A Collaborative Learning Environment of the Medical Diagnosis on

the Basis of the Clinical Reasoning Theory

Mohamed Abderraouf Ferradji and Abdelmadjid Zidani

LaSTIC laboratory,

Department of Computing, Batna University, Batna, Algeria

Keywords: Clinical Reasoning, Medical Education, Medical Pedagogy, Collaborative Learning, Social Knowledge

Building, Synchronous Interaction, Group Awareness.

Abstract: In collaborative clinical learning field, several recent pertinent studies showed that gathering learners with

their tutor still insufficient to improve students' learning quality and knowledge acquisition. Consequently,

focusing attention on professional skills within a collaboration environment seems to be the most

appropriate way to reach the wished learning objectives, particularly in a complex specialty such as medical

diagnosis learning. In this paper, we firstly introduce the concept of medical diagnosis from cognitive

studies view that have been performed in the field of medical education. Then, we will discuss our shared

web environment designed to support distance diagnosis learning, which aims to promote knowledge co-

construction and collaboration between learners.

1 INTRODUCTION

In the medical field, the competencies of making a

diagnosis are refined through the good structuring of

an important mass of medical knowledge in the

doctor’s memory, and the mastery of relevant

reasoning process (Bordage, 2005; Eva, 2005;

Nendaz et al., 2005). However, the observation of

learning strategies in the medicine faculties of

Algeria, showed that the classical objective-based

pedagogy represent the most used learning method.

Indeed, we led an investigation mainly based on

observations and interviews with a medical staff

within a gynaecology and obstetrics emergency unit

in Algeria.

In fact the typical used approach is based on the

direct transmission of knowledge and considers

learner as an inactive entity with a great capacity of

theoretical knowledge memorization. Even in the

internships occurring at hospitals and before the

patient’s bed, we have noticed that the clinician

teacher has not always time and pedagogic strategy

that allow him to explain the approach of the used

reasoning and the mobilized knowledge to achieve

the stated diagnosis ( Nendaz et al., 2005).

Moreover, the quality of the acquired knowledge

depends on the richness of the met cases during the

internship cycle. To overcome these challenges,

many medicine faculties across the world have

introduced the collaborative learning methods

(Quénu-Joiron, 2002).

The integration of these learning methods in the

medicine faculties of Algeria requires radical

changes in terms of educative systems and even on

the faculties’ infrastructures. Moreover, this

pedagogic activity is based on learning in small

groups of learners and its organization in classroom

mode is not always easy (Ortega, 2005). Especially

in Algeria with the area of (2 381 741 Km

) and that

doesn’t allow the very limited number of

experimented doctors to contribute to the

pedagogical activities in all medicine faculties. This

fact limits significantly the learners’ opportunities,

in far southern areas of benefiting from their

experiences in a fair way. This observation ensues

directly from our study about the imbalance in terms

of specialized doctors’ availability compared to the

north of the country. The direction into the distant

collaborative learning support tools may bring more

flexibility and fluency in time and in space of

diagnosis learning (Quénu-Joiron, 2002).

Besides that, the fact of gathering learners with a

tutor in a team, either in classroom or distance mode,

is not enough to improve the learning quality and

knowledge acquisition, as supplementary factors

related to the professional skills, on one hand, and to

the collaboration, on the other hand, must be

Ferradji, M. and Zidani, A.

A Collaborative Learning Environment of the Medical Diagnosis on the Basis of the Clinical Reasoning Theory.

In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016), pages 80-87

ISBN: 978-989-758-180-9

considered.

In this paper, we propose a web-based learning

environment supporting the collaboration, the

coordination and the communication between a

group of learners who are geographically distant,

and share a common task of elaborating a medical

diagnosis in synchronous mode. Finally, to increase

our environment efficiency, we have considered the

most pertinent cognitive studies achieved in the field

of medical education, as well as the researches

dedicated to the clinical reasoning process modeling.

2 CLINICAL REASONING

In the medical education field, the major of the

definitions related to the clinical reasoning process

that have been proposed agree on a common point. It

is about the perception of such process as a set of

mental activities that allow the clinician to make the

right decision while dealing with a specific clinical

situation (Barrows, 1980; Higgs, 2008).

