Download Statistical and Psychological Factors Affecting Clinical Decisions

Statistical and Psychological Factors Affecting Clinical Decisions
By:
Afshan Mirza
Mentors:
Mr. Jeffrey Madura
Dr. Keith Carroll
April 2, 2007
-------------------------------------------------------------------------------------------------------------------5/20/2007
Hi Mr. Madura,
So I went to the Scholars Banquet last night and got a very pleasant surprise and thought I'd
share it with you... Everyone who graduated as a Scholar with Distinction was presented with
plaques. When it was my turn, Dr. Mikula announced that I had the highest IRP grade ever!
EVER!! Wow...it was so amazing! My whole family is so proud of me and I feel a great sense
of achievement too. If it wasn't for your help, I would've quit the program a long time ago and
would never have discovered this hidden potential. So thank you very much once again.
Have a great summer!
Afshan Mirza
Table of Contents
Introduction…………………………………………………………………………...
Prevalence of Misdiagnoses…………………………………………………………..
Misdiagnosis Due to Inadequate Understanding of Statistics
The Problem………………………………………………………………………..
The Solution………………………………………………………………………..
Misdiagnosis Due to Psychological Barriers
The Problem………………………………………………………………………..
The Solution………………………………………………………………………..
Conclusion……………………………………………………………………………..
Bibliography………………………………………….………………………………..
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Medical Misdiagnosis
Introduction
A person faces choices from the moment of waking to the moment of falling asleep.
Sometimes these choices are insignificant, like what color socks to wear; but at other times, they
can mean the difference between life and death, like whether to drink and drive or to take a cab
instead. All individuals tackle choices in their personal and professional lives. Personal choices
often have an impact only on the person who makes them, whereas professional choices usually
affect others.
Among the most important decisions made by professionals are those made by doctors.
Physicians make choices in the testing, diagnosis, and treatment of patients many times a day.
These decisions, when made well, can save a patient’s life or significantly reduce suffering. On
the other hand, when made poorly they can cause extreme stress, pain, new illnesses, and even
death. In order to reduce the possibility of these negative outcomes, doctors need to understand
probability and statistics in medical testing and be able to interpret test results correctly. The
required knowledge in this area can be provided through continuing education and application of
Bayes’ Theorem. The use of computer programs designed to help with diagnosis and
interpretation of test results will also assist doctors.
In addition to knowledge deficiencies, doctors often must overcome psychological
hurdles that stand in the way of good decisions. The psychological pitfalls in decision-making
under uncertainty need to be learned and taken into consideration in order to reduce occurrences
of misdiagnosis and mistreatment in medicine. Currently, eighty percent of medical errors are
the result of predictable mental traps or cognitive errors (Gorman). Being aware of one’s own
mindset and ability to handle risky situations is the key to better medical decisions. Medical
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school curricula should include programs to teach students the risks of cognitive errors. In
addition, doctors should use a formal decision-making approach to ensure their personal
weaknesses do not get in the way of their professional decisions. Education, awareness, and
action are necessary steps towards better medical care and safer treatment for all.
Prevalence of Misdiagnoses
Misdiagnoses are an unavoidable part of medicine. Any process that involves humans is
bound towards imperfection. The problem lies not only in the presence of misdiagnoses, but
more so in the prevalence of them. Each misdiagnosis may result in an increase of a patient’s
pain, an increase in a patient’s medical bills, or even the patient’s death. According to a study
done by the Institute of Medicine, close to 100,000 Americans die annually due to medical
errors. Furthermore, at least 1.5 million people per year are harmed by medical errors, at an
annual cost of around $3.5 billion (Kalb). Autopsy studies show that doctors seriously
misdiagnose fatal illnesses approximately 20 percent of the time. Astonishingly, there has been
no improvement in this rate since the 1930’s, according to an article published in the Journal of
the American Medical Association (Leonhardt). The problem of misdiagnosis affects millions of
people and their families each year. This is an issue that needs to be addressed immediately. But
before something can be improved, it is essential to understand the causes of it.
