Ten Commandments to Reduce Diagnostic Errors

Swerlick, Robert A rswerli at EMORY.EDU
Thu Aug 28 01:59:05 UTC 2014


Bimal,

I am perplexed by your logic. The examples you chose to highlight are ones where the diagnostic tests are especially robust. They represent the exceptions rather than the rule. The utility of most diagnostic tests are heavily dependent upon the context they are deployed and for the most part, they nudge us in certain directions. They do not close the deal.

Are you rejecting the probabilistic nature of diagnoses in general?  What alternative do you offer? Hypotheses are rarely "proven true". The best you can hope for is that they are not refuted and with mounting evidence, the probability they are true becomes greater.

In addition, each diagnosis has predictions, which depend upon probabilities,  built into them. A diagnosis of AMI or PE or any other diagnosis is linked to likelihoods of specific outcomes, which can only be viewed through the lens of probability. Some of those outcomes will come about, some will not and for given populations, the particular outcomes happen with certain predictable frequencies.

Feynman did an autopsy on the Challenger. What we do in medicine is more like doing the ice water test on the o-ring before the crash and predicting an outcome.

Bob

Robert A. Swerlick, MD
Alicia Leizman Stonecipher Chair of Dermatology
Professor and Chairman, Department of Dermatology
Emory University School of Medicine
404-727-3669
________________________________
From: Jain, Bimal P.,M.D. [BJAIN at PARTNERS.ORG]
Sent: Wednesday, August 27, 2014 9:54 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] Ten Commandments to Reduce Diagnostic Errors


1.       T o improve diagnosis, it is important to understand, I believe, how it is performed in actual practice.

2.       The real life method of diagnosis overcomes two major challenges

(a)    The varying typicality of presentation of a given disease in different patients

(b)   The need to determine a disease correctly in every individual patient with symptoms.

3.       The notion of typicality is equivalent to that of pretest probability as both indicate the frequency of a disease in patients with a given presentation. Thus a highly typical presentation as well as high pretest probability of a disease indicates most patients with a given presentation having a disease.

4.       A given patient with a certain presentation can thus be looked upon as being drawn from a series of patients with similar presentations.

5.       The typicality of a presentation is therefore not evidence for or against a disease in a given patient as it refers only to a frequency in a series and not to presence or absence of disease in the given patient.

6.       In actual practice therefore, a presentation is employed only as a clue, I suggest, from which we suspect a disease in a given patient. Thus highly characteristic chest pain in a 65 year old male with multiple cardiac risk factors as well as highly uncharacteristic chest pain in a healthy 40 year old woman with no cardiac risk factor make us only suspect acute myocardial infarction (acute MI)in both these patients.

7.       The suspected disease is then assumed or postulated to be present and thus given the status of a hypothesis.

8.       The hypothesis is then evaluated by a test and if a highly informative test result with likelihood ratio (LR) of 10 or higher is observed, the hypothesis is considered correct and the suspected disease diagnosed definitively.

9.       The hypothesis of acute MI in the above two patients is evaluated by performing an EKG. If acute Q wave and ST elevation changes (acute EKG changes) with LR of 13 are observed in both patients, acute MI is diagnosed with near certainty in both patients.

10.   A test result with LR of 10 or higher is usually obtained by performing a laboratory, imaging or biopsy study and occasionally from physical examination. For example, observation of unilateral erythematous, vesicular skin lesions would confirm diagnosis of herpes zoster suspected in a patient with unilateral back pain.

