Integrated Basic Science Within the Instructional Design of Pattern Recognition Training
Condition: Melanoma; Skin Cancer
MeSH terms: Skin Neoplasms
Study type: Interventional

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.
    

Detailed 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.
    

Intervention name(s): education
Intervention type(s): Other
Intervention description(s): digital education in skin cancer diagnostics
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Single (Participant)
Primary purpose: Diagnostic