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Patient Safety Tip
of the Week
November
16, 2021
Cognitive
Biases and Heuristics in the Delivery Room
Cognitive biases unfortunately often impact our
diagnostic and therapeutic decisions in a negative manner. One such bias
coloring our thinking is the “availability” bias (also known as the “recency”
bias). This is where the most recent or most memorable cases from the past
narrow our thinking about a current patient. We discussed this (and a variety
of other biases) in our August 12, 2008 Patient Safety Tip of the Week “Jerome
Groopman’s “How Doctors Think”. We
all know how a previous bad experience with use of a medication may influence
us not to use it again, even when we know the medical evidence tells us we
should use it (one of the reasons so many patients with atrial fibrillation are
never placed on coumadin). We often encountered this when trying to get
surgeons to use DVT prophylaxis. Some would remember vividly one of their
patients who had a hemorrhage when on DVT prophylaxis, so they would be
reluctant to use DVT prophylaxis again.
Heuristics are simplified decision tools or
shortcuts that are sometimes used in more complex decision-making scenarios. One
researcher recently looked to see if decisions in the delivery room were
influenced by such heuristics (Singh
2021). Singh explains that this
behavior can be loosely predicted by a “win-stay/lose-shift” heuristic,
according to which the decision-maker either switches strategies if the last
outcome was a “loss” or continues with the same strategy if the last outcome
was a “win.”
Singh
examined records of over 86,000 deliveries from an urban and a suburban
academic hospital over a 20-year period to see whether complications in the
prior patient’s delivery mode (whether a cesarean or vaginal delivery) make the
physician more likely to switch to the other delivery mode for their next
patient, and the effect of this heuristic on the patient for whom it is used.
The analysis suggested that, if the prior patient had complications in one delivery
mode, the physician will be more likely to switch to the other—and likely
inappropriate—delivery mode for the subsequent patient, regardless of patient
indications. Moreover, there was evidence that this heuristic has small,
suboptimal effects on patient health.
Though it is difficult to assess from administrative data
whether a delivery mode is appropriate or not, Singh does attempt to show that
some of these delivery mode decisions were likely inappropriate for patients.
According to Singh (Singh
2021b), complications during a
vaginal delivery increased the likelihood of a subsequent C-section by up to
3.6%. That was about 23 potentially inappropriate C-sections per year per
hospital studied. Complications during a cesarean increased the likelihood of a
subsequent vaginal delivery by up to 3.4%. That’s about 50 potentially
inappropriate vaginal deliveries per year per hospital studied.
In the
editorial accompanying the Singh study, Li and Colby (Li 2021) emphasize that she found that “more
experienced physicians use this decision rule more often, even though
physicians who rely more on this rule have worse patient outcomes over time.
The result demonstrates that the use of heuristics or simplified decision rules
is a common human tendency even among smart, well-intentioned, highly trained
doctors.”
Li
and Colby also point out three well-established cognitive biases as potential
causes of this maladaptive switching of delivery modes, whether individually or
in concert: recency, affect heuristic, and confirmation bias. We’ve already
noted the recency (or availability) bias above. That is where the most recent
or most memorable cases from the past narrow our thinking about a current
patient. The “affect heuristic” notes that people rely on their affective
response to gauge how large a risk is and that a highly emotional response to a
previous delivery complication would lead a physician to overestimate the risk
in a subsequent delivery. Thirdly, our frequent nemesis – confirmation bias –
may also come into play. They note that a physician may inadvertently seek and
interpret evidence in a way that is consistent with his/her fears or concerns
about a potential complication, which helps them feel comfortable that modality
switching is appropriate for the current patient.
Singh
gives another common example of this “win-stay/lose-shift” heuristic – a
physician may be reluctant to prescribe a certain drug if a previous patient
suffered an adverse event related to that drug (Singh 2021b). She notes that at least 2 interventions we
use (clinical decision support algorithms and nudges) may help reduce use of
this heuristic. The algorithms in clinical decision support systems suggest
what might be best for a particular patient. The “nudge” example is placing a
preferred drug at the top of a drop-down list and placing a drug that might
best be avoided near the bottom of that drop-down list.
Our
own experience would fit with that in Singh’s study. For years, hospitals have
tracked VBAC (vaginal birth after cesarian) rates. The push was to get more
VBAC’s done and reduce the C-section rates. We would see a cyclicality to VBAC
rates. There would be a trend toward more VBAC’s. Then, after an obstetrician
encountered a ruptured uterus in a patient with a planned VBAC (or even heard
of such a case), the VBAC rates would plummet again.
The Singh study is one of few that have actually studied real-life use of heuristics in medical
decision making by physicians. A systematic review on cognitive biases and
heuristics in medical decision making (Blumenthal-Barby 2015)
concluded that most of the studies on biases and heuristics in medical decision
making are based on hypothetical vignettes, raising concerns about
applicability of these findings to actual decision making. It also concluded
that biases and heuristics have been underinvestigated
in medical personnel compared with patients.
As
physicians, we rarely are aware of cognitive biases underlying our medical
decisions. And we also typically are not aware of when we are using a heuristic
that may be inappropriate. And our patients are often subject to similar
cognitive biases and inappropriate heuristics in their own medical thinking.
One wonders if these phenomena have increased in the “age of disinformation”.
We agree that having more real-life examples of maladaptive use of these would
be helpful.
Some of our prior columns on diagnostic
error and cognitive biases:
- September 28, 2010 “Diagnostic Error”
- November
29, 2011 “More
on Diagnostic Error”
- May
15, 2012 “Diagnostic
Error Chapter 3”
- May 29, 2008 “If You Do RCA’s or Design Healthcare
Processes…Read Gary Klein’s Work”
- August 12, 2008 “Jerome Groopman’s
“How Doctors Think”
- August 10, 2010 “It’s Not Always About The
Evidence”
- January
24, 2012 “Patient
Safety in Ambulatory Care”
- October
9, 2012 “Call
for Focus on Diagnostic Errors”
- March
2013 “Diagnostic
Error in Primary Care”
- May
2013 “Scope
and Consequences of Diagnostic Errors”
- August
2013 “Clinical
Intuition”
- January
2014 “Trigger
Tools to Prevent Diagnostic Delays”
- January
14, 2014 “Diagnostic
Error: Salient Distracting Features”
- May
2014 “Frequency
of Diagnostic Errors in Outpatients”
- June
24, 2014 “Lessons
from the General Motors Recall Analysis”
- November
25, 2014 “Misdiagnosis
Due to Lab Error”
- April
21, 2015 “Slip
and Capture Errors”
- January
2020 “The Joint Commission on Closing the Loop”
- April
20, 2021 “Taser
“Slip and Capture Error” Again!”
- And our review of Malcolm Gladwell’s “Blink” in our Patient Safety Library
References:
Singh
M. Heuristics in the delivery room. Science 2021; 374(6565): 324-329
https://www.science.org/doi/10.1126/science.abc9818
Singh
M. People use mental shortcuts to make difficult decisions – even highly
trained doctors delivering babies. The Conversation 2021; October 14, 2021
Li M,
Colby H. Physicians’ flawed heuristics in the delivery room. Science 2021; 374(6565):
260-261
https://www.science.org/doi/10.1126/science.abl5647
Blumenthal-Barby JS, Krieger H. Cognitive Biases and Heuristics in
Medical Decision Making: A Critical Review Using a Systematic Search Strategy.
Medical Decision Making 2015; 35(4): 539-557
https://journals.sagepub.com/doi/10.1177/0272989X14547740
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