Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05087485 |
| Other study ID # |
AISC-ISF-2021 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
July 10, 2021 |
| Est. completion date |
November 22, 2021 |
Study information
| Verified date |
March 2022 |
| Source |
Herlev Hospital |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
Investigators hypothesize that the introduction of basic science explanations within the
instructional design of case-based training in visual diagnostics will improve students'
learning curves, retention, and retrieval of knowledge/skill following a washout period.
Research question:
In a group of medical students with limited dermatological training, what is the effect of
integrating biomedical causal explanations of visual criteria during a prolonged case-based
skin cancer training program in visual pattern recognition when compared with an identical
instructional design without biomedical explanations? How will the displacement of students'
cognitive resources from practicing pattern recognition towards understanding the pattern,
affect their learning behavior, learning curve (accuracy and time per diagnosis), and
retrieval of pattern recognition skills following a washout period?
The above-mentioned research questions will be tested through a randomized trial with an
allocation ratio of 1:1. All participants will be trained in skin cancer diagnostics through
a mobile application that offers simulation training and learning through written modules
about the various differential diagnoses. Approximately half of the participants will be
subject to a written content that displays the dermoscopic visual criteria without an
explanation while the remaining half will be subject to the dermoscopic criteria + an
explanation of the underlying cause. The training program consists of 500 training cases, a
14 day wash-out period, and a final training session of 100 cases.
Description:
Background:
Ensuring that medical students and novice physicians become good diagnosticians remains at
the core of medical education. For more than six decades academics and clinicians have sought
an understanding of the most successful diagnostic reasoning strategies and how to educate
future physicians in these strategies. Research has revealed that most diagnosticians apply a
mix of pattern recognition, deductive backward reasoning, i.e. searching for symptoms or
signs that justify a tentative diagnosis, and inductive forwards reasoning strategies, i.e.
analyzing the most probable diagnosis based on signs and symptoms. There is some evidence in
support of novice clinicians being more prone towards deliberate deductive and inductive
diagnostic reasoning strategies while experienced clinicians generally rely more on pattern
recognition. Unlike novices, experts generally identify the most likely diagnosis based on
pattern recognition, followed by deliberate and often unconscious deductive reasoning, aimed
at justifying or ruling out the identified tentative diagnosis/-es. The dual-process theory
offers an explanation for this two-step reasoning strategy, dividing human cognition into two
systems; the intuitive system 1 (pattern recognition) and the deliberate and analytic
deductive/inductive system 2. Pattern recognition (system 1) is immediate, often very
accurate, and requires minuscule resources although it has been criticized widely for being
prone to unconscious heuristic biases. The deliberate system 2 processes are slower and
require significantly greater cognitive resources than system 1 processes. System 2 processes
are generally considered less prone to unconscious biases, although several findings suggest
otherwise. Most people prefer to apply the efficient and non-strenuous system 1 whenever
possible, and only active system 2 processes when absolutely necessary. Unfortunately,
pattern recognition (system 1) in a diagnostic setting relies on domain-specific experience,
which is generally unavailable for novice physicians that therefore rely on the significantly
more demanding system 2 operations during diagnostics. Several authors argue that educators
should consider teaching or promoting pattern recognition if possible as it enables accurate,
efficient, and less demanding diagnostics. Realistic and extensive pattern recognition
training is possible within medical specialties that rely mainly on visual processing such as
pathology, radiology, and dermatology. Large case libraries with annotated x-rays, pathology
slides, or dermoscopic images can be made readily available for practice, enabling students
to attain strong mental representations of the relevant differential diagnoses before
initiating their clinical careers. There is extensive literature on how to facilitate the
development of strong mental representations through pattern recognition training. Although
identification of bone fracture types, cellular abnormalities, and skin conditions is
important, additional knowledge retrieval and processing is necessary in order to provide
patients with the correct treatment regime. Artificial intelligence and trained pigeons (yes,
pigeons) handle image diagnostics impressively well, with several authors reporting
accuracies on par with expert clinicians. However, unlike expert clinicians, machines and
pigeons are currently unable to retrieve one or more complex precompiled diagnosis-specific
scripts or schemas from long-term memory based on subtle diagnostic cues, followed by a
deliberate analysis of the most likely diagnosis and appropriate treatment action. Illness
