki/Survivorship_bias#cite_note-Mangel1984-8>[9]<https://en.wikipedia.org/wiki/Survivorship_bias#cite_note-Wald1943-9>

[https://upload.wikimedia.org/wikipedia/commons/thumb/9/98/Survivorship-bias.png/220px-Survivorship-bias.png]<https://en.wikipedia.org/wiki/File:Survivorship-bias.png>
The damaged portions of returning planes show locations where they can take a hit and still return home safely; those hit in other places do not survive

https://en.wikipedia.org/wiki/Survivorship_bias

For more on this topic and the Wald case:

Ellenberg, Jordan. 2014. How Not to be Wrong: The Power of Mathematical Thinking. New York, New York. Penguin Press.


Robert J. Latino, CEO
Reliability Center, Inc.
1.800.457.0645
blatino at reliability.com
www.reliability.com
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From: Karen Cosby [mailto:kcosby40 at GMAIL.COM]
Sent: Sunday, July 23, 2017 6:07 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] Quantifying ER misdiagnoses

The problem is that we don't have a value for  "n", the number of all such diagnoses, rather just reports of recognized misses. We often assume they are among the most common mis-diagnoses in Emergency Medicine when in fact they are probably just the missed diagnoses that are most consequential, and thus most likely to be noticed. Since many conditions can have differing presentations, it is difficult to ever establish the denominator-- the incidence of all cases. Some diagnoses are probably more commonly missed but have less impact on outcome. By default we focus on the those cases that result in poor (and potentially preventable) outcome.

Sent from my iPhone

On Jul 23, 2017, at 4:44 PM, Pat Croskerry <croskerry at EASTLINK.CA<mailto:croskerry at EASTLINK.CA>> wrote:
Gina: I don’t know of any demographic studies in the ED that have ranked diagnoses in terms of their failure rate, although a recall some data from an unpublished study in which respiratory, cardiac and missed injuries diagnoses were ranked as the most likely – in that order.
The psychologists Hogarth specifically described the emergency department as a ‘wicked environment’ – the implication being that the work conditions and ambient conditions generally were not supportive of good decision making.
The most recent study on ED diagnostic errors that I am aware of is that by Okafor N, Payne VL, Chathampally Y, et al. Using voluntary reports from physicians to learn from diagnostic errors in emergency medicine. Emerg Med J 2016;33:245–52.
Pat Croskerry

From: Gina Siddiqui [mailto:gina.siddiqui at GMAIL.COM]
Sent: July 23, 2017 5:06 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: [IMPROVEDX] Quantifying ER misdiagnoses

Hi all,

Wanted to ask the group's knowledge on studies that have ranked diagnoses in the ER by the likelihood they are missed/mistaken for something else -- an empirical/quantitative measure of the "noise" attribute of certain diagnoses that Dr. Croskerry refers to.

I'm especially interested in estimates for the prevalence of diagnostic error in PE, ectopic pregnancy, CVA, and SAH.

Gina

Gina M. Siddiqui, MD
Yale New Haven Hospital
Mobile: 703-973-3470
Twitter: @gina_wrote<https://twitter.com/Gina_wrote>



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