Ten Commandments to Reduce Diagnostic Errors

Vic Nicholls nichollsvi2 at GMAIL.COM
Wed Sep 10 13:52:53 UTC 2014


Actually Dr. Brush, as a patient, if I ask what are other differential 
diagnoses, why are they ruled out compared to X (which according to my 
history, etc. I think are more likely), I've had a number of doctors who 
do not give me this information and label me as a difficult or non 
compliant patient for asking.

Not all, but a good number do, or at least they did, as the no longer 
see some of them.

My question to a former GI was how can you say I have anorexia nervosa 
when I didn't want to be weighed in the office as the only criteria for 
giving me that label? I'm missing over 80% of my stomach, gallbladder, 
have gastroparesis, IBS, GERD, numerous food intolerances, and your 
notes state that I appear nourished (which I am of middle range normal 
weight)? I've never had AN or BN or any other underweight issue. I 
couldn't get a percentage question answered on that one.

Victoria

On 9/10/2014 9:24 AM, John Brush wrote:
> Bimal,
> I take issue with your statement:  "In any case, a probabilistic 
> approach does not appear to be employed by physicians in actual 
> practice. Most physicians, at least the ones I know have no knowledge 
> of probability theory yet many of them are excellent diagnosticians."
>
> 1. Every day, in every hospital and every clinic you hear physicians 
> say “I think the most likely diagnosis is ….”  In that statement, they 
> are stating that the probability of a particular diagnosis is greater 
> than the probability of other possible diagnoses.
> 2. Every day I hear surgeons tell patients that they have x% risk of 
> complications and x% probability of success with a surgical procedure. 
> The Society of Thoracic Surgeons provides a website where you can 
> calculate the probability of success, mortality, and morbidity for 
> individual patients undergoing cardiac surgery.
> 3. Recently, the AHA and ACC released a major guideline document that 
> instructed physicians to prescribe statins to all patients with a 
> calculated 10-year risk of hard cardiovascular endpoints of >7%. 
> That’s an explicit use of calculated probability applied to an 
> individual. The term “risk” is synonymous with “probability.” The Mayo 
> Clinic has an excellent website that creates pictorial displays of 
> probability to help patients understand their cardiovascular risk.
>
> With these 3 examples you can clearly see that probability is actually 
> ubiquitous in practice. Physicians, like all people, estimate 
> probability intuitively using a heuristic called anchoring and 
> adjusting. That heuristic is good, but far from perfect. Anchoring and 
> adjusting isn’t going to go away, but it could be improved upon by 
> calibrating our intuitive estimates a bit and by being more reflective 
> about how we think about probability.
>
> BTW, I think you have misinterpreted Nate Silver’s methods. He used a 
> Bayesian method of constantly updating his probability estimates based 
> on incremental information from various sources. His probability 
> estimates were not simply poll results, they were progressively 
> updated estimates based on a number of information inputs. He explains 
> his Bayesian methods very well in his book.
>
> John
>
> John E. Brush, Jr., M.D., FACC
> Professor of Medicine
> Eastern Virginia Medical School
> Sentara Cardiology Specialists
> 844 Kempsville Road, Suite 204
> Norfolk, VA 23502
> 757-261-0700
> Cell: 757-477-1990
> jebrush at me.com <mailto:jebrush at me.com>
>
>
>
> On Sep 9, 2014, at 10:45 AM, Jain, Bimal P.,M.D. <BJAIN at PARTNERS.ORG 
> <mailto:BJAIN at PARTNERS.ORG>> wrote:
>
> John,
>
> The core issue we are discussing is the role of probability in 
> clinical diagnosis. You hold that diagnosis is best performed from a 
> post test probability generated by  Bayesian reasoning while I have 
> grave reservations about this approach. Let us unravel the various 
> strands of this issue.
>
> 1.Probability is a  mathematical concept that has been interpreted in 
> application subjectively as degree of belief and objectively as a 
> frequency or distribution.
>
> 2.It has been applied with great success in many fields such as 
> statistical mechanics in physics, epidemiology, insurance, stock 
> portfolio management, weather forecasting, betting, forecasting of 
> election results etc.
>
> 3.In all these fields, its application leads to predictions in large 
> groups or series made up of large numbers of objects or events, 
> billions of gas molecules in statistical mechanics, millions of voters 
> in election result forecasting.
>
> 4.A characteristic feature of its application in these fields is that 
> our focus is on accuracy in the result of a group as  whole, error in 
> prediction in given individual members is tolerated. For example, we 
> do not care if we have a loss in a particular stock as long as the 
> whole portfolio makes money.
>
> 5.Nate Silver’s remarkable result in correctly predicting election 
> results in all 50 states that you mention, was based on predictions in 
> groups of millions of voters in each state. He did not and could not 
> predict how a particular person voted in a given state. What he did  