script theory attempts to explain this incredible memory retrieval and processing operation
through a simple framework. Illness scripts are defined as mental representations or schemas
of an illness or disease that contains enabling conditions, i.e. demographics and medical
history, consequences, i.e. the symptoms and presentations of the illness/disease, and
faults, i.e. the biomedical explanation for consequences and enabling conditions. When an
experienced physician recognizes a certain pattern of enabling conditions, consequences, and
faults it activates and retrieves one or more illness scripts from long term memory,
unlocking all of the knowledge stored within that script, including the physiology of the
disease and interrelated scripts (differential diagnoses). Little is known concerning the
formation and consolidation of illness scripts during medical and clinical training but there
is increasing evidence in favor of combining the underlying physiology and anatomy (faults)
with demographics (enabling conditions) and symptoms (consequences) within the instructional
material of educational interventions in medicine. An underlying conceptual understanding of
the medical condition's causal mechanisms seems to help trainees consolidate and inter-link
their illness scripts, resulting in faster and more accurate script activation and
utilization. When causal mechanisms or "the biomedical science" is included in standard
instructional designs for internal medicine, containing clinical manifestation of the
disease, students perform significantly better, especially on delayed tests and transfer
tests. Similar positive effects on diagnostic performance have been observed when "extended"
basic science descriptions that explain the underlying sociological and behavioral causal
mechanisms are integrated within the instructional design for complex medical conditions that
include a combination of social, somatic, and psychological problems. Student's immediate and
retained diagnostic performance and transfer of knowledge improves if biomechanical visual
analogies of the causal mechanisms are introduced within the description of the causal
mechanisms. The positive effect of integrating biomedical science is best harnessed when it
is intermixed with classic curricula describing clinical manifestations of diseases. The
positive effects from classic textbook diagnostics on written cases also translate towards
visual diagnostics, e.g. dental radiology, and the underlying conceptual knowledge of
procedural skills, e.g. lumbar puncture. Integrating the causal explanations for visual
criteria used in visual diagnostics increases both the immediate and long-term diagnostic
performance of students. The improvement in diagnostic performance associated with
integrating biomedical science has been found to be resilient against speed-accuracy
tradeoffs, which indicates that a strong representation of "faults" within an illness script
increases diagnosticians' ability to rapidly and accurately activate the script. Although
basic science explanations seem to translate across the various medical modalities it is
important to note that visual diagnostics varies significantly from remaining diagnostic
modalities. When experienced dermatologists examine a skin lesion they immediately form a
global impression generating one or more tentative diagnoses that are usually very accurate.
Subsequently, they engage in a backward reasoning strategy attempting to find features that
justify or reject their tentative diagnosis. The global impression is a result of intuitive
system 1 operations while the deliberate analysis of differential diagnoses and feature
search is deliberate system 2 operations. Although experts from remaining clinical
specialties such as internists, neurologists, and cardiologists rely heavily on pattern
recognition their assessments are based on input (symptoms, medical history, laboratory
tests, etc.) that are gathered deliberately, requiring a larger degree of system 2 processes
early in the diagnostic reasoning process. Investigators theorize that this difference in the
"point in time" where pattern recognition is used by the various diagnostic specialties
should be reflected in the education of the various specialties. Based on these reflections,
the optimal educational intervention for clinicians that rely mainly on visual processing
(dermatology, pathology, and radiology) ought to be case-based training with direct visual
feedback coupled to a curriculum with a concise and relevant instructional design. The
formerly mentioned studies show that integrating basic science within such an instructional
design improves the diagnostic accuracy and transfer of knowledge to similar disease
categories. However, former studies in visual diagnostics have failed to establish whether
improved performance following the integration of basic sciences improves clinicians' pattern
recognition, deliberate feature search strategy, or both. The educational interventions
within these studies have been short and included less than 3 training cases per diagnosis,
which investigators of this trial consider low in regards to the formation of mental
representations for the visual classification of diseases (pattern recognition). Finally,
former studies have not examined the effect of integrating basic science within the
instructional design of training interventions for visual diagnostics and its effect on
learning behavior (total duration and the number of times accessing instructional material),
learning curves (formation of mental representations), and skill/knowledge retrieval.
