Clinical reasoning processes: unravelling complexitythrough graphical representationBernard Charlin,1 Stuart Lubarsky,2,3 Bernard Millette,1,4 Francoise Crevier,5 Marie-Claude Audetat,1,6

Anne Charbonneau,7 Nathalie Caire Fon,1 Lea Hoff8 & Christian Bourdy6

CONTEXT Clinical reasoning is a core skill inmedical practice, but remains notoriously difficultfor students to grasp and teachers to nurture. Todate, an accepted model that adequately capturesthe complexity of clinical reasoning processes doesnot exist. Knowledge-modelling software such asMOT Plus (Modelling using Typified Objects[MOT]) may be exploited to generate modelscapable of unravelling some of this complexity.

OBJECTIVES This study was designed to create acomprehensive generic model of clinical reasoningprocesses that is intended for use by teachers andlearners, and to provide data on the validity of themodel.

METHODS Using a participatory action researchmethod and the established modelling software(MOT Plus), knowledge was extracted and enteredinto the model by a cognitician in a series ofencounters with a group of experienced cliniciansover more than 250 contact hours. The model was

then refined through an iterative validation processinvolving the same group of doctors, after whichother groups of clinicians were asked to solve aclinical problem involving simulated patients.

RESULTS A hierarchical model depicting themultifaceted processes of clinical reasoning wasproduced. Validation rounds suggested generalis-ability across disciplines and situations.

CONCLUSIONS The MOT model of clinicalreasoning processes has potentially importantapplications for use within undergraduate andgraduate medical curricula to inform teaching,learning and assessment. Specifically, it could beused to support curricular development because itcan help to identify opportune moments for learn-ing specific elements of clinical reasoning. It couldalso be used to precisely identify and remediatereasoning errors in students, residents and practis-ing doctors with persistent difficulties in clinicalreasoning.

clinical reasoning

Medical Education 2012: 46: 454–463doi:10.1111/j.1365-2923.2012.04242.x

Discuss ideas arising from this article atwww.mededuc.com ‘discuss’

1Centre for Applied Pedagogy in Health Sciences (Centre dePedagogie Appliquee aux Sciences de la Sante [CPASS]), Universityof Montreal, Montreal, Quebec, Canada2Centre for Medical Education, McGill University, Montreal,Quebec, Canada3Department of Neurology, Montreal General Hospital, Montreal,Quebec, Canada4Department of Family and Emergency Medicine, Hopital Cite de laSante de Laval, Quebec, Canada5Consultant, Educational Engineering, Faculty of Pharmacy,University of Montreal, Montreal, Quebec, Canada6Department of Family and Emergency Medicine, Faculty ofMedicine, University of Montreal, Montreal, Quebec, Canada

7Department of Oral Health, Faculty of Dental Medicine, Universityof Montreal, Montreal, Quebec, Canada8Department of Medicine, CPASS, University of Montreal,Montreal, Quebec, Canada

Correspondence: Professor Bernard Charlin, Medecine Direction,Centre de Pedagogie Appliquee aux Sciences de la Sante (CPASS),Universite de Montreal, CP 6128, succ. Centre-Ville, Montreal,Quebec H3C 3J7, Canada. Tel: 00 1 514 343 7864; Fax: 00 1 514343 7650; E-mail: bernard.charlin@umontreal.ca

454 ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463

INTRODUCTION

How doctors develop clinical reasoning skills hasfascinated researchers in medical education fordecades.1–3 Yet unravelling the complexity of clinicalreasoning continues to present a formidable chal-lenge to teachers and learners in medicine. Part ofthe difficulty stems from the observation thatseasoned doctors often reason through cases usingrapid, tacit cognitive processes, at least when dealingwith common or routine clinical situations.1,4,5

When asked to render these processes explicit, theytypically find it difficult to slow them down and toretrace their cognitive steps6. It is therefore oftenhard for medical trainees to grasp the variousdimensions and nuances of their tutors’ clinicalreasoning processes, and for medical teachers tofoster clinical reasoning skills in medical learners.Another challenge routinely facingmedical educators is the identification and remedi-ation of specific errors in clinical reasoning.7–10

A model capturing the richness and complexity ofclinical reasoning processes would therefore be veryuseful to inform teaching, learning and assessment.Such a model is difficult to create because clinicalreasoning depends on mobilising and processing vastnetworks of knowledge that may not be easily accessibleto conscious scrutiny. Early attempts to developgeneric models of clinical reasoning, which depictedits processes in a primarily linear fashion, largely fell bythe wayside years ago. However, modern methods ofconcept mapping11,12 have the potential to reinvigo-rate the field and prompt significant advances in theestablishment of a comprehensive model of clinicalreasoning.