Furthermore, studies that have been achieved in this

area have revealed that doctors use diverse methods

of reasoning. These methods are mainly based on

two classes of reasoning: analytical and non-

analytical processes as mentioned by Pelaccia

(2011), as well as the study achieved by Croskerry

(2005).

According to the analytical reasoning approach,

a physician can explicitly draw his own reasoning

process (Duquerroux, 2009; Evans, 2008). Through

such drawing he may then establish a relationship

between the patient’s signs or symptoms and the

identification of the categories of diseases associated

with them ((Eva, 2005). Such kind of reasoning is

generally used by doctors who have very little

experience and which must always follow a set of

steps that allow them to confirm a diagnosis. It is

even used in complex clinical or soon encountered

cases, where the doctor can not directly establish a

diagnosis and will have to resort to reasoning efforts

to reach the final diagnosis (Edwards et al., 2004).

However, according to the non-analytic

approach, the physician cannot explain the used

reasoning process (Duquerroux, 2009; Evans, 2008).

So the non-analytical reasoning can be used in

typical cases where the physician can make a

diagnosis without deploying reasoning efforts,

especially when the considered case is characterized

by its similarity with a specific disease prototype.

Such reasoning way is mainly used by experienced

doctors who can systematically make diagnoses

while mobilizing a minimum of their conscience

through a direct projection of the current processed

case on a similar well known one (Croskerry, 2005;

Eva, 2005;Nendaz et al., 2005; Pottier & Planchon,

2011).

In this paper we mainly focus our work on the

complex process of hypothetical-deductive

reasoning (Figure 1). Such process may be

considered as a part of the analytical process and,

remains as the most used method in the clinical

training field (Coderre et al., 2003). According to the

analytical approach, the physician proposes a set of

early hypotheses, based on the preliminary

interaction with the patient, which seem to be the

most pertinent.

Figure 1: Hypothetical-deductive process of clinical

reasoning (Nendaz et al., 2005).

The number of those hypotheses is, generally,

comprised between 4 and 5 suggestions. After

additional clinical information gathering step, based

on the hypotheses proposed before, an interpretation

of these data must be carried out to check the

compatibility between the additional data and the

proposed hypotheses. Such verification allows for an

evaluation of the hypotheses that determines if it

should be accepted, rejected or reevaluated. It should

be noted here that it is quite possible that new

hypotheses may be generated and evaluated during

the next iterations. Thus the iterative process can

proceed through a cycle that will continue while the

final diagnosis still not reached (Nendaz et al., 2005;

Vanpee, Gillet, & Godin, 2002).

We must understand well that the efficiency of

the reasoning process is highly dependent on the

A Collaborative Learning Environment of the Medical Diagnosis on the Basis of the Clinical Reasoning Theory

way under which knowledge is structured in the

clinician long term memory (Steward et al., 1991).

Indeed, the most significant cognitive studies in

this area showed that the clinical cases encountered

during clinical experience provide physicians and

students with the ability to better structure their

knowledge and build pertinent inference networks,

which can be unconsciously activated every time

they are faced to a new clinical situation (Bordage,

2005; Harasym, Tsai, & Hemmati, 2008).

Consequently, the quality of memory knowledge

structuring is effectively a key factor that directly

impacts on clinicians ’skills and abilities,

particularly for students.

Therefore, one notices that a learning method of

the medical diagnosis becomes relevant since it

allows learners to easily overcome the challenges

related to the reasoning process, to effectively

structure their knowledge (Chamberland, 2007).

Learners collaborate also with others and it seems

that it still the most practical used way in their real

contextual work as reported by numerous pertinent

studies related to such issue (Aarnio et al., 2010;

Lerner, Magrane, & Friedman, 2009; Zwarenstein,

Goldman, & Reeves, 2009).

Concerning our environment design, we have

adopted the analytical approach because it seems to

be the most suitable for learners’ training while it

favors interaction and negotiation between them and

thus promotes collaboration to enable social

knowledge construction. Indeed, such approach

provides for students the opportunity to deal

naturally with the reasoning process used by

experienced clinicians, while they work together on

a common clinical case and manage both

construction and structuring of their knowledge. The

method may even generate situations of socio-

cognitive conflicts that extend the learning process

towards social activities as they arise in real clinical

settings.