Investigatorsors hypothesize that the introduction of basic science explanations within the
instructional design of case-based training in visual diagnostics will improve students'
learning curves, retention, and retrieval of knowledge/skill following a washout period. To
our knowledge, there are no previous studies that elaborate the effect of integrating
biomedical explanations for visual criteria in a prolonged case-based educational
intervention aimed at training students' pattern recognition skills. Acknowledging that
learning may differ when focusing on novice and more advanced learners and that immediate
performances do not always predict long-term learning outcomes, this is an important research
gap to fill.
Research question:
In a group of medical students with limited dermatological training, what is the effect of
integrating biomedical causal explanations of visual criteria during a prolonged case-based
skin cancer training program in visual pattern recognition when compared with an identical
instructional design without biomedical explanations? How will the displacement of students'
cognitive resources from practicing pattern recognition towards understanding the pattern,
affect their learning behavior, learning curve (accuracy and time per diagnosis), and
retrieval of pattern recognition skills following a washout period?
Method:
The study will be conducted as a randomized controlled trial with an allocation ratio of 1:1.
Enrolled participants must be actively enrolled at the Faculty of Health and Medical
Sciences, University of Copenhagen, and have passed exams in basic histopathology and
cellular physiology. Exclusion criteria are former training in dermoscopy or skin cancer
diagnostics in general. A minimum of 60 students will be recruited. All participants are
required to download a mobile application (Dermloop) containing the educational material.
During sign-up participants will be asked to fill out information concerning their
demographics, enter a six-digit trial code and sign a digital consent. Upon registration,
participants will automatically be randomly allocated to the basic science or feature group.
During the study all participants will complete four steps (see figure):
1. Pre-test
2. Digital training session in skin cancer diagnostics concentrated on seven differential
diagnoses (nevi, melanoma, seborrheic keratoses/solar lentigo, basal cell carcinoma,
dermatofibromas, and vascular lesions) that span 7 days. Instructional material differs
between the two study groups. The basic science group will have access to modules that
describe the characteristic visual criteria for each diagnosis and their underlying
histopathological cause. The feature group will read identical descriptions of the
visual criteria without an explanation of the underlying causal mechanisms.
3. Retraining session
4. Retention test
Pre-test:
The pre-test consists of 12 randomly sampled items (generalizability coefficient of 0.7) from
a test item library (n= 25 items) with established validity evidence for skin cancer
diagnostics.
Digital training session:
The session consists of an introduction and presentation of six diagnoses (nevi, melanoma,
seborrheic keratoses/solar lentigo, basal cell carcinoma, dermatofibromas, and vascular
lesions).
Introduction The introduction includes a short written introduction about skin cancer
diagnostics and the six diagnoses that will be included in the educational intervention.
Participants will be asked to briefly read through the various dermoscopic criteria that are
characteristic of the seven diagnoses.
Case-based practice
Participants will be asked to practice on 500 skin lesions within 7 days. The training
consists of quizzes with direct feedback. Participants will be asked to diagnose skin lesions
based on the age and gender of the patient, a clinical image, a dermoscopic image, and the
location of the skin lesion. Participants will receive immediate feedback following their
choice of diagnosis. The feedback consists of their diagnosis, the correct diagnosis, access
to the instructional design of both diagnoses, and an ability to toggle between the images
and location. Each quiz is 10 cases long and the distribution of diagnoses is random across
each quiz, but with an overall distribution across 100 cases of:
Diagnosis distribution:
Melanoma 20% Nevi 20% Seb. K./ Lentigo Solaris 20% Dermatofibroma 10% Basal cell carcinoma
10% Hemangioma 10% Squamous cell carcinoma. 10%
Retraining session:
Following the 14 day wash-out period, all participants will be asked to access the
application and practice on another 100 cases within two days.
Retention test:
Seven days after the retraining session has been concluded participants will be asked to
answer a retention test. The retention test consists of 12 randomly sampled items
(generalizability coefficient of 0.7) from the same test item library (n= 25 items) used for
the pre-test.
Primary outcomes:
Slope and plateau (if reached) of the initial learning curve (accuracy and time per
diagnosis) Slope and plateau (if reached) of a secondary learning curve (accuracy and time
per diagnosis) following a washout period
Secondary outcome:
Time spent reading instructional material Number of times that the instructional material has
been accessed Change in performance from pre-test to retention test.