Misdiagnosis Due to Inadequate Understanding of Statistics
The Problem
Doctors almost always rely on medical testing to diagnose a patient’s condition. From
strep throat to leukemia, doctors use tests to determine the cause of the patient’s suffering. The
cause of misdiagnosis, however, is not testing; it is the incorrect interpretation of the test results
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by inadequately-trained doctors. Every medical test has two main characteristics that must be
understood in order to accurately analyze the test results. The first is the sensitivity of the test
and the second is the specificity. Both of these factors need to be taken into account before
reaching a diagnosis based on test results.
Sensitivity describes how good a test is at correctly identifying people who have the
disease. It is calculated by dividing the number of true positives (the number of people who
tested positive for the disease and did in fact have the disease) by the total number of people who
were sick (Loong).
Consider a population of 100 people, 40 of whom are sick with a certain illness.
Example 1:
Sick
Well
Total
Positive Test
30
30
60
Negative Test
10
30
40
Total
40
60
100
In this example, the sensitivity would be 30/40, or 75%. Although it may seem like a test with
100% sensitivity would be ideal, this is not the case. A type of test with 100% sensitivity could
be a test that is designed to always produce a positive result (Loong). A test like this might be
meaningless due to an excessively high number of false positives, and would not help doctors
make wise decisions. Medical tests should, however, aim to have a reasonably high sensitivity
because it reduces the chances of false negatives.
False negatives are a very serious problem. Patients are always hopeful of walking out of
the doctor’s office or the hospital with the assurance that they are perfectly healthy. A false
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negative gives them this artificial consolation and if they are sick, their disease goes undetected
for a longer period of time. Depending on the disease, the time between the false negative and
actual detection of the disease’s presence could prove to be fatal. Whether it is fatal or not, the
growth of the disease during this time is inevitable, making it more difficult and perhaps more
painful to treat. False negatives have greater chances of being discovered as such by taking the
test again. But most people do not think of the possibility that although the test may have
indicated that they are disease-free, they may still have the disease and the test could be wrong.
Therefore, many people do not ask to be retested in the case of a false negative. For this reason
and many others, medical tests should be designed to have a high sensitivity to decrease the
number of false negatives.
The second characteristic of a medical test is specificity. This describes how good the
test is at correctly identifying people who are well (Loong). It is calculated by dividing the
number of true negatives (people who tested negative for a disease and were truly well) from a
test by the total number of people tested who were well. In the above example, thirty well
people tested negative and the total number of well people was sixty. The specificity of the test
is therefore 30/60, or 50%. Tests with higher specificities are better because they reduce the
chances of producing a false positive (healthy people who test positive for a disease).
False positives, like false negatives, also pose a threat to patient safety. If a patient is
inaccurately diagnosed as having the disease, then there is a strong possibility that a wrong
treatment will be given to him/her. Improper treatment could pose a danger to the patient’s life,
as in the case of a four year old boy from Georgia. This child was diagnosed with leukemia and
chemotherapy was scheduled to start for him in a few days. What the doctors did not realize,
however, was that the little boy had a rare form of the disease, which chemotherapy did not cure.
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Furthermore, each round of chemotherapy had a high risk of killing him since he was so weak
already (Leonhardt). Although dangerous, false positives are often less of a threat than false
negatives. Because no one likes to be diagnosed with a disease, false positives have a greater
chance of being identified by means of retesting and second opinions. On the other hand, the
rate of their discovery is not high enough to undermine the need of reducing their occurrences.
The aim to achieve higher specificity in tests should still be a top priority to decrease the number
of false positives. The real goal for those who develop medical tests should be both high
sensitivity and high specificity.
Sensitivity and specificity are important concepts to understand and properly utilize in
order to make wise medical decisions based on testing. Many doctors, however, do not fully
understand these ideas. Sixty doctors from Harvard Medical School teaching hospitals were
given data about a test and asked to calculate what the chances were that a patient who tested
positive did actually have the disease. They were told that the prevalence of the disease was 1 in
1000, the sensitivity of the test was 100%, and that the specificity of the test was 95%. Here is a
table in the same format as the previous example to illustrate the data (the large numbers are
needed to avoid fractional people):
Example 2:
Sick
Well
Total
Positive Test
100
4,995
5,095
Negative Test
0
94,905
94,905
Total
100
99,900
100,000
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Only 18% of the doctors answered correctly that a patient with a positive test result had
less than a 2% (100/5,095 = .019627 = 1.96%) chance of having the disease. The low prevalence
was the key factor in this scenario. Because the prevalence was so low, there were bound to be
more false positives (4,995) than true positives (100) (Pradhan). Eighty-two percent of the
doctors from a prestigious medical school known for its high standards of education were unable
to analyze the situation correctly. This number would probably be higher if this test was given to
students of other schools.