3 ENVIRONMENT

PRESENTATION

Our research approach reflects, the socio-

constructivist theory point of view, which considers

learning as an activity that overcomes the individual

scale, and projects it in a larger framework. It is

about the social process of knowledge construction,

which favors interaction and communication

between learners.

As we mentioned it previously, our environment

is based on the Hypothetical-deductive process,

which is considered as the most effective method in

the field of the medical education. Indeed, though

this process allows clinical reasoning to be modelled

according to a way that is individually used by

experienced doctors, we tried in our work to adapt it

for distant collaborative learning situations that are

based on synchronous interaction. Our approach is

intended to favour the social aspect within the

learning environments through a set of appropriate

tools supporting interaction and negotiation

activities as well as learners’ points of view

confrontation. Our main objective here is to enhance

the collaborative reasoning and knowledge co-

construction, as well as providing to learners the

suitable opportunities to master the most utilized

reasoning process in the clinical background.

Access to a remote collaborative learning

environment, should be considered across

heterogeneous platforms machines. This makes

interoperability a fundamental factor in the

assessment of the system quality and effectiveness.

The web 2.0 concept seems to be the most adequate

technical solution to effectively overcome machines

incompatibility problems. Taking into account also

the excessive keen interest of the current generation

of learners for any web technology, it would be

injudicious to provide learning assistance models

while ignoring such fact. Thus, the technological

solution we propose here is designed through a web-

based approach. Through such way, we expect to

have learners’ full commitment while taking part to

distance learning sessions and lead them to

effectively interact with their peers. Finally, a key

factor that also affects learners interactions, concerns

the environment external presentation.

Therefore, our preoccupation was obviously to

provide a learning environment with an ergonomic

easy to use web interface for learners in order to

significantly reduce their cognitive loads, and then

enable them to focus more on the clinical reasoning

and the common case solving.

Through a typical collaborative learning

scenario, a clinical problem must be presented to

learners as a spontaneous complaint announced by a

patient. This complaint is elaborated by the tutor

who will supervise the whole problem solving

process. We must notice here that the case

elaboration should be done by taking into account

the tutor’s learning objectives that he plans to

achieve with learners. Another key factor concerns

the problem’s complexity degree which must be

adapted to learners’ skills and work session duration.

Consequently, the suggested environment

consists of three workspaces (Figure 2). The first

ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health

workspace is intended for the preparation of the

clinical case by the tutor, the second one is designed

to support collaborative learning sessions and the

third one allows learners to review any previous

learning session, by replaying different scenarios of

the associated clinical solved problems.

Figure 2: Environment workspaces.

3.1 Clinical Case Elaboration

The first environment’s workspace is private and is

accessible only to the tutor where he (she) elaborates

the clinical case content that will be collaboratively

solved by learners under a synchronous mode. We

have to remind that learners work on the same case

at the same time through a WYSIWIS way (What

You See Is What I See), which is a synchronous way

of sharing the same view, so the actions performed

by one of them are immediately made visible to the

others. The case elaboration includes the clinical

case presentation, collecting patient's personal

information, anamnesis or clinical history, patient’s

clinical and paraclinical exams related to the case as

well as the pertinent related documentation required

when solving the problem. We should note that

efficiency of the collaborative session depends

greatly on the clinical case elaboration. In addition,

the tutor has the possibility of adapting or enriching

more the studied case as the learning session

evolves. The prepared content will be progressively

displayed depending on the session’s phase and

learners’ needs. Once a case elaboration is fully

completed, the collaborative learning session of the

clinical problem solving may be achieved within the

second shared workspace, which represents the

environment’s core and the most significant step of

the clinical reasoning training process. Learners can

access it through a simple authentication protocol

based on the user’s login and password. To allow

group awareness, each learner’s action in the shared

workspace is colored by his specific associated

color. Such way allows his peers to intuitively

identify him (her) within the shared workspace via

his contributions. The patient’s role is assigned to

the tutor, who is considered as the session’s

facilitator and the main source of the patient’s

information.

3.2 Medical Diagnosis Collaborative

Learning Session

The collaborative learning session comprises three

work phases according to the clinical reasoning

Hypothetical-deductive process (Figure 1, Sec. 2). In

the following paragraphs of this section, we will

discuss each phase and bring more details through

our environment interface views.