> was what an epidemiologist does, not what a physician does in 
> diagnosing a disease in a given patient.
>
> 6.His amazing success was due The Law of Large Numbers as the eminent 
> Fields Prize winning mathematician Terence Tao has pointed out (Best 
> Writing in Mathematics 2013)
>
> 7.This law states that a predicted frequency from a probability gets 
> progressively closer to an actual frequency as a series gets larger.
>
> 8.By this lawman actual frequency will tend to deviate more and more 
> from a frequency predicted from a probability as a series gets 
> progressively smaller. In the limit when the series consists of one 
> member only, the frequency or distribution vanishes and all  we have 
> is presence or absence of an outcome which a probability  cannot 
> correctly predict.
>
> 9.In clinical diagnosis we are dealing with a given, individual 
> patient who makes up a series of one in whom probability 
> considerations do not apply.
>
> 10.It is not clear to me, why a probabilistic approach has been 
> proposed for diagnosis. Perhaps it is due to its success in 
> epidemiologic studies which is inappropriate as clinical and 
> epidemiologic are two different domains, one dealing with an 
> individual patient and the other with groups of patients. The term 
> clinical epidemiology is most unfortunate, in my view, as it seems to 
> imply that the method of epidemiology can be applied in the clinical 
> domain.
>
> 11.In any case, a probabilistic approach does not appear to be 
> employed by physicians in actual practice. Most physicians, at least 
> the ones I know have no knowledge of probability theory yet many of 
> them are excellent diagnosticians. Furthermore, in hundreds of 
> published discussions of diagnosis in actual patients in CPCs and 
> clinical problem solving exercises, a Bayesian approach has not been 
> employed. Thus in none of these discussions, the pretest probability 
> of a suspected disease is estimated, which is a hallmark of the 
> probabilistic approach. I have come across only one instance in which 
> Bayesian diagnosis was attempted(Pauker NEJM 1992) where it performed 
> poorly leading to an incorrect diagnosis.
>
> 12.The method of diagnosis employed in actual practice consists, I 
> suggest of suspecting one or more diseases from a presentation, 
> formulating them as hypotheses and diagnosing one of them definitively 
> when a highly informative test result is observed. In this method, a 
> suspected disease is not assigned a pretest probability, its status is 
> indeterminate, it is a hypothesis. It is proven correct or incorrect, 
> which corresponds to presence or absence of disease in the given 
> patient from a highly informative test result..
>
> 13.This is the method used in all of science. It is the same method 
> used by Feynman in finding the cause of explosion of space shuttle 
> Challenger as I discussed earlier.
>
> 14.The accuracy of diagnosis in actual practice, Mark Graber tells us 
> is 85 percent. The aim of our Society is to reduce or eliminate the 15 
> percent diagnostic error rate.
>
> 15.The pioneering studies of Gordon Schiff, Hardeep Singh, John Ely 
> have shown that failure to suspect a disease in patients with atypical 
> presentations is the commonest cause of diagnostic errors.
>
> 16.A probabilistic approach is likely to increase this failure as the 
> low pretest probability in these patients is likely to be interpreted 
> as low  plausibility or low pretest evidence increasing the likelihood 
> of a disease being ruled out without further testing.
>
> 17.I would like to emphasize I am not questioning the mathematical 
> correctness of Bayes’ theorem on which the probabilistic approach is 
> based. It is an elegant, mathematically consistent theorem which is 
> derived from the axioms of probability in a straightforward manner.
>
> 18.But its mathematical correctness does not ensure its correctness in 
> application. In this regard, the fine saying of Einstein ‘As far as 
> the laws of mathematics refer to reality they are not certain and as 
> far as they are certain they do not refer to reality’ in his great 
> essay Geometry and Experience is very relevant.
>
> *19.*A correct application depends on features in the real world which 
>  correspond to mathematical concepts. Bayes’ theorem fails to apply 
> because  probability gives correct results in groups of patients only 
> such as in epidemiologic studies while clinically we diagnose a 
> disease in a given individual *patient.*
>
> *20.*Finally, regardless of what you or I believe, the issue about 
> probabilistic diagnosis can only be settled like all issues in science 
> by experiment.**
>
> *21.*I suggest studies be conducted in large numbers of patients to 
> compare accuracy rates in Bayesian  and usual diagnosis. **
>
> *22.*If well conducted studies show clear-cut superiority of one or 
> the other method, we should accept the verdict regardless of our 
> personal belief.**
>
> *23.*Our Society consisting of physicians interested in diagnosis 
> could take the initiative in conducting such studies.**
>
> *24.*The results of such studies would have important implications, I 
> believe, in teaching clinical diagnosis to novice physicians including 
> medical students and in reducing diagnostic errors.**
>