Another statistical error that occurs in medical decision making is conjunction fallacy.
Conjunction fallacy occurs when a person concludes that the conjunction of two events is more
likely than one of the events alone (Elstein). This cannot be true because in probability theory,
the probability of the intersection of two events cannot be greater than the probability of one of
the events alone. This faulty thinking can lead to a diagnosis that is more threatening than the
true condition of the patient therefore putting the patient at risk of overtreatment.
The Solution
The best and simplest way of reducing the number of misdiagnoses due to an inadequate
understanding of statistics is by educating doctors in these matters. Providing physicians with
the correct information on the significance of different statistical measures, especially sensitivity
and specificity, and their correct applications will give them the resources they need to make
intelligent decisions. Education on Bayes’ Theorem will be one of the keys to lowering
misdiagnosis rates and moving towards safer healthcare.
Bayes’ Theorem is an easy way of calculating a conditional probability based on the
inverse conditional probability, which is known. For example, it is possible to calculate the
probability that the patient has the disease given that the test is positive from knowledge of the
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probability that the test is positive for a patient who has the disease. In addition to this, two more
pieces of information are needed for the calculation: the probability that a person has the disease
and the probability of a false positive test result. The equation is as follows:
P = the event that the test gives a positive result
D = the event that the patient has the disease
W = (well) the event that the patient does not have the disease
P(W) = 1 – P(D)
Then
P(D|P) =
P(P|D)P(D)
.
P(P|D)P(D)+P(P|W)P(W)
The following is a sample application of the Bayes’ Theorem:
Problem: A test is known to detect a disease in a patient correctly 95% of the time (sensitivity =
95%). However, 15% of all disease-free patients who take this test also test positive (specificity
= 85%). 10% of the population has the disease. Patient X has taken this test and has tested
positive. What is the probability that he does in fact have the disease?
Solution: P(P|D) = 0.95 (sensitivity)
P(P|W) = 0.15 (1 – specificity)
P(D) = 0.10 (prevalence)
P(W) = 0.90 (1 – prevalence)
Using Bayes’ Theorem, P(D|P) can now be calculated using the probabilities above.
P(D|P) =
(0.95)(0.10)
.
(0.95)(0.10)+(0.15)(0.90)
P(D|P) = 0.095
0.23
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P(D|P) = 0.4130
The probability that patient X does in fact have the disease, given that his test was positive, is
only 41.30% (Waner).
This table reflects the facts above:
Example 3:
Sick
Well
Total
Positive Test
95
135
230
Negative Test
5
765
770
Total
100
900
1000
Of the 230 patients with positive test results, only 95, or 41.30%, are actually sick. Here is
Bayes’ formula using the terms sensitivity, specificity, and prevalence:
P (D|P) =
(sensitivity)(prevalence)
.
(sensitivity)(prevalence) + (1 – specificity)(1 – prevalence)
Bayes’ Theorem is a very important concept to be familiar with when dealing with any
kind of testing, but especially when it comes to medical testing. If doctors were to assume that
patient X had the disease without realizing that there was a 58.70% chance that he did not have
the disease, then his health could have faced serious risks. Education in school and training in
the workplace are the starting points of a better future for healthcare.
Another tool, aside from education, that will help in reducing the number of medical
misdiagnoses is computer software designed for the purpose of aiding doctors with making wise
medical decisions. These computer programs are commonly referred to as clinical decision
support systems. They are defined as “active knowledge systems which use two or more items
of patient data to generate case-specific advice” (“Clinical Decision Support Systems”). These
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systems can make a doctor’s decision-making process easier and more reliable in stressful or
complicated situations.
