a new integrated model of clinical reasoning: development, description and preliminary assessment in...

REHABILITATION IN PRACTICE

A new integrated model of clinical reasoning: Development, descriptionand preliminary assessment in patients with stroke

PANAGIOTA NIKOPOULOU-SMYRNI & CHRISTOS K. NIKOPOULOS

School of Health Sciences and Social Care, Brunel University, Uxbridge, Middlesex, UK

Accepted July 2006

AbstractPurpose. The main objective was the development and collection of preliminary data on the application of a new integratedclinical reasoning model (Anadysis) with patients suffering a stroke or Transient Ischemic Attack (TIA).Method. Twelve healthcare professionals working in the neurological and the Accident and Emergency (A&E) units of anacute general hospital participated and experimental control was achieved by employing a pre-test post-test control groupexperimental design. Members of the control group used the current reasoning model of their discipline whereas the newintegrated model was used by the members of the experimental group irrespective of their professions. Outcomes weremeasured by scoring on a protocol derived from the UK National Clinical Guidelines for Stroke divided into the three mainclinical reasoning processes.Results. Collectively, data from 186 protocols based on the medical records of 49 patients showed that median percentagesof correct responses in clinical reasoning were substantially higher for the experimental group by using the new integratedmodel.Conclusions. This study will inform the healthcare professionals about a new effective integrated clinical reasoning modelwhich incorporates the complex processes of diagnosis, planning and treatment as a whole. This study may also become animportant consideration in the further development of clinical decision support systems within the scientific area of healthinformatics.

Keywords: Clinical reasoning, model, patients with stroke, health informatics

Introduction

Advances in information technology and evidence-

based practice in healthcare have spurred the need to

capture the knowledge and the variety of clinical

reasoning processes that healthcare professionals use

to assess and treat patients [1 – 3]. Currently, each

healthcare discipline considers clinical reasoning

process under a different theoretical approach and

thus, each professional applies the knowledge of his/

her own discipline to problem-solving and decision-

making [2,4 – 6]. Hence, clinical reasoning models

have been structured to represent the available body

of specialized clinical knowledge. These models,

however, lack to integrate the diverse reasoning

processes of diagnosis, planning and treatment as

employed by each healthcare profession [7 – 9].

Adequate modelling of these clinical reasoning

processes is one of the major research challenges and

will be critical for the practical performance of

clinical decision support systems and consequently

for the improvement of patients’ care [10,11]. In the

light of this demand and particularly in the UK,

initiatives such as the NHS Plan [12], A First Class

Service [13] and the Moving Ahead [14] have set up

an emphasis on integrated documentation as the key

strategy for developing health informatics solutions

and a universal clinical reasoning process [15,16].

Such a strategy aims to enhance multidisciplinary

collaboration, and deliver a coordinated and inte-

grated health service to end-users [17].

Accordingly, the main objective of this study

was to collect preliminary data on the application

of a new integrated clinical reasoning model

Correspondence: Dr Panagiota Nikopoulou-Smyrni, School of Health Sciences and Social Care, Mary Seacole Building, Brunel University, Uxbridge,

Middlesex, UB8 3PH, UK. E-mail: [email protected]

Disability and Rehabilitation, July 2007; 29(14): 1129 – 1138

ISSN 0963-8288 print/ISSN 1464-5165 online ª 2007 Informa UK Ltd.

DOI: 10.1080/09638280600948318

Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

(i.e., Anadysis) with patients suffering a stroke or

Transient Ischemic Attack (TIA). A brief account of

the development of this model is also provided.

Materials and methods

The Anadysis clinical reasoning model

The Anadysis model was designed taking into

account the definition of clinical reasoning which

suggests that healthcare professionals form a diag-

nosis of the clinical problem, plan and determine

treatment goals and intervention by using the

cognitive processes that involve information proces-

sing, problem solving, judgement and decision

making [8]. Specifically, clinical reasoning in the

Anadysis model was treated as a process of reflective

inquiry, in collaboration with a patient or family (if

possible), which seeks to promote a deep and

contextually relevant understanding of the clinical

problem, in order to provide a sound basis for

clinical intervention.

The name Anadysis was inspired by the Greek

language, in which it means ‘‘evolution’’, and it

comprises of the following essential steps: (a) Gather

initial data (step 1); the assessment which will

provide the baseline data needed to define the nature

and severity of problems to predict outcomes, to plan

treatments and to evaluate functional outcomes, (b)

Determine the nature and severity of the problems (step 2);

the importance of these problems and their interac-

tions with each other. In particular, cognition,

communication, and behaviour, are considered

along with the patient’s disabilities, resources, and

potential for change, (c) Predict risk-adjusted outcomes

(step 3); the determinants of outcome can be

grouped into those independent of the episode or

disorder (e.g., age, pre-morbid status, personal

preferences and motivation), those related to the

disorder itself (e.g., type and extent of brain

damage), and those that reflect the pattern of

recovery (e.g., stage of recovery and duration of

motor paralysis), (d) Develop treatment goals (step 4);

short-term and long-term goals need to be realistic in

terms of current levels of disability and the potential

for recovery and they should be mutually agreed by

the patient, family and clinical healthcare team, (e)

Develop a management plan and select specific interven-

tions (step 5); the clinical management plan should

indicate the specific treatments planned and their

sequence, intensity, frequency, and expected dura-

tion, (f) Specify the treatment target (step 6); the target

guides the health professional in knowing when to

stop treatment, change its intensity, or switch to

some other treatment, (g) Provide the best therapy and

monitor progress (step 7); the complexity of the

relationships among various health problems

(e.g., physical, cognitive, communicative and beha-

vioural) requires that treatment or rehabilitation of

one problem or disability does not occur in isolation.