Concept maps are graphical tools for organising andrepresenting knowledge.13,14 Recently, computersoftware has greatly facilitated the dissemination ofconcept mapping as a research tool in many domains,including medical education. However, the concept-mapping software in current use provides neitherguidance nor constraints, such that users are free tocreate an infinite array of possible models. Knowl-edge modelling software that includes grammaticalconstraints during model construction may allowusers to generate maps that are better organised,more complete, more useful in practical settings andmore efficient in communicating information thantheir traditional non-constrained counterparts.11,12

The Modelling using Typified Objects (MOT) soft-ware and technique is an innovative tool with built-in

rules of grammar for guiding modelling.11 It may beused to map clinical reasoning processes and renderknowledge networks and strategies explicit.11,12 Thegoals of this study were: (i) to develop a new,comprehensive representational model of clinicalreasoning processes using MOT Plus software intendedfor use by teachers and learners, and (ii) to providedata on the validity of this model.

METHODS

This research was situated in a socio-constructivistparadigm whereby the processes and guide wereco-constructed with clinical educators who wereinvolved at each step. We used a qualitative method-ology known as participatory action research, aprocess of development carried out collaboratively bya group of people interested in changing practice intheir setting.15

MOT software

The MOT software can be described as a semi-structured cognitive mapping tool. It includes atypology of knowledge objects and a typology of links.This software was developed in 1992 by the LICEF(Laboratoire en Informatique Cognitive et Envi-ronnements de Formation) Research Center, Mon-treal, Quebec, Canada.12 Its current version, MOT Plus1.6.7, is available in English and French, free ofcharge, at www.licef.ca (under ‘Realisations/Produits’). It has been used to help design courses, topromote knowledge transfer in corporations, toenhance e-learning courses and to facilitate infor-mation management.11,12

A MOT model is the graphical expression of a field’sknowledge objects and the links that unite them. Thegrammar of MOT uses geometric symbols to repre-sent types of knowledge objects, such as concepts(rectangles), procedures (ovals) and principles(hexagons). It also defines the types of link that arepermissible between the various types of knowledge.There are six types of link: composition (C: ‘iscomposed of’); specialisation (S: ‘is a sort of’);instantiation (I: ‘is an example or instance of’);precedence (P: ‘precedes’); input ⁄ product (IP: ‘is aninput to’ or ‘produces’), and regulation (R: ‘is aregulating principle for’). Using these types ofknowledge objects and types of link, the MOTsoftware enables the modelling of any field ofknowledge or competence. When a competence or a

ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463 455

Clinical reasoning: graphical representation

knowledge field is complex, a model can beexpanded into several sub-models.11,12

Participants

Six clinician-educators (hereafter referred to asclinician participants [CPs]) were interviewed as agroup in a series of sessions occurring over more than250 contact hours. The participants were selectedfrom different disciplines: four were medical doctors(two in family medicine, one in otolaryngology, onein geriatrics); one was a dentist with expertise in oraland facial pain, and one was a psychologist affiliatedwith a department of family medicine, who worksdirectly with residents experiencing clinical reasoningdifficulties. Three participants were male and threewere female. All CPs had > 15 years of clinicalexperience and were educators with a particularinterest in clinical reasoning.

Construction and validation of the model

The process of participatory action research is oftendescribed as a spiral consisting of several reflectiverounds.15 Following this methodology, we submittedthe MOT model to a three-round validation process.During each round, we solicited critical reflectionsand comments from the participants, and adjustedthe model accordingly. This method thereforeenabled us to concurrently construct, amend andvalidate the model.

Round 1: construction and initial validation

A cognitician, who was familiar with the MOTsoftware and technique, interviewed CPs to ‘extract’and analyse their knowledge, reflections on action16

and cognitive pathways, and subsequently convertedthem into a graphic representation adhering to thegrammatical rules of MOT. Knowledge extractionand graphical construction of the model were inte-grated and based on multiple successive iterations in2-hour sessions held over 2 years. A concurrentprocess of validation occurred during this first stageas the cognitician, who led the discussion, helped CPsto unravel their reasoning processes and identifyinconsistencies and gaps in the emerging model. Theprocess was repeated until all participants felt that themodel was ‘saturated’ (i.e. that it provided anadequate depiction of their conscious cognitivepathways during clinical encounters). During ses-sions, CPs were also encouraged to identify importantdefinitions and concepts from the clinical reasoningliterature that they felt were useful for constructingand validating the model.

Round 2: groups of clinician-validators

The model produced at the end of the first round wasthen submitted to a sample of 12 doctors. The samplewas purposeful. To ensure the assembly of a diversepanel, participants were selected based on theirinterest and experience in medical education andtheir clinical specialties. Potential participants weregiven information on the design of the study. Thestudy was approved by the university’s institutionalreview board and all those contacted gave informedconsent prior to participation.