11.   Clinical diagnosis in actual practice is performed, I suggest in two sequential steps:

(a)    A disease is suspected from a presentation

(b)   It is diagnosed definitively from a test result with LR of 10 or higher.
        12.During diagnosis therefore, the status of a disease is
                (a) That of a postulated disease or hypothesis in the first stage and
                (b) That of a confirmed or definitively diagnosed disease in the second stage.
        13. In the CPCs published in NEJM, these two stages are clearly seen
               (a) In the first stage, the discussing physician postulates a disease from given information (presentation) which has the status of a hypothesis
               (b) In the second stage, the postulated disease is proven correct (or not) when the pathologist gives the result of a highly informative test result which is usually a biopsy ( or autopsy) finding but
                      may be laboratory or imaging test result.
        14. In general, it is rare to make a definitive diagnosis from a presentation alone. This may occur however when a highly informative test result is part of presentation.
               For examp0le, herpes zoster is diagnosed definitively if a patient presents with painful, unilateral erythematous, vesicular skin lesions.
        15. A presentation ceases to play any further role in diagnosis once a test result with LR of 10 or higher is observed. For example, pulmonary embolism is diagnosed definitively when a positive chest
               CT angiogram (LR 21) is observed and deep vein thrombophlebitis is diagnosed definitively when a positive venous ultrasound study (LR 19) is found regardless of typicality of presentation.
        16. It is seen from above account that probability does not seem to play any significant role in diagnosis in actual practice.
        17. It is difficult to assess the value of the proposed Bayesian (probabilistic) to diagnosis in actual practice as there are hardly any published accounts of Bayesian diagnosis in actual patients. I present
               below a patient discussed in a clinical problem solving exercise (Pauker, NEJM 1992) in which Bayesian diagnosis was attempted.
        18. A healthy 40 year old woman without any cardiac risk factor presents with highly uncharacteristic chest pain and is found to have acute Q wave and ST elevation EKG changes.
        19. The pretest probability of acute MI was estimated to be 7 percent which was combined with known LR  of acute EKG changes of 13 by Bayes’ theorem to generate a post test probability of acute
               MI of  50 percent. The Bayesian diagnosis from this post test probability obviously is that acute MI is indeterminate in this patient.
        20. But the discussing physician ignored the Bayesian diagnosis and correctly diagnosed acute MI with near certainty from the strong evidence provided by acute EKG changes alone.
        21. He diagnosed in this manner, I suggest, because acute EKG changes are known to diagnose acute MI correctly in 90 percent patients regardless of pretest probability (Rude Am J Card 1983)
        22. I consider diagnosis to be a problem solving process which is similar to problem solving in any other field. It is strikingly similar for example as I discuss below, to the manner in which the great
               American physicist Richard Feynman ‘diagnosed’ the cause of explosion of space capsule Challenger in 1986.
        23. He carefully studied all available information about launch of Challenger, much as a physician discussing  a CPC would study available information about patient he is to discuss. From his study he
               suspected malfunction of a rubber O ring which served as a valve due to extremely cold temperature (28 F) at time of launch. He postulated this explanation as a hypothesis which he evaluated
               with his famous experiment conducted on television in which he dipped a replica of O ring in a glass of ice cold water. He found the O ring to become brittle and therefore incapable of functioning
               properly as a valve thereby proving his hypothesis correct.
         24. It will be noted Feynman did not employ probabilities, therefore his method is non-Bayesian. In fact, he had harsh words to say about what he felt was improper use of probabilities by NASA
                engineers. Feynman has narrated his investigation of Challenger incident in his inimitable style in his highly entertaining and instructive book  ‘What do you care whir other people think?’
         25. It would be immensely useful to all of us  if the value (or not) of Bayesian approach in diagnosis is decisively established by a well conducted experimental study.
         26. For if such a study shows a clear cut superiority over the usual approach which is about 85 percent accurate, we should all adopt it in our daily practice. If however, it is not found to be superior or
                Found to be inferior we should stop thinking about employing it and focus instead on learning more about how diagnosis is performed in actual practice.

         Bimal


        Bimal P Jain MD
        Pulmonary-CriticalCare
        NorthShore Medical Center
        Lynn MA 01904












From: Pauker, Stephen [mailto:SPauker at TUFTSMEDICALCENTER.ORG]
Sent: Tuesday, August 26, 2014 1:12 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] Ten Commandments to Reduce Diagnostic Errors

Well now, Dr Crab, one can craft similar prolems
With every (any) commandment or rule.


