[EXTERNAL] [IMPROVEDX] Bayesian diagnosis

Jain, Bimal P.,M.D. BJAIN at PARTNERS.ORG
Wed Jul 23 11:27:28 UTC 2014


Hi everyone,

These are all great comments.  There are three things, I believe, which make clinical diagnosis challenging.

(a)    The wide variation in clinical presentations and therefore of pretest probabilities of a given disease in different patients.

(b)   The need to diagnose a disease correctly in every individual patient.


(c)    The need to validate a definitive diagnosis of a disease in a given patient by our experience of diagnosing the same disease in the same manner correctly in other patients.



I believe our usual method of diagnosis in actual practice is designed to meet these three challenges.  It consists of suspecting a disease or diseases from a presentation and diagnosing it definitively when a highly informative test result (LR 10 or higher is observed). From what I have gathered from personal observation, study of CPCs and clinical problem solving exercises, probabilistic reasoning does not play any significant role in actual practice. But we know so little about diagnosis in actual practice I may not be right. What we need are studies , both observational and experimental of how physicians actually  diagnose. One specific question I would like answered is the role of a clinical presentation. Is it perceived only as a problem, as I believe, whose function is only to make us suspect a disease which is then diagnosed from a highly informative test result ? Or is it a source of pretest evidence in the form of a pretest probability which is combined with evidence from a  test result in form of LR  as the Bayesians hold?



Regards



Bimal



Bimal Jain MD

Pulmonary-Critical Care

NorthShore Medical Center

Lynn MA 01904





From: Ted.E.Palen at KP.ORG [mailto:Ted.E.Palen at KP.ORG]
Sent: Saturday, July 19, 2014 9:02 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] [EXTERNAL] [IMPROVEDX] Bayesian diagnosis

Hi Michael
You make some great points. I have always been troubled by trying to select/guess/determine a prior probability. It is often just a guess or estimated from populations that may have nothing to do with the patient in front of me. As we improve our ability to collect information about our patients from diverse sources and information about our local healthcare environment and as our point-of-care analytics mature it may be possible to feed patient information into software like Watson, CareFlow, or similar programs to produce a patient specific pre-test probability. Perhaps the AI could help direct our path of testing and point us to better ddx.
Sent from my iPhone

On Jul 19, 2014, at 6:47 PM, "Michael H Kanter" <Michael.H.Kanter at nsmtp.kp.org<mailto:Michael.H.Kanter at nsmtp.kp.org>> wrote:
In my view, part of the problem with Bayesian approaches is that of obtaining the prior probability.  Most examples attempt to use the prevalence of the disease in the population giving the appearance of it sounding scientific.  This is probably okay when screening asymptomatic people for certain diseases where the physician actually never sees individual patients but is instead looking at the population.   Once the patient is seen by the physician, the prior probability changes as not all patients are alike.
.  In true Bayesian statistics, the prior probability is a subjective probability. (this is also one reason why many statisticians do not feel Bayesian statistics are "scientific").  So, if a patient presents with certain symptoms, the prior probability should ideally be determined by the physician who uses all information about the patient other than the various tests/symptoms that are used to modify the prior probability to create a final conditional probability that the patient has a disease given certain symptoms or test results.  This then becomes an exercise in subjectivity where the physicians' intuition, prior experience, and "gut feel" may and should be used in determining prior probabilities.   Most advocates of a Bayesian approach simply select a prior probability based on a few variables and require that all physicians use that prior probability.  This is a fundamental flaw, in my view.
 In the example below (item 6) where Bayesian approaches may be problematic due to failure to consider a rare disease, this  failure to consider a rare disease can be viewed as simply using the wrong prior probability for the more common disease.  Physicians who consider the rare disease would have a much lower prior probability of the non rare disease than those who do not consider the rare disease.
Thus, the concept that a Bayesian approach is more objective may not be correct.   One can force it to be objective by requiring use of a certain prior probability but this then creates other potential errors and in theory is less accurate because it may not consider variables that are available to the physician seeing the patient but not in the model.   Advocates of a Bayesian approach seem to forget that the prior probability is generally not known but is merely a subjective "guess".   So, comparing a Bayesian approach to the usual approaches is something that would be real interesting to do yet, one must be careful in defining exactly what a Bayesian approach means.

Michael Kanter, M.D.
Regional Medical Director of Quality & Clinical Analysis
Southern California Permanente Medical Group
(626) 405-5722 (tie line 8+335)
THRIVE By Getting Regular Exercise

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From:        "Graber, Mark" <Mark.Graber at VA.GOV<mailto:Mark.Graber at VA.GOV>>
To:        IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Date:        07/19/2014 10:03 AM
Subject:        Re: [IMPROVEDX] [EXTERNAL] [IMPROVEDX] Bayesian diagnosis
________________________________



I'd love to see the kind of comparative study Bimal is advocating.  One of the suggestions on how to improve diagnosis that seems like it should work is exactly what Bimal is advocating: expanding the differential diagnosis and weighing the options appropriately.  Hardeep's work found that in cases of dx error there was no differential diagnosis listed 80% of the time.  Would this be an antidote to dx error, as Jason Maude has suggested (Differential diagnosis: The key to reducing diagnostic error?   DIAGNOSIS 2014; 1:107-109).

