Clinical decision support
tools as a solution to
preventable diagnostic
errors
Joyce Rommens
464481
Word count: 2191
, Introduction
While being educated to become a doctor, students learn to first collect all relevant medical
information and then to ask the questions that may lead to a diagnosis. However, in practice,
the doctors often skip this first step of collecting medical information, for the sake of
efficiency, and uses early hypothesis formation. Early hypothesis formation means that
during medical consultation, a doctor follows his or her intuition that indicates a particular
disease or disease category. This way of diagnosing is based on experiential knowledge
(Jongh & Ruijven, 2000).
Nivel (2017) estimated that about 10 to 15 percent of diagnoses made by doctors are
wrong. Diagnostic errors are the cause of 11 percent of all healthcare-related harm to
patients. As a result of a diagnostic error, a patient may die or develop a functional
impairment, which underlines the societal relevance of this problem. The relatively common
delay in (proper) treatment can also have major social consequences, such as an extended
duration of sick leave. This happens while the diagnostic errors could potentially be
prevented in 79 percent of cases (Knol, 2017). The causes of and solutions to diagnostic
errors have received little attention in scientific research to date (Zwaan, 2017). According to
Zwaan, this is due to the complexity of the possible causes and the threshold for doctors to
discuss errors. This essay contributes to scientific knowledge by addressing a possible
solution to the problem of diagnostic errors and their possible consequences.
This possible solution is a technological support tool for doctors in making a
diagnosis, called a clinical decision support tool (Graber, 2021). This tool considers the
patient's symptoms and supports the doctor in making the correct diagnosis, by naming
diagnoses that fit this combination of symptoms. Graber claims that the use of the clinical
decision support tool does not depend on the quality of the tool, but on the usefulness that
physicians see in it and their willingness to use it. The following research question will
therefore be used as a common thread in this essay: “How could a clinical decision support
tool be used in healthcare to prevent diagnostic errors?” To answer this question, the factors
that influence doctors to use the tool correctly are outlined within this essay. Whereby the
proper use of the tool can lead to a reduction in diagnostic errors.
Conceptual Framework
Within this conceptual framework, several theoretical concepts are outlined that can be used
as a lens within the analysis of the study to answer the question how the clinical decision
support tool can be used in healthcare to prevent diagnostic errors. Authors Bromley and
Powell (2012) argue that a new protocol often mismatches with the daily practice. They
conceptualise this as decoupling. The first type of decoupling they distinguish is policy-
practice decoupling, which implies that the protocol of using the clinical decision support tool
is not followed by doctors. In other words, they do not use the tool. The second type the
authors distinguish is means-end decoupling, which implies that the use of clinical decision
support tools is performed by doctors as an end it itself instead of as a mean to decrease the
errors in diagnosis (Bromley & Powell, 2012). Therefore, means-end decoupling is suitable
for understanding why the use of a clinical decision support tool does not automatically lead
to decreased diagnostic errors. It must be used as a means not as an end.
One way to prevent decoupling is improving the alignment between the protocols and
the daily practice. Authors van Loon et al. (2014) conceptualise this as diagnostic work. They
tools as a solution to
preventable diagnostic
errors
Joyce Rommens
464481
Word count: 2191
, Introduction
While being educated to become a doctor, students learn to first collect all relevant medical
information and then to ask the questions that may lead to a diagnosis. However, in practice,
the doctors often skip this first step of collecting medical information, for the sake of
efficiency, and uses early hypothesis formation. Early hypothesis formation means that
during medical consultation, a doctor follows his or her intuition that indicates a particular
disease or disease category. This way of diagnosing is based on experiential knowledge
(Jongh & Ruijven, 2000).
Nivel (2017) estimated that about 10 to 15 percent of diagnoses made by doctors are
wrong. Diagnostic errors are the cause of 11 percent of all healthcare-related harm to
patients. As a result of a diagnostic error, a patient may die or develop a functional
impairment, which underlines the societal relevance of this problem. The relatively common
delay in (proper) treatment can also have major social consequences, such as an extended
duration of sick leave. This happens while the diagnostic errors could potentially be
prevented in 79 percent of cases (Knol, 2017). The causes of and solutions to diagnostic
errors have received little attention in scientific research to date (Zwaan, 2017). According to
Zwaan, this is due to the complexity of the possible causes and the threshold for doctors to
discuss errors. This essay contributes to scientific knowledge by addressing a possible
solution to the problem of diagnostic errors and their possible consequences.
This possible solution is a technological support tool for doctors in making a
diagnosis, called a clinical decision support tool (Graber, 2021). This tool considers the
patient's symptoms and supports the doctor in making the correct diagnosis, by naming
diagnoses that fit this combination of symptoms. Graber claims that the use of the clinical
decision support tool does not depend on the quality of the tool, but on the usefulness that
physicians see in it and their willingness to use it. The following research question will
therefore be used as a common thread in this essay: “How could a clinical decision support
tool be used in healthcare to prevent diagnostic errors?” To answer this question, the factors
that influence doctors to use the tool correctly are outlined within this essay. Whereby the
proper use of the tool can lead to a reduction in diagnostic errors.
Conceptual Framework
Within this conceptual framework, several theoretical concepts are outlined that can be used
as a lens within the analysis of the study to answer the question how the clinical decision
support tool can be used in healthcare to prevent diagnostic errors. Authors Bromley and
Powell (2012) argue that a new protocol often mismatches with the daily practice. They
conceptualise this as decoupling. The first type of decoupling they distinguish is policy-
practice decoupling, which implies that the protocol of using the clinical decision support tool
is not followed by doctors. In other words, they do not use the tool. The second type the
authors distinguish is means-end decoupling, which implies that the use of clinical decision
support tools is performed by doctors as an end it itself instead of as a mean to decrease the
errors in diagnosis (Bromley & Powell, 2012). Therefore, means-end decoupling is suitable
for understanding why the use of a clinical decision support tool does not automatically lead
to decreased diagnostic errors. It must be used as a means not as an end.
One way to prevent decoupling is improving the alignment between the protocols and
the daily practice. Authors van Loon et al. (2014) conceptualise this as diagnostic work. They
