Bringing Clinical Reasoning to Morbidity and Mortality.pdf

David Gordon, MD
Jeremiah Schuur, MD, MHS
Joshua Wallenstein, MD

The presenters do not have any relevant
commercial relationships or conflict of interests
to disclose
J. Schuur
•Scientific Advisory Board of United HealthCare

• To structure M&M conference as a protected
environment for non-judgmental case review, in
order to foster professional growth
• To identify systems issues and cognitive biases
that underlie common errors
• To review cognitive strategies that can help
prevent a similar event next time

• Improve patient care
• Reduce medical error
• Maximize efficiency
• Foster collaboration
• Improve patient satisfaction

• The “director”
• The case
• The presenter
• The materials
• The audience

• One individual with oversight of the M&M
program
• Collaborative skills are key
• Background in quality and education
• Set expectations of presenters and audience
• Ensure mentorship

• Elements of a good case
• Quality issues
• System failures
• Judgment errors
• Near miss
• Case selection
• Standard reporting
• Key informants
• Patient complaints
• Anonymous reporting system

• Case investigation
• Medical record
• Involved parties
• Presentation and public speaking skills
• Interactive - discussion throughout case

• Begins with triage note (or earlier)
• Presentation in real time
• Scan or import images
• De-identify

• Awake
• Engaged
• Key players should not reveal themselves or
be revealed
• Share what’s going on in your head

Cognitive and Systems Autopsy:
Identifying Systems Factors and
Cognitive Biases and in M&M
Jeremiah Schuur, MD, MHS
Department of Emergency Medicine
Brigham & Women’s Hospital

• “Well, if it isn’t the Four
Horsewomen of the
Apocalypse. There’s a
morbidity and mortality
conference tomorrow to
figure out who’s
responsible for Foster’s
death. And here’s the
exciting news : I’m pretty
sure it was one of you.”

Patient Factors
Task Factors
Staff Factors (Individual)
Traditional M&M
-identify blame
-personal failing
Team Factors
Work Environment Factors
Systems
Issues
Organization / Management
Institutional Context
Modeled on Vincent C, et al, BMJ 2000. 320:777

• Cook County ED
• Retrospective review of
636 cases
• Two physicians
independently reviewed:
• 4 categories phase of work
(diagnosis, treatment,
disposition, and public health)
• contributing factors that likely
affected outcome (patient
factors, triage, clinical tasks,
teamwork, and system).
Cosby KS, et el. Characteristics of patient care management problems
identified in emergency department morbidity and mortality investigations
during 15 years. Ann Emerg Med. 2008 Mar;51(3):251-61, 261.

• Cook County ED
• Retrospective review of
636 cases
• Two physicians
independently reviewed:
• 4 categories phase of work
(diagnosis, treatment,
disposition, and public health)
• contributing factors that likely
affected outcome (patient
factors, triage, clinical tasks,
teamwork, and system).
Triage
System
Clinical:
Task Based
Clinical:
Skill Set
Teamwork
Clinical:
Affective
influences
Patient
Diagnosis
Clinical:
Reasoning
Cosby KS, et el. Characteristics of patient care management problems
identified in emergency department morbidity and mortality investigations
during 15 years. Ann Emerg Med. 2008 Mar;51(3):251-61, 261.
0% 20% 40% 60% 80% 100%

• Local ED environment (the microsystem):
• Triage; Policies & Procedures; Equipment; Teamwork
• Hospital environment (the macrosystem):
• Equipment, Consultants, Boarding…
• Hospital administration and third parties:
• Policies & Procedures, Insurance policies…
• Community level:
• community services, EMS policies, healthcare access…

• Education Issues
• Lack of knowledge of one or more practitioners
• Technical Error
• Error on performance of a truly indicated
procedure
• Judgment Issues
• Adequate knowledge but incorrect judgment
Modeled on: Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality
Conference” APDIM Spring Meeting, 2004

• Commission and Omission bias:
• Commission = tendency toward action rather than
inaction, due to obligation toward beneficence.
• Omission: tendency toward inaction based on
principle of nonmaleficence (first do no harm).
• Hassle Bias: tendency to avoid difficult
actions.
• call in consultant at 3am
• pelvic exam on hallway patient…
Modeled on:Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002
Nov;9(11):1184-204.and Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality Conference” APDIM Spring Meeting, 2004

• Ascertainment bias: decision-making is shaped by
prior expectation; e.g. stereotyping and gender bias.
• Availability Heuristic: judge things as being more
likely, or frequently occurring, if they readily come
to mind. Recent experience with a disease inflates
its rank on the DDx.
• “the last patient like this had PE”
• Gambler’s fallacy: the belief that if a coin is tossed
ten times and is heads each time, the 11th toss has a
greater chance of being tails.
• “We can’t see 3 patients in one shift with aortic dissection”
Modeled on:Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002
Nov;9(11):1184-204.and Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality Conference” APDIM Spring Meeting, 2004