Just to make a point though:  The Ledley\Lusted approach was to use the full range of conditional probability analysis in diagnosis, which includes first deriving all the possible candidate diagnoses, then weighing the evidence pro and con, and using tests to adjust probabilities to narrow the list down..  It seems that when people refer to Bayesian analysis these days they are referring to the narrower usage of Bayes theorem to calculate the probability of a SINGLE disease probability.  This focus on just one entity is exactly the opposite of what Ledley\Lusted were advocating, and to the extent that it discourages thinking about all the other diagnostic options, could work against diagnostic accuracy.


________________________________
From: Lorri Zipperer <Lorri at zpm1.com<mailto:Lorri at zpm1.com>>
Reply-To: Society to Improve Diagnosis in Medicine <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>, Lorri Zipperer <Lorri at zpm1.com<mailto:Lorri at zpm1.com>>
Date: Thu, 17 Jul 2014 17:05:08 -0400
To: <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>
Subject: [EXTERNAL] [IMPROVEDX] Bayesian diagnosis

**** moderator forwarded ****

-----Original Message-----
From: Jain, Bimal P.,M.D. [mailto:BJAIN at PARTNERS.ORG]
Sent: Wednesday, July 16, 2014 5:35 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>; 'Graber, Mark'
Subject: RE: [IMPROVEDX] [EXTERNAL] [IMPROVEDX] Patient-Provider
Communication and Diagnostic Error

Great references. Here are some thoughts on a somewhat unrelated topic--
Bayesian diagnosis.

1. Since Ledley and Lusted introduced a probabilistic (Bayesian) approach to
clinical diagnosis in 1959 (SCIENCE 1959), innumerable books and papers have
been written advocating its use in actual practice.

2. However, this does not seem to have happened to any great extent in last
55 years. For example, CPCs in NEJM continue to be discussed in a manner
that is not Bayesian. Also discussions about diagnosis in clinical problem
solving exercises on actual patients published in journals do not employ a
Bayesian approach. In the occasional patient in whom it is employed, it does
not come out looking too good as it seems to suggest an erroneous diagnosis
(Pauker, NEJM 1992).

3. There is no published evidence it is superior to the usual method of
diagnosis in actual practice which is about 85 percent accurate (Graber BMJ
2013). There are no head to head trials which demonstrate superiority of
Bayesian over usual method of diagnosis.

4. It is ironic, in this day and age of evidence based medicine, Bayesian
diagnosis continues to be advocated without any evidence for its
superiority.
If it were a treatment, this issue would have been settled a long time back
by a controlled trial.

5. In all studies on diagnostic errors (Gordon Schiff, Hardeep Singh, Mark
Graber), the method of diagnosis has not been identified as a source of
diagnostic error. How would switching to a Bayesian approach reduce error?

6. One of the major causes of errors found in these studies is a failure to
suspect a disease in patients with atypical presentations. This error has
potential to increase with a Bayesian approach in which a low pretest
probability may be interpreted as low plausibility or low pretest evidence
leading to ruling out a disease without testing.

7. A Bayesian approach appears attractive, it seems, due to consistency and
elegance of underlying Bayes theorem which it employs.But reliance solely on
mathematical criteria without observation can be misleading as seen from the
well known example of planetary orbits. For over two thousand years,
planetary orbits were believed to be circular due to mathematical beauty and
elegance of a circle as a geometrical figure. This belief was found to be
erroneous when Kepler observed positions of Mars and calculated its orbit to
be elliptical.

8. I believe we need to have observational evidence of some sort, probably
from a controlled trial, to decide if Bayesian diagnosis is superior to
usual diagnosis or not. Only if it is found to be superior can it be
recommended for widespread use.

Bimal

Bimal Jain MD
NorthShore Medical Center
Lynn MA 01904

-----Original Message-----
From: Graber, Mark [mailto:Mark.Graber at VA.GOV]
Sent: Tuesday, July 15, 2014 5:54 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] [EXTERNAL] [IMPROVEDX] Patient-Provider
Communication and Diagnostic Error

Barbara - Thanks for asking a GREAT question.  I think we all know the
answer.   The bad news is that I don't think you'll find a paper that
clearly links effective communication with improved diagnostic accuracy.
The reason is that so far we don't have a good way to actually measure
diagnostic accuracy.  We really need that tool to start making progress in
studying diagnostic accuracy and error.  David Sofen (Palo Alto Foundation
Medical Group) and I have discussed this exact issue a couple of times, and
the possibility of looking to see if there is correlation between
communication style and diagnostic accuracy just amongst all the physicians
in a large practice.  Hopefully he will weigh in on his own as well.


The good news is that there is PLENTY of INDIRECT evidence to support the
relationship between effective communication and diagnostic quality:

1)   Communication breakdowns are a common cause of diagnostic error.
(Graber et al.  Diagnostic Error in Internal Medicine.  Arch Int Med (2005).
165:1493-99).  Hardeep Singh also has a series of publications on diagnostic
errors arising from breakdowns in lab communications, which is a related
issue.