There are four main functions of clinical decision support systems. The first is
administrative. The systems support clinical coding and documentation. In addition, they
authorize procedures and referrals. The second function is to manage clinical details and
complexity. As a part of this function, systems keep track of referrals, follow-up, and preventive
care of patients. The third function is cost control. Here, the systems are used to monitor
medication orders and avoid unnecessary or duplicate testing. The fourth function of a clinical
decision support system is decision support itself. This is perhaps the most important function of
a decision support system. It supports the processes of clinical diagnosis and treatment plans.
Furthermore, it promotes the use of “best practices, condition-specific guidelines, and
population-based management” (“Clinical Decision Support Systems”).
The use of clinical decision support systems should be encouraged by hospitals and
demanded by patients. Doctors need these systems to either help them make their decisions or to
reconfirm them. Since doctors are expected to memorize and remember so much information,
the chances are high that they may overlook essential information at the time when it is needed
the most. Statistical analysis formulas, such as Bayes’ Theorem, take time to work out and are
sometimes difficult to remember. Computer programs make it more convenient and easier to
apply these and other sophisticated case-management tools.
A study was done to test the significance of clinical decision support systems. In this
study, the human and computer-aided diagnoses of 304 patients suffering from abdominal pain
were compared. The most senior member of the clinical team achieved a 79.6% accuracy, while
the computing system scored a diagnostic accuracy of 91.8% (“Clinical Decision Support
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Systems”). Not only is this an impressive percentage, but it is also an enormous improvement
from the human performance level. This study successfully proved the value of computer-aided
diagnoses in the medical sector.
Bayes’ Theorem and computer programs designed to assist in medical decision making
are essential in order to reduce the number of misdiagnoses due to incorrect statistical analysis.
Using these two simple methods, doctors can provide their patients with a safer healthcare
environment and a more secure feeling towards their medical concerns.
Misdiagnosis Due to Psychological Barriers
The Problem
Incorrect statistical analysis is not the only factor contributing to the high rates of
misdiagnosis. Psychological barriers also play a large role in its prevalence. Unlike statistical
errors, psychological blunders are more difficult to recognize and avoid. Technical errors are
more commonly discussed while thinking errors are largely ignored (Gorman). A few simple
steps, however, can ensure the path to a wise decision. But before a solution can be discussed,
the problem has to be identified. This is often the most challenging task of the process. Yet, at
the same time, it is often the most important.
Doctors, and people in general, find it difficult to recognize the flaws in their decisions
because they assume that they are using the rational model of decision-making. This, however,
is rarely the case (Plochg). Most people do not use this model and by erroneously assuming that
they do, they are greatly reducing their chances of exploring the formal methods of decision
making. Without any formal methods, doctors are left to their own individualized approaches to
decision making. Decisions and diagnoses made under such circumstances have a high risk of
being distorted by the doctor’s personal emotions, drives, and preferences (Plochg). These
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distortions are usually subconscious phenomena; most doctors do not even realize that they are
occurring. Some of the psychological impediments to good decision making will be discussed
here.
Tunnel vision occurs when people have mental blinders that limit them to only consider a
narrow range of alternatives (Huitt). In a clinical environment, this could mean a doctor limiting
the possibilities of a patient’s diagnosis. For example, if a patient walks into the doctor’s clinic
complaining of a fever and muscle aches during January, the doctor, who has been seeing a lot of
flu patients lately, may unconsciously start limiting his diagnosis options to a cold or the flu. But
in fact, this patient could have a wide range of problems or diseases. The patient’s symptoms
could be an indication of anything from meningitis to a reaction to a recently taken tetanus shot
(Gorman). The doctor’s limited vision causes him to ignore the less obvious alternatives of
diagnosis.
Bounded rationality is the idea that people attempt to be rational, but their rationality is
“constrained by their own values and experiences and by unconscious reflexes, skills and habits”
(Plochg). For example, if a doctor has the habit of using a specific medical test when attempting
to diagnose a blocked artery in a patient, then she may not consider other methods of diagnosis
for new patients. Other methods may be less invasive or more convenient, but because she is in
the habit of using that particular test, she will most likely continue using it for her future patients
as well. This could cause a problem for patients who prefer that their medical treatment be
personalized according to their needs and preferences, as opposed to the doctor’s.
Previous commitments can also interfere with medical decision making and diagnosis.