3.2.1 Clinical Problem Representation

The first phase of the session (Figure 3) takes into

account the representation of the clinical problem,

which constitutes a key step of the whole process.

We should note that for the medical community, the

direct exploration of the patient’s complaint in the

clinical problem solving has a negative effect on the

reasoning approach as well as on the quality of the

suggested diagnosis. It is rather highly required first

to look for the most appropriate medical meaning

that should be associated to such complaint and then

generate what we call semantic axes (man/woman,

unilateral/bilateral, acute/chronic) whose medical

data are compared and contracted (Bordage, 1999,

2005; Steward et al., 1991).

The resulting semantic transformation called also

mental representation of the problem enables

optimising the research of pertinent hypotheses in

the clinician’s memory. Compared to experienced

doctors, novices ones generally encounter

difficulties when building their mental

representation of the met case, this may lead to a

random generation of hypotheses and to an uncertain

research on clinical data. Consequently, it seems

very important to fix the problem’s representation as

one of the key learning objectives in the clinical

background (Bowen, 2006).

In this phase of the session, learners elaborate

their own representations of the clinical problem.

A Collaborative Learning Environment of the Medical Diagnosis on the Basis of the Clinical Reasoning Theory

Figure 3: Interface view of the first step of the learning session.

Each learner can visualize the others’

representations and leave them comments which will

be displayed as notifications. According to these

remarks, learners can adjust or correct their

representations, and the most agreed one will be

selected as the relevant collaborative representation.

Regarding to the patient’s medical history,

learners can explore a fictive medical patient record

to collect any required information for their medical

representation. Our shared workspace embeds

diverse assistance tools that learners may use. To

support communications within the shared

workspace, we provided a conversational tool with

the patient, and another for collaborative discussions

among the group’s members. Learners can also

access at any time to the documentation

recommended and seek assistance from tutors to

remove ambiguities or seek clarifications. Finally, to

support learners’ notes and remarks editing, we have

integrated within the shared workspace a shared text

editor.

As we have previously indicated, the problem’s

representation quality has a great impact on the

pertinence of the clinical reasoning approach and the

correctness of the proposed diagnosis. In our

environment, we have provided to learners the

opportunity to compare and confront their different

representations in order to favour brainstorming

activity during clinical reasoning and enable them to

significantly improve their skills.

3.2.2 Hypotheses Generation, Filtering and

Structuring

The first generation of hypotheses, allows learners to

start collecting as much information as possible on

the considered case. The following step will be

achieved through interactions among learners and

lead them to structure the clinical problem and

reduce the number of its associated suggestions.

Such selection of hypotheses allows learners to

optimize their work memory efficiency while

useless details don’t have to be memorized and

overload it, which may have great impact on

learners’ focusing ability (Nendaz et al., 2005). We

note here that the work memory enables keeping

short term information in mind, some seconds or

minutes, to mentally realize the associated

operations.

At the second step of the session, learners can

suggest early hypotheses (Figure 4). Each one is

displayed with the specific learner’s color that has

suggested it. It follows then interactions between

learners through explicit comments related to these

hypotheses which may be added to naturally express

learners’ diverse points of view.

We notice that with the collaborative learning

approach, it is strongly possible that among the

generated hypothesises we find many of them that

are not suitable for the current case. Consequently,

learners should filter them at the beginning before

the following steps. Such situation that seems to be

ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health

Figure 4: Hypotheses generating, filtering and structuring.

as a constraint is pedagogically interesting. Indeed

learners reported that they found it very useful while

it generates strong wish for interactions and

exchanges between them and enables them by the

way to better structure their knowledge.

Furthermore, the next step will be easier while

learners have only to focus their attention on the

most probable hypothesis, which can improve the

quality of the problem solving approach.

3.2.3 Additional Information Research and

Hypotheses Evaluation

The third step of the session concerns additional

information requesting in order to confirm or reject

the suggested hypotheses (Figure 5). Such

information may be collected through the patient’s

questioning, signs confirmation and complementary

examinations. The requested information will

automatically be sent as soon as it is available in the

data base. For more pedagogic clarifications, the

tutor can ask any requesting learner to motivate his

request in order to increase the debate around the

question if he deems it relevant. For example, in the

case of an imaging diagnostic examination request

(scanner, ECG, radio ...) the image will be displayed

in a shared window where learners can insert

graphic marks or textual comments.