During this second round, a printed copy of the MOTmodel prepared during Round 1 was presented toclinician-validators (CVs). Clarification questionswere answered and CVs were then randomly dividedinto four groups of three participants that were sentinto separate rooms. Each group encountered asimulated patient (SP) trained to depict the sameclinical scenario involving a chief complaint ofchronic fatigue. The simulated situation was chosenbecause clinicians from all disciplines were felt to belikely to have some familiarity with this commonclinical presentation. In each room, one CV volun-teered to interview the SP, while the other two CVsobserved the interaction, looked at their printedcopies of the model and took notes. If the CVconducting the interview requested a specific exam-ination or laboratory result, the SP provided therelevant data. In all rooms, a CP recorded allobservable indicators of reasoning (questions,hypotheses generated, signs, investigations, etc.). Allencounters were videotaped. When the SP left theroom, the video of the encounter was played andthe three CVs were given the opportunity to commenton reasoning processes they observed or might haveused instead. The goal of this activity was to place CVsin a clinical reasoning situation with the model inhand with which they could compare their ownreasoning pathways.

Finally, all 12 CVs returned to a common confer-ence room, where a plenary session was held inwhich all the models annotated by the CVs wereshared. The CVs were instructed to: (i) specify whichelements within the model were relevant, erroneousor missing; (ii) determine if the model was congru-ent with their perceptions of their own clinicalreasoning processes; (iii) discuss the strengths,weaknesses and potential missing elements of themodel, and (iv) discuss the utility of the model forlearning and teaching clinical reasoning. The entiresecond round of validation took approximately4 hours to complete.

456 ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463

B Charlin et al

Round 3: CPs and the cognitician

In this final round of model validation, data obtainedin the second round were used by CPs and thecognitician to make final adjustments to the MOTmodel with the added aim of producing a model thatwould be detailed enough to reflect the multi-dimensional structure of clinical reasoning, butwould not be prohibitively unwieldy. In other words,CPs were requested to fine-tune the model such thatimportant processes and concepts that emerged inRounds 1 and 2 were included, but excessive detailsthat might render the model cumbersome orimpractical to use as a teaching and learning toolwere avoided.

RESULTS

The Modelling using Typified Objects modellingsessions confirmed that clinical reasoning is a highlycomplex and multifaceted process. The CPs were allseasoned educators, who were well versed in classicalclinical reasoning concepts, such as hypothesis gen-eration or hypothetico-deductive reasoning, but inthe early stages of modelling they realised that certainconcepts required operational definition to ensure acommon base of language and understanding amongthemselves during the construction and validation ofthe model. Space limitations preclude the inclusionin this paper of all discussed concepts. The followingsection defines concepts taken from the medicaleducation literature that prompted discussion amongCPs and were incorporated within the model.

Concepts

Salient concepts that emerged in the research processconcerned diagnosis versus categorisation, types ofknowledge used in clinical encounters, semantictransformation of information, problem solving,problem representation and metacognition.

Diagnosis versus categorisation

Cognitive psychologists consider that diagnosis is acategorisation task that consists of placing patients’illnesses in different classes according to their attri-butes.2,17,18 Because for a doctor ‘the diagnosis’ is aprecise entity and because the primary function inmany medical encounters (such as in emergencymedicine) is to understand the situation enough tobegin action, such as investigation or treatment,rather than to obtain a precise diagnosis, CPs agreedthat ‘categorisation for the purpose of action’ rather

than ‘diagnosis’ better described the product ofinitial phases in clinical reasoning.

Types of knowledge used

There is growing agreement among researchers andmedical educators that several types of knowledgeinteract in a medical encounter.1,4,19,20 The CPs felt itwas necessary to specify these in order to produce amodel that would be useful for teaching and learn-ing. The illness script theory18–23 assumes thatknowledge networks adapted to clinical tasks developthrough experience and operate autonomouslybeneath the level of conscious awareness. Thesenetworks consist of associative links among illnessesand their attributes, consequences, investigation ortreatment, and links with memories of previouslyencountered exemplars of the illness (instances). Inambiguous situations, clinicians search for a fitbetween the available information and appropriatescripts.23,24 The CPs reported that, in clinical situa-tions, they effectively mobilise specific knowledge(scripts) relevant to the particular situation (e.g.knowledge related to vertigo and then knowledgelinked to cervical masses when they see two successivepatients whose main complaints are, respectively,dizziness and progressive swelling of the upper neck).The group reported that clinical reasoning occa-sionally taps into sources of knowledge of a differentnature and that the concept of knowledge encapsu-lation20,25 describes that type of knowledge well. Inthe model, these latter sources of knowledge arereferred to as ‘biological, psychological and socio-logical knowledge’.

Semantic transformation, problem solving, problemrepresentation and metacognition

For cognitive psychologists,26 problem solving in-volves processes that transform the initial state of theproblem into a state in which the goal is achieved. Thekey to solving a problem is to represent it in such a waythat the required processes to understand and solve itcan apply.26 Problem representation has a crucial rolein problem solving.26,27 It was clear to participantsthat the clinician’s representation of a patient’sproblem changes over the course of the encounterwith the discovery of additional data. Semantic trans-formation emerged as another key concept.28 Clini-cians ascribe meaning to the presenting symptomsand clinical findings by transforming the data usingsemantic qualifiers that represent conceptualisationor abstraction of the clinical findings. For instance, ‘apatient’s painful, swollen, right knee that began2 nights ago with attacks 2 and 9 years ago’ is

ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463 457

Clinical reasoning: graphical representation

transformed in ‘an acute, recurrent attack of abrupt,nocturnal and extremely severe pain in a single, largejoint’. Metacognition refers to an individual’s knowl-edge concerning his or her own cognitive pro-cesses.2,29 In medical practice, the application ofmetacognition implies that the clinician is aware ofencounter goals and verifies that his or her cognitiveprocesses and actions are contributing to theachievement of the goals of the clinical encounter.