Previous commitments come into play when people feel personally responsible for a poor
decision made in the past, their ability to evaluate the possible alternatives in a current situation
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becomes distorted (Plochg). This is especially relevant with doctors. Since doctors make
decisions that can mean the difference between life and death for some patients, an incorrect
decision leading to the patient’s death or severe harm can push doctors into harshly blaming
themselves for the consequences of their decisions. To some extent, this is a healthy way of
committing to do better next time, but some doctors become so engrossed with the feeling of
guilt that they are unable to function properly in future decisions. They are so wrapped up in the
past decisions, that they find it difficult to “objectively evaluate other alternatives” of the
situation at hand (Plochg). This is a pitfall that doctors should avoid as much as possible.
Another psychological barrier is anchoring. This is when a person chooses an alternative
at the very beginning of the decision-making process and continues to evaluate the other
alternatives, but with a bias towards the initial selection. The subsequent alternatives are
therefore distorted perceptually according to the first choice (Plochg). Furthermore, since the
doctor has become psychologically committed to the initial hypothesis, she will retain it even in
light of new and relevant data (Elstein). Anchoring is a behavior found in novices as well as
senior clinicians and needs to be guarded against carefully (Sutherland). For example, if a doctor
initially feels inclined to utilize a certain method of diagnosing lung cancer in a patient, then she
will continue to analyze other methods of diagnosis, but through the lens of the first method.
According to her perception, the other alternatives may be too expensive, too time-consuming, or
too simplistic compared to her first inclination. It is not necessary that the first picked alternative
should be the best, but when the doctor is a victim of anchoring, the first alternative will seem
like the best alternative. This, of course, is a substantial concern for patients since they want the
best test or the best treatment, rather than the test or treatment that their doctor first thought of.
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Lack of creativity is also a barrier to effective medical decisions. Creativity is the ability
to generate ideas that are both innovative and functional (Plochg). The lack of it limits the
potential of doctors and places constraints on the possibilities of medical testing, diagnosis, and
treatment options. Doctors need to be creative in order to develop new ways of testing that are
less invasive and more accurate. They need to be creative in finding new methods of treatment
that minimize side effects and maximize healing. Creativity is an essential characteristic for
doctors and is a fundamental factor for advancement in healthcare.
Another cognitive error made in medical decision-making is that the frequency of events
that can be easily recalled tends to be overestimated, while the frequency of events that are
ordinary or difficult to remember is usually underestimated (Elstein and Summerton). Diseases
that receive media attention or are supported by famous celebrities are often thought of as
occurring more frequently than they do. More mundane conditions, such as throat infections,
however, are considered to occur less frequently than they actually do. This psychological
mindset causes an overemphasis of rare conditions in the medical setting since unusual cases
remain in the memory longer (Elstein).
According to Tversky and Kahneman’s Prospect Theory, small probabilities are
overweighted while large probabilities are underweighted (Elstein). This provides an
explanation for why “the difference between 99% and 100% is psychologically much greater
than the difference between, say, 60% and 61%” (Elstein). This distortion of the probability
scale could play a very important role in the patient’s decision for treatment. For instance, if a
patient is recommended for major surgery and is told that there is a 99% to 100% chance of
success, the patient may worry about that uncertain 1% much more than if the chances for
success were 60%-61%.
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Support theory suggests that people assign higher probabilities to events that are
described in greater detail than the same events when described with fewer details (Elstein).
From a clinical perspective, this predicts that “a longer, more detailed case description will be
assigned a higher subjective probability of the index disease than a brief abstract of the same
case, even if they contain the same information about that disease” (Elstein). Factors such as
description and length of the case should not matter as long as the information is the same. The
fact that they do matter, however, provides an insight into the psychological factors that affect
medical decisions.
The term framing refers to the manner in which information is presented. The way
information is framed has a significant impact on the medical decisions of patients in reference
to treatment and course of action (Edwards and Cox). For example, framing a situation in terms
of loss causes patients to opt for medical screenings more often than when the same situation is
framed in positive terms (Edwards). Another example is when the “probability of having a child
with Down’s syndrome was framed negatively – as a 20% risk of an affected child – women
were more likely to have an amniocentesis than if the risk was framed positively – an 80% risk
of no abnormality” (Sedgwick). Furthermore, presenting data in more understandable terms for
the patient and providing greater detail is associated with a greater wariness towards
participating in trials and taking medication (Edwards). Patients are the final decision makers of
what treatment they would like or whether they would even like to be treated or not. Therefore,
it is essential for doctors to understand the effects of framing and to be able to communicate risk
and other important information properly.
