![CAN TEACHING CLINICIANS ABOUT DECISION TREES IMPROVE THE INFORMED CONSENT PROCESS?
Lucas Marinacci, BA; Herbert Chase, MD, MA
Columbia University, College of Physicians and Surgeons
Background and Purpose Conclusions
When doctors engage in medical decision making with
patients and their families, such as during the informed
consent process, they often omit important risks,
benefits, and alternatives to the treatment being
discussed [1,2]. Omission of these options and outcomes
can make it difficult for patients or their surrogates to
make a truly informed decision.
Decision trees are analytical constructs which allow for a
systematic approach to complicated decision-making
scenarios [3]. In its simplest form, a tree consists of a
choice to be made (decision node) connected to the
possible outcomes given those choices (chance node).
These connections can be iterated arbitrarily until they
culminate in a final outcome at a terminal node (Figure1).
By assigning chance and terminal nodes numerical
values, trees can be “folded back” to generate an
optimal solution. They have been extensively utilized in
this way as an algorithm for clinical decision support;
however, to our knowledge no attempt has been made to
use them in the context of the shared decision making
between clinicians and patients.
Even without performing any calculations, the mental
exercise of constructing a decision tree can serve as a
prompt for the clinician to consider the full range of
options and outcomes for an individual patient and their
unique concerns. They provide a simple, generalizable,
reproducible, and customizable tool to assist the clinician
in presenting alternatives, risks, and benefits to a
proposed treatment plan. The purpose of this pilot study
was to determine if students exposed to a novel
educational primer about decision trees listed more
options and outcomes in a simulated informed consent
exercise compared to students who did not receive the
educational intervention.
References
10 medical students participated in this IRB approved
study. Using existing literature on methods of teaching
decision analysis to medical students as well as literature
describing the use of decision trees in other medical
contexts, a 7 page instructional primer on decision trees
was developed consisting of reading as well as written
exercises. Two separate hypothetical clinical vignettes
involving patients facing an ambiguous medical decision
were written with the input of senior faculty; one
involving the family of a patient with dementia and an
abdominal aortic aneurysm considering surgical repair,
the other involving starting an elderly patient on an
antihypertensive drug.
The participants were directed to a web site which
instructed them to read the vignettes and write out all of
the patients’ options including all the information they
would need to make an informed decision. The five
participants randomized to the intervention arm were
given the decision tree primer to complete prior to
reading and responding to the vignettes.
The number of options and outcomes each student listed
in each vignette were counted, and the means of the
intervention and control group were compared using a
one-tailed student t-test, with a significance cut off of
p<0.05.
[1] Schenker, Y. Interventions to improve patient
comprehension in informed consent for medical
and surgical procedures: a systematic review.
Med. Decis. Making, 2010
[2] Braddock, C. Informed Decision Making in
Outpatient Practice: Time to Get Back to Basics.
JAMA, 1999
[3] Zarin, D. Decision Analysis as a Basis for
Medical Decision Making: the Tree of
Hippocrates.
The Journal of Medicine and Philosophy, 1984
1. Teaching medical students about the basics
of decision trees may be a simple,
inexpensive, effective educational tool to
reduce omission of pertinent options and
outcomes during the shared decision making
process.
2. Future considerations including repeating
the study with a larger sample size, utilizing
standardized patient interactions, and to
track the longevity of the observed effects
over time.
3. Major limitations include sample size, coder
bias, lack of follow up, giving all options and
outcomes equal weight regardless of clinical
relevance or accuracy, and using a written
exercise to simulate what is normally an
verbal, conversational exchange.Table 1 demonstrates the mean number of
options and outcomes listed per group per
vignette.
The average number of outcomes listed by the
intervention group was significantly higher than
the means number of outcomes listed by the
control group for both cases, however there was
no difference between the options listed by each
group.