The MOT model

The MOT model is hierarchical. It is depicted on sixscreens that show, respectively, a main model and fivesub-models. Four screens are presented here.The complete, updated model (with appendices)can be accessed at www.medent.umontreal.ca/clinical-reasoning/. Figure 1 features the main mod-el, a graphical representation of the core clinicalreasoning processes that emerged during develop-ment of the model. Beginning from the left side ofthe screen, the concepts (rectangles) ‘Context’ and‘Patient’ are rich input sources for the process (oval)‘Identify early cues’. The product of this process,‘Initial data’, then feeds into the process ‘Determinethe objectives of the encounter’. These objectives

orient the process ‘Categorise for the purpose ofaction’. This process has two possible outcomes:‘Categorisation is suitable for purposeful action’ and‘Categorisation is NOT suitable for purposefulaction’. The former outcome feeds another process,‘Implement purposeful action’, which has ‘Investiga-tions’ and ‘Therapeutic interventions’ as outputs.Results of these actions are then evaluated. Ifsatisfactory, the episode of care is terminated. If not, thecategorisation process or the therapeutic interven-tions put in place are reconsidered.

Within Fig. 1, the core clinical reasoning processesare depicted mainly along the screen’s horizontalaxis. However, the vertical axis of the figure is alsoimportant. Doctors’ repertoires of knowledge arerepresented beneath the series of core processes byrectangles entitled ‘Clinical knowledge organised foraction (= illness scripts)’ and ‘Biological, psycholog-ical, sociological knowledge’. Links show that thelatter underpins the former and that each clinicalencounter modifies and enriches illness scripts.Above the series of core processes, problem repre-sentation is horizontally depicted by three rectanglesdesignating, respectively, ‘Initial representation’,‘Dynamic representation’ and ‘Final representation’.

IPIP

Context IPIP

Final representationof the problem

Semantic transformation of datais a key process throughout the

clinical encounter

R

IP

IP

IP

Therapeuticinterventions

IP

Throughout the process, the doctor considersthe problem from the patient's perspective,

taking into account the patient's beliefs, fears,expectations, choices, and feelings, as well asthe impact of the problem on the patient's life

IP

IP

Categorisation isNOT suitable

for purposeful actionPatient

Organise knowledge for clinicalaction

IP

IP

IP

IP

IP

Investigations

IP

Initial representationof the problem Dynamic representation of the problem

IP

IP

Categorisation is suitablefor

purposeful action

Find alternativehypotheses

IP

IP

IP

Initial data

Biological, psychological, sociological knowledge

IP

R

Objective(s) of theencounter

Clinical experience

Evaluate theresults

IP

R

IP

Each clinical encountermodifies and enriches

illness scripts.

IP

Clinical knowledge organised for action( = illness scripts)

IP

IP

Categorise for thepurpose of action

Identify early cuesDetermine the

objectives of theencounter

Implementpurposeful action

Regulate one's owncognitive process(Metacognition)

IP

R

IP

IP

Figure 1 Graphical representation of core clinical reasoning processes; main model

458 ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463

B Charlin et al

Above all is the metacognition process. A regulatingprinciple, contained in a hexagon, describes theimportance during clinical reasoning of taking intoaccount the patient’s perspective and the impact ofthe illness on the patient’s life.

On the site (www.medent.umontreal.ca/clinical-reasoning), clicking on red arrows allows the user tonavigate between the main model and the five sub-models that depict specific processes from the mainmodel in greater detail. Examples of these sub-screens are shown in Figs 2–4.

Figure 2 depicts the five processes (ovals) thatunderlie the process ‘Determine the objective(s) ofthe encounter’: (i) ‘Clarify the patient’s request(s)’;(ii) ‘Acknowledge the request’; (iii) ‘Acknowledgethe information’; (iv) ‘Identify the patient’s needs’,and (v) ‘Establish priorities’. As shown, ‘Patient’srequest(s)’ (input, represented by a rectangle) feedsthe process ‘Clarify the patient’s request(s)’, which

produces as outcome (another rectangle) the‘Patient’s needs according to the patient’. ‘Requestfrom the referring doctor’ (an input) feeds theprocess ‘Acknowledge the request’, which producesas outcome ‘Patient’s needs according to the refer-ring doctor’. ‘Identify the patient’s needs’ is aprocess that results in ‘Patient’s needs according tothe doctor’. The four outcomes feed the fifthprocess, ‘Establish priorities’, which ultimately pro-duces the ‘Objective(s) of the encounter’. Thisprocess is regulated by a principle (contained in ahexagon): ‘When establishing priorities, factors suchas urgency, appropriateness and efficiency must beconsidered.’