• Anchoring: tendency to lock onto salient features in
the patient’s initial presentation too early in the
diagnostic process, and failing to adjust this initial
impression in the light of later information.
• epigastric pain as upper abdominal pain, not chest pain.
• Confirmation bias: tendency to look for and weight
confirming evidence to support a diagnosis rather
than evidence that refutes it, despite the latter
often being more persuasive and definitive.
• the WBC count is cited if it supports course of action.
Modeled on:Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002
Nov;9(11):1184-204.and Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality Conference” APDIM Spring Meeting, 2004

• Search satisfying: universal tendency to call
off a search once something is found.
• failure to thoroughly examining for additional
gunshot wounds in a trauma patient.
• Premature closure: tendency to shut down
the decision-making process prematurely,
accepting a diagnosis before it has been fully
verified. Common.
Modeled on:Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002
Nov;9(11):1184-204.and Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality Conference” APDIM Spring Meeting, 2004

• Diagnosis momentum: once diagnostic labels are
attached to patients they tend to stick. What might
have started as a possibility gathers increasing
momentum until it becomes definite.
• Triage cueing: during triage labels get applied and patients
get sorted to locations that ultimately influence
downstream providers, leading to the maxim: ‘‘Geography
is destiny.’’
• Overconfidence Bias: over reliance on the opinions
of the “expert” (PMD, consultant…)
• the opinion of a surgeon about abdominal pain.
Modeled on:Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002
Nov;9(11):1184-204.and Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality Conference” APDIM Spring Meeting, 2004

• Aggregate bias: belief that aggregated data,
such clinical decision rules, do not apply to
individual patients -- leads to errors of
commission
• ordering tests when none required.
Modeled on:Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002
Nov;9(11):1184-204.and Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality Conference” APDIM Spring Meeting, 2004

• Psych-out error: Psychiatric patients are particularly
vulnerable to cognitive errors, especially to
fundamental attribution error: co-morbid medical
conditions may be overlooked or minimized.
• Sutton’s slip: strategy of going for the obvious is
Sutton’s law. The slip occurs when possibilities other
than the obvious are not given sufficient
consideration. (Bank-robber Willie Sutton is alleged
to have told the judge that he robbed banks,
‘‘because that’s where the money is!’’)
Modeled on:Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002
Nov;9(11):1184-204.and Lucey C, Winiger D, Shim R. “Towards a More Effective Morbidity and Mortality Conference” APDIM Spring Meeting, 2004

Addressing Cognitive Biases:
Forcing functions and other tools
David Gordon, MD
Division of Emergency Medicine
Duke University

No-Fault
System
Cognitive
• Unusual presentation of
disease
• Patient refusal to be
tested
• Limitations in medical
knowledge
• Technical failures
• Organizational failures
• Testing not available
• Inadequate supervision
• Poor coordination of
care
• Knowledge
• Judgment
• Memory/Vigilance
Graber M, Gordon R, Franklin N. Acad Med 2002;71:981-992

• Cognitive awareness
• Cognitive debiasing strategies
• Cognitive training

• Providing descriptions of cognitive biases and
examples of their adverse impact on decisionmaking
• Metacognition: thinking about thinking.
Reflection on the decision making process
itself.

• Patients
• Situations
• Diagnoses

• High risk patients
• “The young, the old, and the crazy”
• The hostile, abusive, and intoxicated
Unreliable history, atypical presentations,
negative visceral response

• The Return Visit
• Diagnosis Momentum
• Visceral Bias

• High risk times
• Patient sign-out
▪ Loss of information
▪ Misinterpretation of new incoming data
• High acuity or volume
• End -of-shift (personal fatigue)

• High Risk Diagnoses
• CP: MI, PE, TAD
• Headache: SAH, meningitis, SDH
• Abd pain: appendicitis, ectopic , torsion
• Ortho: tendon & nerve injuries, foreign bodies

Heuristics or mental strategies to avoid bias
Forced Thinking

• What else could it be?
• Is there anything that
doesn’t fit?
• Is it possible that I have
more than one problem?

• Adequacy
• Are all the patient's findings
(abnormal or normal) accounted for
by the diagnostic hypothesis?
Have I explained all the patient’s findings?
• Coherency
• Is the diagnostic hypothesis
pathophysiologically consistent with
all the clinical findings?
Is there a non-fit?

High-risk
Patients
High-risk
Times
High-risk
Diagnoses
Cognitive
Awareness
Have I explained all the
patient’s findings?
Cognitive
Strategies
Could there be more than one
problem?
What else could it be?
Have I considered worst-first?
Is there a nonfit?

• Simulation – create clinical scenarios that are
high risk for cognitive errors
• Observation- training videos that contrast
biased vs. unbiased approaches
• Feedback
• Immediate
• M&M

M&M Case Discussions
Decision-Making Issues
• Cognitive Awareness/Diagnostic challenges: high risk patient,
time, or diagnosis
•Cognitive biases
Systems Issues
•E.g. difficulty with finding consultant, slow time for final
radiology reads etc.
Counterstrategies
• Forced questions
• Systems change or improvement

• Croskerry P. Achieving quality in clinical decision
making: cognitive strategies and detection of bias.
Acad Emerg Med. 2002 Nov;9(11):1184-204.
• Patient Safety in Emergency Medicine (Textbook)
• Jerome Groopman. How Doctors Think.