> *Bimal*
>
> **
>
> **
>
> *Bimal P Jain MD*
>
> *Pulmonary-CriticalCare*
>
> *NorthShore Medical Center*
>
> *Lynn MA 01904*
>
> **
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> **
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>
> *From:**John Brush [mailto:jebrush at ME.COM]
> Sent: Saturday, August 30, 2014 10:18 AM
> To:*IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG 
> <mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
> *Subject:* Re: [IMPROVEDX] Ten Commandments to Reduce Diagnostic Errors
>
> Bimal,
>
> You are confusing variability of clinical manifestations with 
> probability of a disease. The range of possible manifestations of a 
> disease is not the same thing as the range of possible diagnoses that 
> might explain a manifestation. This is shown by the figure from the 
> article by Goodman that was referenced in a previous email response 
> from David Newman-Toker (see below). We explain variability of disease 
> deductively. CPCs and book chapters are set up this way.  But we make 
> diagnoses inductively, starting with the manifestation and working 
> backward toward a general category, or diagnosis. This requires 
> inverse probability, induction, Bayesian inference, and the use of 
> conditional probability.
>
> <image001.png>
>
> This has actually been thoroughly discussed in the literature. (see 
> the classic article by Diamond and Forrester: Analysis of probability 
> in the clinical diagnosis of coronary-artery disease. N Engl J Med 
> 1979;300:1350-1358). I don’t think you could pass the cardiology 
> boards without an understanding of conditional probability and 
> Bayesian logic as it applies to stress testing, troponin, BNP, 
> d-dimers, and other tests. There was classic series in JAMA 
> commissioned by David Sackett that was compiled in excellent book, The 
> Rational Clinical Examination by Simel and Rennie where the use of 
> conditional probability was expanded to simple physical exam findings. 
> Bayesian logic was also discussed in the lay literature by Nate Silver 
> in The Signal and the Noise. He used Bayesian logic to predict the 
> outcome of the last election and was 50/50 in his predictions.
>
> Your call to only use tests with likelihood ratios of greater than 10 
> is simply unrealistic. A test has to have a sensitivity of at least 
> 90% and a specificity of at least 91% to have a LR(+) of 10. A test 
> that good is almost non-existent. As I stated in a prior email, 
> imaging stress tests have a LR(+) of 6 and a positive troponin is 4.7. 
> ST elevation on EKG has a high LR(+), but only when used in a specific 
> setting, and using a very restrictive criteria of ST elevation.
>
> Learning about variability of clinical manifestations is already part 
> of our training. We know that there can be formes fruste of 
> disease. We are all taught that an unlikely manifestation of a common 
> disease is more likely than an uncommon disease. Representativeness is 
> when you fall for an uncommon disease because of unusual 
> manifestations and you ignore the more likely possibilities. In fact, 
> virtually all of the fallacies described by Daniel Kahneman are 
> misinterpretations of probability (representativeness, availability, 
> anchoring and adjusting).  The solution is to think more about 
> probabilities, not less, and to try to be a bit more quantitative and 
> precise. Probabilistic thinking, even if it is semi-quantitative, 
> provides a framework for double checking our own thinking and 
> thoughtfully examining our conclusions.
>
> Likelihood ratios are useful as multipliers to help us calculate 
> probability. And they help us understand the relative strength of new 
> information. But they don’t really make sense unless you use them in 
> conjunction with pre-test odds to calculate post-odds, then post test 
> probability. I don’t know how you can talk about likelihood ratios 
> while criticizing the value of using probability in practice. This 
> doesn’t seem logical to me.
>
> We seem to be going over the same ground over and over. But I think we 
> need to have some clear logic for the readers of the listserv.
>
> Thanks.
>
> John
>
> John E. Brush, Jr., M.D., FACC
>
> Professor of Medicine
>
> Eastern Virginia Medical School
>
> Sentara Cardiology Specialists
>
> 844 Kempsville Road, Suite 204
>
> Norfolk, VA 23502
>
> 757-261-0700
>
> Cell: 757-477-1990
>
> jebrush at me.com <mailto:jebrush at me.com>
>
> On Aug 27, 2014, at 9:59 PM, Swerlick, Robert A <rswerli at EMORY.EDU 
> <mailto:rswerli at EMORY.EDU>> wrote:
>
> 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 
> <mailto:BJAIN at PARTNERS.ORG>]
> *Sent:* Wednesday, August 27, 2014 9:54 AM
> *To:* IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG 
> <mailto: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 
> <mailto: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>
>
>
> The information contained in this e-mail message is privileged and/or 
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> If you have received this message in error, please immediately notify 
> UMIA Insurance, Inc. and delete this message from your computer. Thank 
> you.
>
> *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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