Figure 3 depicts the sub-model ‘Categorise for thepurpose of action’. This process is informed bypatient data and the objectives judged to be relevantin the encounter. It bears upon repertoires ofknowledge for action (illness scripts). It is composedof a succession of six sub-processes: (i) ‘Search for

Patient

IP

IPClinical knowledgeorganised for action( = illness scripts)

Search for relevantdata

New set of activated scripts andtheir associated attributes

New clinical data

Deliberately searchfor an explanatory

hypothesis

IP

Search for apattern or an

analogous case

IP

Objective(s) of the encounter

Initial hypothesis andassociated activated script

IP

No initial hypothesis

IPIP

Deliberately generateother explanatory

hypotheses

IP

IP

Evaluate the fit betweenthe clinical data and the

attributes of the activatedscript(s)

IP

IP

Initial clinical data

IP IP

IP

IP

Categorisation is NOT suitable forpurposeful action

IP

Categorisation is suitablefor purposeful action

IP

Orient search for furtherdata to confirm or refute

hypotheses

Figure 3 Process: ‘Categorise for the purpose of action’

IP

IP

Acknowledge therequest

Acknowledge theinformation

IP

Patient's needsaccording

to the patient

Clinical informationprovided by the third

party

IP

R

When establishing priorities,factors such as urgency,

appropriateness, and effciencymust be considered

Establishpriorities

Request from thereferring doctor

Identify thepatient's needs

IP IP

Clinical information provided by athird party (e.g. another professional

or a patient's relative)

IP

Initial dataPatient'srequest(s)

IP

IP

IPIP

Objective(s) of theencounter

Clarify thepatient's

request(s)

IP

IP

Patient's needsaccording

to the doctor

Patient's needsaccording to thereferring doctor

Figure 2 Process: ‘Determine the objective(s) of the encounter’

ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463 459

Clinical reasoning: graphical representation

relevant data’; (ii) ‘Search for a pattern or ananalogous case’; (iii) ‘Deliberately search for anexplanatory hypothesis’ (if there is no initial hypoth-esis); (iv) ‘Deliberately generate other explanatoryhypotheses’; (v) ‘Orient search for further data toconfirm or refute hypotheses’, and (vi) ‘Evaluate thefit between the clinical data and attributes of theactivated scripts’. The activation of hypotheses andtheir associated scripts gives access to script attributesthat inform the oriented search for data to confirm orrefute hypotheses. The process ‘Categorise for thepurpose of action’, composed of these six sub-processes, has two possible outcomes: ‘Categorisationis suitable for purposeful action’ and ‘Categorisationis NOT suitable for purposeful action’. Subsequentprocesses are launched by each of these outputs.

Figure 4 depicts the series of processes that arelaunched by the output ‘Categorisation is suitable forpurposeful action’. Investigation and treatment compo-nents of selected script(s) are then activated. Factors thatmight influence the selection of particular investiga-tive or management options, such as practical con-siderations (e.g. cost, availability), evidence from theliterature, patient profile and preferences, the par-ticularity of the situation, and the clinician’s level ofexperience, are then considered. The myriad possibleoptions are thus narrowed down to several that arerelevant to the current case and fit well with theclinical data. These are discussed with the patient,leading to the selection of appropriate actions andproducing ‘Investigations’ and ‘Therapeutic inter-ventions’ that are then implemented.

The two other sub-models, which describe theprocesses ‘Identify early cues’ and ‘Regulate cognitiveprocesses’, are available online (Figs S1 and S2).

DISCUSSION

Toward a new ‘enriched’ generic process model ofclinical reasoning

The present study represents a modern revival ofattempts to model generic clinical reasoning path-ways.3,30–32 Our own model, derived through a com-puter-generated cognitive mapping methodology,depicts clinical reasoning as a series of highly complex,multidimensional, non-linear processes that dependon the mobilisation of specific knowledge held inlong-term memory. Our view is that the MOT model isnot at odds with current concepts regarding thequality and content of the knowledge base, or postu-lated mechanisms such as hypothesis generation orproblem representation; in fact, it encompasses themand are necessary elements of the model.

Research has shown that clinicians follow divergentlines of reasoning when solving a problem33 and thatthere rarely exists a ‘single correct pathway’ to aclinical solution. Nevertheless, our study suggests thatdoctors across different disciplines traverse commoncognitive signposts when reasoning through cases intheir respective domains. These clinical reasoningsignposts, initially identified by our CPs, were subse-quently acknowledged and validated within a largearray of medical specialties such as neurology,psychiatry and internal medicine, and even dentistry.This indicates that there are common reasoningprocesses that clinicians across different disciplinesroutinely employ, and common steps that all seem tobe essential for success in problem solving duringclinical encounters. We suspect that, depending onthe discipline, some processes may be more salient

IP

IP

Evaluate the fit betweenthe clinical data and theattributes of the relevant

options

IP

IP

Therapeutic intervention plan

Relevant options

Evidence-baseddata

Activated script(s)