Rule 1: Every rule has exceptions. (Yes, but)
Rule 2: Uncertainty and variation are not going away. (So manage them)
Rule 3: Although History was important, it pales at the autopsy (or is it the MRI)
Rule 4: Since history is a kind of test, testing only if it will change plans,
             Is not an enforceable rule.
Rule 5: Publication is merely telling a convincing story to reviewers. (What  makes evidence?)
Rule 6: In a pinch, a brain outsmarts an iPad.
Rule 7: Be wary of Intuition and “Evidence.”
Rule 8: Although the uncommon can be important, Prevalence and Bayes Rule triumphs.
Rule 9: Don’t ignore System I thinking (gut feelings)
Rule 10: It’s only a guideline.”

Steve ;-)

From: Harold Lehmann [mailto:lehmann at JHMI.EDU]
Sent: Tuesday, August 26, 2014 9:30 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] Ten Commandments to Reduce Diagnostic Errors

Am I a crab to point out that “First Do No Harm” is false? Because we harm all the time—because we (and hopefully the patient) think it’s worth it. (Asking embarrassing History questions (#)…asking for disrobing…cold stethoscope…rectal exam…gagging pharyngeal exam…blood test…IV…VCUG…bone marrow aspiration…Need I go on?)

So: “First, Do the Least Necessary Harm”?

Also—re "think of serious and treatable conditions and act on them without delay”—does that reward availability bias? Or are we saying that any such “thought” means the likelihood is > 1/1,000, which I have found (in 20 years of eliciting from residents) is the threshold for referring infants to the ED for an LP, and therefore above threshold?

Or should we say: “"think of serious and treatable conditions and act on them without delay, if the likelihood is high enough”

Harold

From: "<Patrice F. Hirning>", <MD>, <MACP>, CPHRM <phirning at UMIA.COM<mailto:phirning at UMIA.COM>>
Reply-To: Society to Improve Diagnosis in Medicine <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>, "Patrice F. Hirning, MD, MACP, CPHRM" <phirning at UMIA.COM<mailto:phirning at UMIA.COM>>
Date: Monday, August 25, 2014 at 8:52 PM
To: "IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>" <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>
Subject: Re: [IMPROVEDX] Ten Commandments to Reduce Diagnostic Errors

What a great list. This should be shared with all medical students, house staff and practicing physicians. I plan to add these to my presentation to physicians about diagnostic error.

Patrice


Patrice F. Hirning, MD, MACP, CPHRM
Medical Director
UMIA Insurance, Inc.
310 East 4500 South, Suite 550
Salt Lake City, Utah 84107
Office 801.554.1145
Fax 801.531.0381
Toll Free 800.748.4380
phirning at umia.com<mailto:phirning at umia.com>



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From: Lorri Zipperer [mailto:Lorri at ZPM1.COM]
Sent: Sunday, August 24, 2014 7:13 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: [IMPROVEDX] Ten Commandments to Reduce Diagnostic Errors


Forwarded by the moderator:



from  Dr. Leonardo Leonidas, Bangor, Maine 20 May 2001  Copyright 2001

Given to his Son Len and Class 2001 Tufts University School of Medicine



   1. Thou shalt First "Do No Harm."

   2. Thou shalt think of serious and treatable conditions and act on them without delay.

   3. Thou shalt remember that Diagnosis is History, History, History.  Then confirm with clinical examination and more History.

   4. Thou shalt request a test only if it will change your plan or help in predicting the outcome.

   5. Thou shalt question "authority" such as your senior residents, attendings, experts, or even National guidelines.

   6.  Thou shalt continue the debate and questioning even though the data is "IN."

   7. Thou shalt maintain a high index of suspicion for uncommon presentations of the common.

   8. Thou shalt recognize your own beliefs, biases, prejudices, and thinking style.

   9. Thou shalt be wary of your hunches and intuitions. It is better to use Evidence Based Medicine.

  10.  Thou shalt have an iPad* or a smartphone in your palm.



*Palm Pilot in the first edition.



Leonardo L. Leonidas, MD

Assistant Clinical Professor in Pediatrics (retired 2008)

Tufts University School of Medicine, Boston, USA

nonieleonidas68 at gmail.com<mailto:nonieleonidas68 at gmail.com> <mailto:nonieleonidas68 at gmail.com>


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