A psychological activity leading to misdiagnosis and mistreatment that patients may take
part in is called telescoping. This is when patients have the “tendency to combine separate,
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similar symptoms into a single generic event” (Summerton). Since each of the separate
symptoms may have provided essential clues to the correct diagnosis, telescoping does prove to
be harmful and counter-productive.
The psychological state of patients has an immense impact on how they report symptoms
to their doctors. For example, women who are tying to become pregnant are more likely to
describe symptoms of morning sickness and amenorrhea than women who do not want to
become pregnant (Summerton). The cognitive unwillingness to accept symptoms of a possible
unwanted condition leads to underreporting, which may cause the true source of the patient’s
physical condition to go undetected.
Furthermore, the patient’s psychological state along with his/her sex, age and other such
characteristics can lead doctors to false assumptions and incorrect diagnosis (Gorman). This was
clearly demonstrated in the case of an eight year old girl who went to the doctor complaining of
severe headaches. At the time, the patient’s parents were adjusting to new, high-stress jobs and
top neurologists and pediatricians concluded that her symptoms were probably caused by high
stress levels at home and perhaps a sinus condition. A few months later, it was discovered that
she had a brain tumor. The doctors looked at scans of her brain taken earlier and were shocked
to see that there was a shadow of a tumor and they had completely missed seeing it (Gorman).
The same type of situation may arise if a homeless man complains of disorientation. The doctors
may feel inclined to attribute this symptom to alcoholism although the true cause may be due to
diabetes (Gorman).
A doctor’s emotional feelings towards the patient can also be the cause of cognitive
errors and misdiagnosis. Doctors who love their patients are known to miss diagnoses of lifethreatening cancers in their patients because they do not want it to be true. On the other hand,
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patients who are disliked by their doctors are equally disadvantaged. Doctors tend to hesitate to
go the extra mile for patients they are not particularly fond of (Gorman).
Another psychological factor in the proper diagnosis and treatment of patients is a stigma
of psychological illness. Doctors and patients alike go to extensive measures to find a physical
cause for the patient’s problems rather than accepting that a psychological factor might be at
play. In cases like this, there is a “risk that patients receive extensive investigations that are of
limited value and potentially damaging both physically and psychologically, irrespective of the
additional healthcare costs incurred” (Summerton). The fear of psychological illness and the
stigma attached to it leaves the patient to have unnecessary physical testing and an untreated
psychological condition.
The psychological barriers to wise medical diagnosis need to be taken seriously. There is
no doubt that they do exist and do impact medical decisions significantly. All doctors and
patients should be made aware of the potential barriers they could face when making medical
decisions and they should be encouraged to gain a better understanding of them and to avoid
them as much as possible.
The Solution
The first step doctors can take to reduce medical misdiagnoses due to psychological
barriers is to accept and recognize the fact that these factors are present in their work
environment. It becomes very difficult to work towards a solution without the acceptance and
recognition of the problem. Educational institutions and doctors need to work together to create
awareness of these barriers and to encourage action towards eliminating or reducing them.
Since framing plays an essential role in medical decision making, it is extremely
important that medical students learn how to properly communicate risk to patients. This topic
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has received very little attention over the past years in undergraduate medical curriculum
(Sedgwick). Using quantitative evidence is key in communicating effectively with patients since
qualitative expressions can be interpreted in various ways by each individual. Lessons like these
need to be taught to students early in their medical education so that they have the opportunity to
practice this knowledge throughout their education and in their careers.
Additionally, a formal decision-making model should be used in order to minimize
subjectivity due to the doctor’s personal beliefs, habits, and values. There are several different
decision-making models that a doctor can choose from, but they all share similar steps. The
seven most common and the most helpful decision-making steps for the medical field will be
discussed here. They are: identifying the decision to be made, self-assessment, identifying
options, gathering information and data, evaluating options, selecting an option, and designing a
course of action to implement the decision.