Clinicianexperience

IP

IP

IP

IP

Address theINVESTIGATION and

INTERVENTIONcomponents of theactivated script(s)

Particularityof the patient

Implementinvestigation and/or

therapeuticintervention plan

Investigation plan

IP

Investigative andtherapeutic options

Selected options

C

IP

Discuss theoption with the

patient

IP

IP

Therapeuticinterventions

IP

Investigations

Practicalconsiderations

IP

Particularityof the situation

IP

Consider factors that mightinfluence the choice of

particular investigative ormanagement options

IP

IPIP

Categorization is suitablefor purposeful action

Figure 4 Process: ‘Implement purposeful action’

460 ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463

B Charlin et al

but that the essence of the processes is similar. Forinstance, categorisation ⁄ diagnosis is often central inmedicine, whereas in dentistry a greater part ofreasoning may revolve around treatment planning.

Lessons drawn from the development of the MOTmodel

The extraction and graphical representation of theclinical reasoning processes of our participants raisedseveral important points. Firstly, it confirmed thedynamic nature of problem representation2,27

throughout a clinical encounter. Secondly, CPs foundthe processes they used when considering manage-ment (i.e. investigation and treatment) options verysimilar to those they employed during categorisationprocesses: both entailed a search for a fit betweenactivated scripts and information derived during theclinical encounter. For instance, performance of alumbar puncture (an investigative option) ‘fits’ withthe investigation script ‘measure the patient’s intra-cranial pressure’ only if there is no clinical evidenceof elevated intracranial pressure (such as papilloe-dema, which might be considered an unacceptablevalue for the ‘measure intracranial pressure’ scriptprior to the acquisition of a computed tomographyscan of the head). Thirdly, the core reasoningprocesses depicted on the horizontal axis of the mainscreen of the model suggest that, from the moment aclinical encounter begins, a whole series of concom-itant cognitive actions occur: the clinical reservoir ofknowledge is tapped; scripts are mobilised andenriched, and from time to time encapsulated bio-psycho-social knowledge is accessed. All these pro-cesses work in parallel and are under the control ofmetacognition, indicating that clinical reasoning isnot a linear process consisting of a succession ofsteps, but, rather, a complex deployment of numer-ous cognitive processes.

Potential educational applications of the MOT modelof clinical reasoning

The identification and graphical representation ofthe complexity of clinical reasoning has potentiallyimportant applications for use within undergraduateand graduate medical curricula to inform teaching,learning and assessment. The MOT model could bepresented early in these curricula as an advanceorganiser, thus providing students with an overview ofexperts’ clinical reasoning processes. It could be usedto support curricular development because it canhelp to identify opportune moments for students tolearn specific elements of clinical reasoning, and helpstudents gain a global appreciation of clinical

reasoning processes even while they concentrate onspecific aspects in isolation in dedicated teachingsessions.

Under the complex and ambiguous conditions thatcharacterise daily practice, seasoned clinicians areattentive to a wide variety of dimensions of a clinicalencounter and reasoning cannot be successful ifsome of these dimensions are neglected. A narrowview of the multidimensional nature of clinicalreasoning may explain the paucity of methods avail-able to medical educators for fostering the acquisi-tion of clinical reasoning skills, detecting learnerswith deficiencies in clinical reasoning, and offeringappropriate remediation to those who have beenidentified as learners in trouble.7–10 Although someclinical reasoning difficulties may be linked to clearlydelineated steps in the reasoning process (e.g. ‘datagathering’ or ‘hypothesis generation’), others may beascribed to less commonly identified factors, such asability to appreciate the clinical context, ability todeal with uncertainty, or ability to communicateeffectively. The relationship between these factorsand clinical reasoning is rarely discussed in theliterature. Our model may be a useful tool forbringing these crucial yet often overlooked dimen-sions of clinical reasoning to light for the benefit ofdiscussion and instruction among teachers andlearners.

Written assessments of clinical reasoning typicallyprovide a brief clinical context and then ask ques-tions such as ‘What is the correct or most probablediagnosis?’ or ‘What is the most relevant investiga-tion or treatment option?’ Such formats assess theoutcome of clinical reasoning, rather than its pro-cesses. As long as the right answer is provided by thelearner, reasoning is considered acceptable and noregard is paid to how the answer is obtained. In suchinstances, educators who assess clinical reasoning donot actually have access to learners’ reasoningprocesses and are therefore unable to identifywhether important elements are lacking or are beingmisused. Educators are therefore generally good atdetecting ‘those who do not have it’, but are rarelyable to point out precisely where in a learner’sreasoning processes about a particular case theproblem lies, and thus have difficulty designingappropriate remediation activities.10 The MOTmodel can be used as a guide to test specific parts oflearners’ clinical reasoning processes, and thereforeto help pinpoint flaws in reasoning as learnersreason through clinical encounters. Remediationactivities can then be designed to target thesespecific areas of difficulty.

ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463 461

Clinical reasoning: graphical representation

Study limitations

This study has several limitations. The 12 CVs were alldoctors; health professionals from other health-related disciplines were therefore under-representedin this particular cohort. Furthermore, the sixclinicians who participated in the iterative knowledgeextraction and modelling sessions were all stronglyimplicated in the teaching of clinical reasoning, wereall informed of the literature on clinical reasoning,and had more than basic knowledge of cognitivesciences. They were inevitably influenced by theseconcepts from the literature and perceived them ascritical both for unravelling their own clinicalreasoning processes and for facilitating clinical edu-cation. However, that the CPs held these character-istics can be viewed as a strength as well as a limitationof the study because the research method – partici-patory action research – borrows techniques fromqualitative methodology, in which the co-construc-tion process is carried out collaboratively by a groupof people who are interested in changing practice intheir setting.

It is interesting to consider whether reasoningprocesses and their representations would have beendifferent had the study involved other CPs andanother cognitician. It is likely that the model woulddiffer in detail, as symbols (ovals, rectangles, arrows)can be arrayed in slightly different manners, with noultimate consequence for conceptualisation. How-ever, the nature of the processes and the nature oflinks are another matter. A first concern refers towhether all key processes have been unveiled. Asknowledge extraction was undertaken in multiplesessions in an iterative way, we think that participants’conscious reasoning processes continued to revealthemselves until saturation was reached; only findingsfrom research using similar or other techniques willconfirm or refute this. A second concern refers tohow realistically these cognitive operations aredepicted. In this respect the MOT software andtechnique offer significant advances over previoustools used for the purpose of deriving cognitive maps.

The rules inherent in the modelling technique inMOT Plus often led to the detection of logical errorsduring the construction of models. Links that did notaccurately express the experts’ knowledge indicatedthat the model probably contained a logical error. Byprobing participants further it was possible to correctthe mistake and enhance the precision of the map.Additionally, the graphical layout of the model oftenrevealed possible faulty interpretations. For example,the grammar in MOT Plus very rarely allows links to

cross. When this situation arises, analysis almostalways shows that knowledge is incorrectly interpretedor that the model is incomplete. Within the sessions,resolving these issues often revealed tacit knowledgethat had not surfaced before.

CONCLUSIONS

The MOT model constitutes an explicit graphicalrepresentation of the multifaceted processes of clin-ical reasoning. Its validity across disciplines andsituations needs to be confirmed, but it has thepotential to inform the acquisition, teaching andassessment of clinical reasoning skills, to help clinicaleducators identify flawed reasoning processes in theirstudents and residents and design appropriate reme-diation activities, and to orient future research studiesthat aim to unravel more of the complexities ofclinical reasoning.

Contributors: BC co-led the conception and design of thestudy and is a principal co-author of the manuscript. SLparticipated in the validation session and is a principalco-author of the paper. BM conceived and organised thevalidation session. FC co-led the conception and design ofthe study. BC, FC, M-CA, AC, NC and CB served as clinician-participants and were responsible for developing the ideasand analysing the findings of the study. LH described thetechnical aspects of MOT methodology and wrote apreliminary version of the paper. All authors contributed tothe critical revision of the paper for intellectual content andapproved the final manuscript for submission.Acknowledgements: Valerie Dory, Institute of Health andSociety (IRSS), Universite catholique de Louvain, Brussels,Belgium (VD) and Mark GoldszmidtDepartment ofMedicine, Schulich School of Medicine & Dentistry,London, ON, Canada (MG), provided useful comments ondrafts of the paper.Funding: none.

Conflicts of interest: none.

Ethical approval: the validation session described in thisstudy was approved by the University of Montreal/Faculty ofMedicine Institutional Review Board.

REFERENCES

1 Norman G. Research in clinical reasoning: past historyand current trends. Med Educ 2005;39:418–27.

2 Gruppen L, Frohna A. Clinical reasoning. In: NormanGR, van der Vleuten CP, Newble DI, eds. InternationalHandbook of Research on Medical Education. Dordrecht:Kluwer Academic 2002;205–30.

3 Schwartz A, Elstein AS. Clinical reasoning in medicine.In: Higgs J, Jones M, Loftus S, Christensen NC, eds.

462 ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463

B Charlin et al

Clinical Reasoning in the Health Professions, 3rd edn.Edinburgh: Elsevier 2008;223–34.

4 Eva KW. What every teacher needs to know aboutclinical reasoning. Med Educ 2005;39:98–106.

5 Bowen J. Educational strategies to promote clinicaldiagnostic reasoning. N Engl J Med 2006;355:2217–25.

6 Bargh JA, Chartrand TL. The unbearable automaticityof being. Am Psychol 1999;54:462–79.

7 Bordage G. Why did I miss the diagnosis? Somecognitive explanations and educational implication.Acad Med 1999;74 (Suppl):138–43.

8 Mamede S, Schmidt HG, Rikers R. Diagnostic errorsand reflective practice in medicine. J Eval Clin Pract2007;13:138–45.

9 Hauer K, Ciccone A, Henzel T, Katsufrakis P, Miller S,Norcross W, Papadakis M, Irby D. Remediation of thedeficiencies of physicians across the continuum frommedical school to practice: a thematic review of theliterature. Acad Med 2009;84:1822–32.