During the hypotheses evaluation step and in

case of conflictual situations, learners may use a

voting embedded tool to complete filtering them.

Any learner has the opportunity to express and

motivate his point of view. Through the negotiation

process that is fully coordinated within the

environment, learners’ ideas may dynamically

evolve. This is especially intended to enhance

learners’ skills while providing them with the

opportunity to interact within a shared workspace.

Furthermore through awareness features, each

hypothesis is displayed with one of these specific

colours that explicitly shows its state according to

the learners’ voting process. Thus, the green color

means that most of the learners have agreed the

hypothesis; the orange one that few learners among

the group have rejected it; and the red one is

displayed when it has been rejected by most of them.

The collaborative reasoning process progress

may be simultaneously edited by learners through a

graphical structure. Such structure is visualized by

learners and dynamically evolves during a work

session. It allows each learner to evaluate his

contributions with regard to the others and adjust

them according to the diagnosis evolution. The main

goal of the reasoning process graphical structure is

to provide as much as possible assistance to learners

and enable them to better organize knowledge in

their long-term memory. Therefore, they will be

predisposed to reuse the acquired knowledge during

work session. We notice that at the current state of

our research we used a simple graphical structure to

facilitate learners’ task. However, we plan in the

short term to recourse to concept mapping

representation which is more suitable while it

illustrates the relationships between symptoms and

disease related to the treated case.

A Collaborative Learning Environment of the Medical Diagnosis on the Basis of the Clinical Reasoning Theory

Figure 5: Interface view of the third step of the learning session.

Finally, to model the iterative approach of the

hypothetical-deductive method, our environment

allows learners to review previous steps of the

clinical reasoning process to make changes either on

the problem collaborative representation, or on the

proposed hypotheses, as new hypotheses can be

generated and guide the diagnosis to alternative

directions. Furthermore, in order to keep all the

participants attention and let them strongly focused,

the environment diffuses immediately notifications

when new actions are performed by learners. Thus

when one clicks a specific notification, a list of

shortcuts is displayed to allow direct access to the

associated activities areas. We think that through

such approach, each learner will have a complete

idea about others current activities. For more

transparency and coordination within the shared

workspace, we also explore reminding notifications

that appear when for example a learner proposes a

hypothesis or an examination already mentioned

before to recall him a redundancy case.

3.3 Individual and Collaborative

Review of Previous Learning

Sessions

In the classical methods of learning, it is widely

recognized that the majority of learners cannot

follow and understand the entire session’s content,

where each of them reaches some level of

comprehension and can only improve it during the

revision. Thereof, the third section of our

environment provides learners with the opportunity

of replaying any previous session scenario to fill the

gaps. The review session may happen through both

individual and collaborative mode and learners can

add questions or observations to the session’s

content.

4 CONCLUSIONS AND FUTURE

WORK

In this paper, we have discussed our collaborative

web-based environment designed to support medical

diagnosis learning and synchronous interaction

between learners. Its design aims to favour the

professional skills acquiring for medical diagnosis

learners, and to enhance collaboration between

them.

In order to model a better learning opportunities,

we have tried to explore the most pertinent cognitive

studies results achieved in the medical education

field, that have tackled the hard issue of the clinical

reasoning and highlighted the most relevant

impacting factors. Finally for the next step of our

research work, we plan to elaborate an experimental

protocol through our collaboration with our partners

at the faculty of medicine. This step will enable us to

draw the necessary lessons to improve our design

proposition.

ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health

ACKNOWLEDGEMENTS

This paper is the result of our collaboration with the

medical staff within a gynecology and obstetrics

emergency unit at Batna town, which we especially

like to thank all of them. We also extend our thanks

to the students of the service for their patience and

cooperation during this study, and for the fruitful

exchange of ideas.

REFERENCES

Aarnio, M., Nieminen, J., Pyörälä, E., & Lindblom-

Ylänne, S., 2010. Motivating medical students to learn

teamwork skills. Medical Teacher, 32(4), e199-e204.

Barrows, H. S., 1980. Problem-based learning: An

approach to medical education. Springer Publishing

Company.