The first step to making a good medical decision is identifying the decision being made
(Roberts). The doctor must clearly understand what he is trying to decide. For example, he
should not attempt to decide which treatment option to use for the patient before he decides what
ailment the patient has. Being able to properly identify an appropriate decision at the beginning
of the decision-making process has numerous benefits. Firstly, it brings out any biases that the
doctor may have about the patient’s condition. In the above example, it would make it clear to
the doctor that he has already set his mind on a certain diagnosis without going through the
proper decision-making process. Secondly, becoming aware of the goals early in the process
provides direction for the work to come. Once the objective is set, the means to achieving it can
be worked out. Step one is the foundation of the decision-making model.
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The next step is self-assessment (Roberts). In order to make a good decision, doctors
need to be fully aware of their abilities and also their potential biases or favoritisms. Doctors
should evaluate their knowledge and understanding of the patient’s symptoms and objectively
decide if they are the correct person to handle the case. In addition, they must also examine their
psychological standing. They should ask themselves if they have any psychological barriers that
could keep them from making the correct decision in the patient’s best interest. Some doctors
may see this step as unimportant or inconsequential, but this aspect of self-assessment is crucial
because it has the potential of identifying psychological barriers and eliminating them through
their awareness. Doctors should be mindful to be fully objective and honest in their analysis
while performing self-assessment in order for it to be successful.
Step three in the decision-making process is identifying the possible options (Roberts).
Before making any decision, it is important to first consider the various alternatives. If a patient
complains of certain symptoms, the doctor should think about all the potential diagnoses that
could be indicated by these symptoms. Computer programs designed to generate a list of
possible diseases based on the symptoms provided by the doctors are a way of making sure that
no possibilities are overlooked. By generating this list, doctors can reduce the chances of
psychological barriers such as implicit favorites, bounded rationality, and tunnel vision affecting
their medical decisions. In addition, it narrows down the list of diseases to only the ones that are
relevant to the specific case. Since this eliminates the insignificant information, the task at hand
becomes more focused and easier to handle.
Once the possible options have been identified, it is time to gather information and data
(Roberts). It is during this step that all the research, medical tests, and examining will be done.
Since the possible options for diagnosis have already been brought to attention, the type of
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information needed is also automatically narrowed down. If any of the possible diagnoses need
medical testing, then data should be gathered on the sensitivity and specificity of the test. Other
data on the disease itself should be gathered, such as its prevalence. Furthermore, medical tests
need to be given to the patients and their results need to be submitted to the doctors as soon as
possible. After the results are submitted, doctors can move on to the fifth step of decision
making, which is evaluating the options (Roberts).
Evaluating the options is an essential part of the decision-making process. Once all the
data has been gathered and the medical reports are back from testing, doctors must use this
information to critically assess the validity of each test result. For example, if the patient is
given a test for diagnosing leukemia and tests positive for it while the doctor’s research finds that
this test has high sensitivity and low specificity, then the doctor should realize that this patient
has a high chance of being a false positive. Knowing this, the doctor can order the same test to
be given again, or better yet, another more reliable test. This step also narrows down the options
for diagnosis. For example, a simple evaluation of a blood test can eliminate possible diagnoses.
Additionally, all options should be reexamined in light of the new data gathered in the previous
step. No piece of information should be disregarded as insignificant until it has been given
proper attention. Evaluating the options is a key factor to making wise medical decisions.
The sixth step for decision making is selecting one of the options (Roberts). This is
perhaps the most difficult and certainly the most important step in the whole decision-making
process. This is the step that all the other steps were designed to work towards. And this is the
step that will direct the last phase of the process. After all the information has been evaluated
and all the test or retest results have been taken into consideration, the best option has to be
picked. The best option should not be chosen in a hurry. Doctors should give the decision a
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sufficient amount of time to analyze it from all different perspectives. In addition, doctors
should discuss the reasons for picking this option with the patient and his/her family. It is
important that the patient should not be left in the dark about any side effects or risks of the
treatment. This will help avoid lawsuits as well as reduce the patient’s anxiety and make him/her
more confident about the doctor’s knowledge and ability. Furthermore, the patient will feel more
secure knowing that the doctor has used a logical and objective approach to reaching his medical
diagnosis as opposed to a less structured method.