Table 1 Control
(N=5)
Intervention
(N=5)
P
Value
HTN Case
Options 3.2
(1,6)
4.6
(2,7)
0.14
Outcomes 5.8
(4,7)
10.8*
(5,13)
0.01
Alz/AAA
Case
Options 3.4
(2,5)
3.4
(2,5)
0.5
Outcomes 3.8
(2,7)
11.6*
(6,27)
0.04
Mean (Range) *p<0.05
Methods Results](https://image.slidesharecdn.com/decisiontrees-180820194309/85/Can-Decision-Trees-Improve-the-Informed-Consent-Process-1-320.jpg)
Can Decision Trees Improve the Informed Consent Process
- 1. CAN TEACHING CLINICIANS ABOUT DECISION TREES IMPROVE THE INFORMED CONSENT PROCESS? Lucas Marinacci, BA; Herbert Chase, MD, MA Columbia University, College of Physicians and Surgeons Background and Purpose Conclusions When doctors engage in medical decision making with patients and their families, such as during the informed consent process, they often omit important risks, benefits, and alternatives to the treatment being discussed [1,2]. Omission of these options and outcomes can make it difficult for patients or their surrogates to make a truly informed decision. Decision trees are analytical constructs which allow for a systematic approach to complicated decision-making scenarios [3]. In its simplest form, a tree consists of a choice to be made (decision node) connected to the possible outcomes given those choices (chance node). These connections can be iterated arbitrarily until they culminate in a final outcome at a terminal node (Figure1). By assigning chance and terminal nodes numerical values, trees can be “folded back” to generate an optimal solution. They have been extensively utilized in this way as an algorithm for clinical decision support; however, to our knowledge no attempt has been made to use them in the context of the shared decision making between clinicians and patients. Even without performing any calculations, the mental exercise of constructing a decision tree can serve as a prompt for the clinician to consider the full range of options and outcomes for an individual patient and their unique concerns. They provide a simple, generalizable, reproducible, and customizable tool to assist the clinician in presenting alternatives, risks, and benefits to a proposed treatment plan. The purpose of this pilot study was to determine if students exposed to a novel educational primer about decision trees listed more options and outcomes in a simulated informed consent exercise compared to students who did not receive the educational intervention. References 10 medical students participated in this IRB approved study. Using existing literature on methods of teaching decision analysis to medical students as well as literature describing the use of decision trees in other medical contexts, a 7 page instructional primer on decision trees was developed consisting of reading as well as written exercises. Two separate hypothetical clinical vignettes involving patients facing an ambiguous medical decision were written with the input of senior faculty; one involving the family of a patient with dementia and an abdominal aortic aneurysm considering surgical repair, the other involving starting an elderly patient on an antihypertensive drug. The participants were directed to a web site which instructed them to read the vignettes and write out all of the patients’ options including all the information they would need to make an informed decision. The five participants randomized to the intervention arm were given the decision tree primer to complete prior to reading and responding to the vignettes. The number of options and outcomes each student listed in each vignette were counted, and the means of the intervention and control group were compared using a one-tailed student t-test, with a significance cut off of p<0.05. [1] Schenker, Y. Interventions to improve patient comprehension in informed consent for medical and surgical procedures: a systematic review. Med. Decis. Making, 2010 [2] Braddock, C. Informed Decision Making in Outpatient Practice: Time to Get Back to Basics. JAMA, 1999 [3] Zarin, D. Decision Analysis as a Basis for Medical Decision Making: the Tree of Hippocrates. The Journal of Medicine and Philosophy, 1984 1. Teaching medical students about the basics of decision trees may be a simple, inexpensive, effective educational tool to reduce omission of pertinent options and outcomes during the shared decision making process. 2. Future considerations including repeating the study with a larger sample size, utilizing standardized patient interactions, and to track the longevity of the observed effects over time. 3. Major limitations include sample size, coder bias, lack of follow up, giving all options and outcomes equal weight regardless of clinical relevance or accuracy, and using a written exercise to simulate what is normally an verbal, conversational exchange.Table 1 demonstrates the mean number of options and outcomes listed per group per vignette. The average number of outcomes listed by the intervention group was significantly higher than the means number of outcomes listed by the control group for both cases, however there was no difference between the options listed by each group. Table 1 Control (N=5) Intervention (N=5) P Value HTN Case Options 3.2 (1,6) 4.6 (2,7) 0.14 Outcomes 5.8 (4,7) 10.8* (5,13) 0.01 Alz/AAA Case Options 3.4 (2,5) 3.4 (2,5) 0.5 Outcomes 3.8 (2,7) 11.6* (6,27) 0.04 Mean (Range) *p<0.05 Methods Results