Bordage, G., 1999. Why did I miss the diagnosis? Some

cognitive explanations and educational

implications. Academic Medicine, 74(10), S138-43.

Bordage, G., 2005. La prise de décision en médecine:

quelques mécanismes mentaux et des conseils

pratiques. La revue de médecine interne, 26, S14-S17.

Bowen, J. L., 2006. Educational strategies to promote

clinical diagnostic reasoning. New England Journal of

Medicine, 355(21), 2217-2225.

Chamberland, M., 2007. Les séances d’apprentissage du

raisonnement clinique (ARC): description de la

méthode pédagogique. Université de SHERBROOKE.

Coderre, S., Mandin, H., Harasym, P. H., & Fick, G. H.,

2003. Diagnostic reasoning strategies and diagnostic

success. Medical education, 37(8), 695-703.

Croskerry, P., 2005. The theory and practice of clinical

decision-making.Canadian Journal of

Anesthesia/Journal canadien d'anesthésie, 52, R1-R8.

Duquerroux, V., 2009. Etude du raisonnement du clinicien

expérimenté et de l'étudiant: apports de la psychologie

cognitive (Doctoral dissertation).

Edwards, I., Jones, M., Carr, J., Braunack-Mayer, A., &

Jensen, G. M., 2004. Clinical reasoning strategies in

physical therapy. Physical therapy, 84(4), 312-330.

Eva, K. W., 2005. What every teacher needs to know

about clinical reasoning.Medical education, 39(1), 98-

106.

Evans, J. S. B., 2008. Dual-processing accounts of

reasoning, judgment, and social cognition. Annu. Rev.

Psychol., 59, 255-278.

Harasym, P. H., Tsai, T. C., & Hemmati, P., 2008. Current

trends in developing medical students' critical thinking

abilities. The Kaohsiung journal of medical

sciences, 24(7), 341-355.

Higgs, J., 2008. Clinical reasoning in the health

professions. Elsevier Health Sciences.

Lerner, S., Magrane, D., & Friedman, E., 2009. Teaching

teamwork in medical education. Mount Sinai Journal

of Medicine: A Journal of Translational and

Personalized Medicine, 76(4), 318-329.

Nendaz, M., Charlin, B., Leblanc, V., & Bordage, G.,

2005. Le raisonnement clinique: données issues de la

recherche et implications pour l’enseignement.

Pédagogie médicale, 6(4), 235-254.

Ortega, E. M., Lessard, Y., Burgun, A., & Le Beux, P.,

2005, September. Virtu@ I Consult@ tion: an

interactive and multimedia environment for remote

clinical reasoning learning in cardiology.

In Computers in Cardiology, 2005 (pp. 829-832).

IEEE.

Pelaccia, T., Tardif, J., Triby, E., & Charlin, B., 2011. An

analysis of clinical reasoning through a recent and

comprehensive approach: the dual-process

theory. Medical education online, 16.

Pottier, P., & Planchon, B., 2011. Description of the

mental processes occurring during clinical

reasoning. La Revue de médecine interne/fondée... par

la Société nationale francaise de médecine

interne, 32(6), 383-390.

Quénu-Joiron, C., 2002. Une contribution aux systèmes

supports de Formation Médicale Continue à distance

et d'apprentissage entre pairs: conception et

expérimentation du forum DIACOM (Discussions

Interactives à bAse de Cas pour la fOrmation

Médicale) (Doctoral dissertation, Université de

Picardie Jules Verne).

Steward, D., BORDAGE, G., & LEMIEUX, M., 1991.

Semantic structures and diagnostic thinking of experts

and novices. Academic Medicine, 66(9), S70-S72.

Vanpee, D., Gillet, J. B., & Godin, V., 2002. Séance

d'apprentissage au raisonnement clinique: Une

méthode potentiellement intéressante pour

l'enseignement de la médecine aiguë. Louvain

médical, 121(10), 425-429.

Zwarenstein, M., Goldman, J., & Reeves, S., 2009.

Interprofessional collaboration: effects of practice-

based interventions on professional practice and

healthcare outcomes. Cochrane Database Syst

Rev, 3(3).

A Collaborative Learning Environment of the Medical Diagnosis on the Basis of the Clinical Reasoning Theory