The last step of the decision-making process is to “design a course of action to implement
the decision” (Roberts). Even after the decision has been made, the plan of implementation is
still unclear at times. For example, if the treatment option chosen for a patient is chemotherapy
and the facility where this decision was made does not have the proper equipment for it, then
issues such as where to send the patient for treatment need to be worked out. Additionally, in the
case of this example, other details like what the course of chemotherapy should be, how often it
should be administered, and should the patient observe any dietary regulations before a
chemotherapy session also need to be discussed and planned. Once all of these specifics have
been decided, the decision-making process is complete. The only remaining task is to put the
decisions in action.
Aside from using a formal decision-making technique, different educational programs
can also help reduce the problem of misdiagnosis due to psychological errors. Problem-based
learning is a way of improving medical education in this area. This method can be understood as
“an effort to introduce the formulation and testing of clinical hypotheses into the preclinical
curriculum” (Elstein). Experienced physicians already use this approach when they are faced
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with difficult cases. It would therefore be logical to begin training students in this field since
even a mediocre case may seem difficult to them at the beginning of their careers (Elstein).
Evidence-based medicine should also be taught to students aspiring to become
doctors. Evidence-based medicine is the most recent and the most successful effort yet to apply
statistical decision theory in medicine. It teaches students Bayes’s theorem and shows them
how to interpret diagnostic results and compute probabilities while understanding what they
mean (Elstein). Combined with problem based learning, evidence based medicine has the
potential to drastically reduce the number of misdiagnosis and mistreatment cases due to
statistical errors and psychological barriers, and increase the awareness of the essential role
statistics play in medicine.
HIT (health information technology) is one of the major proposed solutions to medical
errors. It is
“the intersection of information science, medicine, and health care. It deals with the
resources, devices, and methods required to optimize the acquisition, storage,
retrieval, and use of information in health and medicine. Health informatics tools
include computers and information technology as well as clinical guidelines,
medical research, science, and engineering. A major focus is the support of
information systems for reasoning, decision-making, and learning in a clinical
setting” (Grabowski).
It has the “potential to make a highly significant contribution to the advancement of medicine
and the improvement of healthcare quality and reduction of medical errors” (Zhang). Many
medical mistakes are caused by human errors that occur mainly because of “inadequate
information processing in cognitive tasks” (Zhang). HIT comes as a solution to this problem. If
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the health information technology is designed specifically around the environment in which it is
meant to be used, then it can simplify things and be a great help to doctors and a blessing for
their patients. Several colleges and universities are now taking the initiative to offer programs
designed around HIT. University of Illinois at Chicago (UIC), for example, is offering a Masters
of Science degree in Health Informatics and Northwestern University is doing the same in
Medical Informatics. Benedictine University is currently working on a proposal to start a Master
of Science degree in Health Informatics as well.
Conclusion
Misdiagnosis and mistreatment are critical problems in the healthcare industry. They
need to be addressed immediately and measures have to be taken to reduce their high prevalence.
Understanding what is causing the misdiagnosis and mistreatment rates to be so high is the
beginning of the solution. The two main factors allowing this crisis to go on are doctors’
inability to analyze statistics accurately and the presence of psychological barriers in the minds
of doctors and patients. There are simple solutions to both these obstacles. Educational
emphasis and the use of Bayes’ Theorem will increase doctors’ ability to accurately analyze
statistics while a formal decision-making model, educational programs, and the use of HIT will
greatly reduce the chances of their psychological biases distorting proper diagnosis and treatment
decisions.
The solutions are present and their implementations are not demanding. Perhaps the only
remaining hurdle standing in the way of safer healthcare is the lack of awareness about the
severity of this problem. It is the responsibility of all patients and doctors to educate themselves
about the risks of misdiagnosis and mistreatment. It is the responsibility of every hospital to
encourage risk-reducing measures and to educate its staff on this topic. Soaring rates of
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misdiagnoses and mistreatment are not problems without solutions. But the solution lies in the
willingness to take action and to demand safer healthcare for all.
25
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