2)  As Charlene Weir points out, there is evidence that the diagnosis
emerges from the history alone in the majority of cases.    (Peterson MC,
Holbrook JH, Von Hales D, et al. Contributions of the history, physical
examination, and laboratory investigation in making medical diagnoses. The
Western Journal of Medicine. BMJ 1992;156:163-5.  Wahner-Roedler DL, Chaliki
SS, Bauer BA, et al. Who makes the diagnosis? The role of clinical skills
and diagnostic test results. J Eval Clin Pract 2007;13:321-5.)  There is
also a famous Osler quote on this:  "Listen to your patient, he is telling
you the diagnosis".

3  There is substantial evidence that high levels of patient satisfaction
(typically reflecting communication) correlate with improved health outcomes
in general. Articles that David sent me address this point:

§  Doyle, C., Lennox, L., & Bell, D. (2013). "A systematic review of
evidence on the links between patient experience and clinical safety and
effectiveness." BMJ Open, 3. Available at
http://bmjopen.bmj.com/content/3/1/e001570.full. This study finds consistent
positive associations among patient experience, patient safety, and clinical
effectiveness for a wide range of disease areas, settings, outcome measures,
and study designs. It finds that patient experience is positively associated
with clinical effectiveness and patient safety, and the results support the
case for the inclusion of patient experience as one of the central pillars
of quality in health care.

§  Boulding, W., Glickman, S. W., Manary, M. P., Schulman, K. A., & Staelin,
R. (2011). "Relationship between patient satisfaction with inpatient care
and hospital readmission within 30 days", American Journal of Managed Care,
17(1), 41-48. Focusing on three common ailments-heart attack, heart failure
and pneumonia-the authors measured 30-day readmission rates at roughly 2,500
hospitals and found that patient satisfaction scores were more closely
linked with high-quality hospital care than clinical performance measures.
Hospitals that scored highly on patient satisfaction with discharge planning
also tended to have the lowest number of patients return within a month for
all three specified ailments. Overall, high patient satisfaction scores were
more closely linked to a hospital's low readmission rates than a solid
showing on clinical performance measures.

§  Glickman, S. W., Boulding, W., Manary, M., Staelin, R., Roe, M. T.,
Wolosin, R. J., et al. (2010). "Patient satisfaction and its relationship
with clinical quality and inpatient mortality in acute myocardial
infarction", Cardiovascular Quality and Outcomes, 3(2), 188-195. Hospitals
use patient satisfaction surveys to assess their quality of care. The
objective of this study was to determine whether patient satisfaction is
associated with adherence to practice guidelines and outcomes for acute
myocardial infarction and to identify the key drivers of patient
satisfaction. The authors found that higher patient satisfaction is
associated with improved guideline adherence and lower inpatient mortality
rates, suggesting that patients are good discriminators of the type of care
they receive. Thus, patients' satisfaction with their care provides
important incremental information on the quality of acute myocardial
infarction care.

§  Isaac, T., Zaslavsky, A. M., Cleary, P. D., & Landon, B. E. (2010). "The
relationship between patients' perception of care and measures of hospital
quality and safety", Health Services Research, 45(4), 1024-1040. The overall
rating of the hospital and willingness to recommend the hospital had strong
relationships with technical performance in all medical conditions and
surgical care. The authors found that better patient experiences for each
measure domain were associated with lower decubitus ulcer rates, and for at
least some domains with each of the other assessed complications, such as
infections due to medical care.

§  Jha, A. K., Orav, E. J., Zheng, J., & Epstein, A. M. (2008). "Patients'
perception of hospital care in the United States", New England Journal of
Medicine, 359, 1921-1931. This study assessed the performance of hospitals
across multiple domains of patients' experiences and found a positive
relationship between patients' experiences and the quality of clinical care.
The authors found that patients who received care in hospitals with a high
ratio of nurses to patient-days reported somewhat better experiences than
those who received care in hospitals with a lower ratio, and hospitals that
performed well on the HCAHPS survey provided a higher quality of care across
all measures of clinical quality than did those that did not perform well on
the survey.


Mark L Graber MD FACP
President, SIDM
Sr Fellow, RTI International



________________________________
From: Barbara Balik <Barbara at THECOMMONFIRE.COM<mailto:Barbara at THECOMMONFIRE.COM>>
Reply-To: Society to Improve Diagnosis in Medicine
<IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>, Barbara Balik
<Barbara at THECOMMONFIRE.COM<mailto:Barbara at THECOMMONFIRE.COM>>
Date: Tue, 15 Jul 2014 10:30:03 -0400
To: <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>
Subject: [EXTERNAL] [IMPROVEDX] Patient-Provider Communication and
Diagnostic Error

I am looking for citations that link effective patient-provider
communication and improvement in diagnosis accuracy.

Effective communication from the patient's view is often described as an
effective listener, understanding the patient's story, and respecting values
and choices

I appreciate any help



Barbara Balik, RN, EdD

Balik.Barbara at gmail.com<mailto:Balik.Barbara at gmail.com>
www.thecommonfire.com <http://www.thecommonfire.com<http://www.thecommonfire.com/>>

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