10 Audetat MC, Dory V, Nendaz M, Vanpee D, Pestiaux D,Junod Perron D, Charlin B. What is so difficult aboutmanaging clinical reasoning difficulties? Med Educ2012;46:216–27.

11 Basque J, Pudelko B. Intersubjective meaning-makingin dyads using object-typed concept mapping. In:Torres PL, Marriott RCV, eds. Handbook of Research onCollaborative Learning using Concept Mapping. New York,NY: Hershey 2009;180–206.

12 Paquette G. Modelisation des Connaissances et des Compe-tences. Un Langage Graphique pour Concevoir et Apprendre.Sainte-Foy, QC: University of Quebec Press 2002.

13 Novak JD, Canas AJ. The Theory underlying Concept Mapsand How to Construct and Use Them. Technical ReportIHMC CmapTools. Florida Institute for Human andMachine Cognition 2008. http://cmap.ihmc.us/publications/researchpapers/theorycmaps/theoryunderlyingconceptmaps.htm. [Accessed 27 February2012].

14 Daley BJ, Torre DM. Concept maps in medical educa-tion: an analytical literature review. Med Educ2010;44:440–8.

15 Kemmis S, McTaggart R. Participatory action research:communicative action and the public sphere. In:Denzin N, Lincoln Y, eds. Handbook of QualitativeResearch, 3rd edn. Beverley Hills, CA: Sage Publications2005;271–330.

16 Schon D. From technical rationality to reflection inaction. In: Dowie J, Elstein A, eds. Professional Judgment:a Reader in Clinical Decision Making (1988). Cambridge:Cambridge University Press 1983;60–77.

17 Gilhooly KS. Cognitive psychology and medicaldiagnosis. Appl Cogn Psychol 1990;4:261–72.

18 Charlin B, Tardif J, Boshuizen HP. Scripts and medicaldiagnostic knowledge: theory and applications forclinical reasoning instruction and research. Acad Med2000;75 (2):182–90.

19 Custers EJ, Regehr G, Norman GR. Mental represen-tations of medical diagnostic knowledge: a review. AcadMed 1996;71 (Suppl):55–61.

20 Schmidt H, Rickers R. How expertise develops inmedicine: knowledge encapsulation and illness scriptformation. Med Educ 2007;41:1133–9.

21 Feltovich PJ, Barrows HS. Issues of generality inmedical problem solving. In: Schmidt HG, De VolderML, eds. Tutorials in Problem-Based Learning. Assen ⁄Maastricht: Van Gorcum 1984;128–42.

22 Schmidt HG, Norman GR, Boshuizen HPA. A cognitiveperspective on medical expertise: theory and implica-tions. Acad Med 1990;65:611–21.

23 Charlin B, Boshuizen HP, Custers EJ, Feltovich PJ. Scriptsand clinical reasoning. Med Educ 2007;41:1178–84.

24 Lurie S. Towards greater clarity in the role of ambiguityin clinical reasoning. Med Educ 2011;45:326–8.

25 Boshuizen HPA, Schmidt HG. On the role of bio-medical knowledge in clinical reasoning by experts,intermediates and novices. Cogn Sci 1992;16:153–84.

26 Anderson J. Problem solving. In: Anderson J, ed.Cognitive Psychology and its Implications, 3rd edn. NewYork, NY: W H Freeman 1990;219–55.

27 Nendaz M, Bordage G. Promoting diagnostic problemrepresentation. Med Educ 2002;36:760–6.

28 Bordage G. Prototypes and semantic qualifiers: frompast to present. Med Educ 2007;41:1117–21.

29 Flavell JH. Metacognition and cognitive monitoring, anew area of cognitive–developmental inquiry. AmPsychol 1979;34:906–11.

30 Elstein AS, Shulman LS, Sprafka SA. Medical ProblemSolving: An Analysis of Clinical Reasoning. Cambridge,MA: Harvard University Press 1978;273–302.

31 Balla JI. The Diagnostic Process: A Model for ClinicalTeachers. Cambridge: Cambridge University Press1985;49–106.

32 Barrows HS, Feltovich PJ. The clinical reasoningprocess. Med Educ 1987;21:86–91.

33 Grant J, Marsden P. Primary knowledge, medicaleducation and consultant expertise. Med Educ 1988;22:173–9.

SUPPORTING INFORMATION

Additional supporting information may be found in theonline version of this article.

Figure S1. Process: ‘Identify early cues’.

Figure S2. Process: ‘Regulate cognitive processes’.

Please note: Wiley-Blackwell is not responsible for thecontent or functionality of any supporting materials sup-plied by the authors. Any queries (other than for missingmaterial) should be directed to the corresponding authorfor the article.

Received 18 July 2011; editorial comments to authors 12 October2011; accepted for publication 1 November 2011

ª Blackwell Publishing Ltd 2012. MEDICAL EDUCATION 2012; 46: 454–463 463

Clinical reasoning: graphical representation