If the patient’s problems are not responding to care

as predicted, treatment goals and the treatment plan

should be re-examined, (h) Complete discharge plans

and evaluate outcomes (step 8); this assessment should

focus on both the extent to which disabilities were

minimized and the clinically important changes that

were achieved. Absence of progress on two succes-

sive evaluations before the achievement of the

treatment goals should lead to reconsideration of

the treatment regime or the appropriateness of the

current setting, (i) Re-integration into the community

(step 9); information on community resources

should be provided to patients’ families and health-

care professionals as well as assistance in obtaining

needed services should be offered, and (j) Suspected

need for further intervention (step 10); the treatment

goals have been fully achieved and the patient stops

treatment and safely returns to the community.

Nevertheless, the patient’s progress should be

evaluated within regular intervals during at least the

first year and the patient should be referred back to

the hospital for further intervention in case of any

potential remaining problems (Figure 1 illustrates

the steps of Anadysis).

During the development of this model, qualitative

data were obtained to monitor its reliability (e.g.,

[18]) on the treatment of specific gait patterns that

four patients had developed after a stroke onset. In

particular, an already piloted evaluation form was

delivered and filled in by one doctor of physical

medicine and rehabilitation, one doctor of general

pathology, two senior physiotherapists and two

senior occupational therapists. Collectively, Anadysis

model was reported (a) to integrate the basic

characteristics of clinical reasoning and to reduce

maintenance (problem areas could be identified and

treated at an earlier stage during the treatment

process); (b) to encourage the healthcare profes-

sionals to exercise their creative knowledge and

experience step by step while approaching the

desired goal; and (c) to adequately describe

the precise processes which are common across the

existing clinical reasoning models and favoured by

the different disciplines.

The four traditional clinical reasoning models

For the purposes of the present study, the four

clinical reasoning models which are currently used

by the members of a hospital multidisciplinary team

and the new integrated model described above were

employed. A brief reference to the steps of each of

these four models along with the definitions of

clinical reasoning which reflect their development

1130 P. Nikopoulou-Smyrni & C. K. Nikopoulos

Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

would be regarded as essential and it is provided

below.

Clinical reasoning in medicine refers to the cognitive

process employed by medical doctors in the analysis

and interpretation of data that enables them to arrive at

a diagnosis and to make decisions with regard to

patient treatment and management [2]. This involves

synthesis and integration of patient/community infor-

mation with the medical doctor’s knowledge and

experience [19]. Medical doctors are increasingly

challenged on how best to integrate evidence in

making decisions about day-to-day care of their

patients [20 – 21]. Hence, a six-step clinical reasoning

model has become entrenched in medical practice

which in essence consists of: (a) gathering evidence,

(b) interpretation of evidence, (c) probability assess-

ment, (d) treatment outcomes, (e) treatment fram-

ing, and (f) number of alternatives [22].

Literature has suggested that clinical reasoning in

nursing is a process that involves those cognitive

processes and strategies that nurses use to under-

stand the significance of patient data, to identify and

diagnose actual or potential patient problems, to

make clinical decisions to assist in the problem

solution, and to achieve positive patient outcomes

(e.g., [6,23 – 25]). Thus, the relative clinical reason-

ing process consists of: (a) assessment, (b) diagnosis,

(c) planning, (d) implementation, and (e) evaluation

[26].

The term clinical reasoning in physiotherapy refers

to the thinking processes associated with a therapist’s

examination and management of a patient [27].

While a growing interest in clinical reasoning in

physiotherapy exists, research is considered rather

limited (e.g., [28 – 32]). Initially, Rothstein and

Echternach [33] proposed a hypothesis-orientated

model of clinical reasoning to logically guide

therapists’ evaluation and treatment planning activ-

ities. Further discussions, however, attempted to

expand on the hypothesis-orientated algorithm

Figure 1. Anadysis: An integrated model of clinical reasoning.

Clinical reasoning in stroke patients 1131

Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

through elaboration of the underlying cognition

behind the various components of the clinical

decision making process. Thus, a more advanced

clinical reasoning process in physiotherapy emerged

by Jones [34], which tends to be widely used [27].

According to this model, the physiotherapists’ overall

management of the patient includes: (a) information

perception and interpretation, (b) initial concept and

multiple hypothesis, (c) evolving concept of the

problems, (d) decision, (e) physiotherapy interven-

tion, and (f) re-assessment.

Clinical reasoning in occupational therapy has been

a focus of much study in the past several years (e.g.,

[35 – 38]). It concerns the many types of inquiry that

an occupational therapist uses to understand patients

and their difficulties. In essence it is a process of

figuring out and perceiving how to act and what to

do in specific circumstances involving the patient’s

well-being [39]. Then the goal is to decide upon and

take the best action for a particular patient at that

particular time [40 – 41]. Hopkins [42] described a

generic, though well constructive, clinical reasoning

process consisted of ten steps. This model is still

regarded essential in occupational therapy clinical

reasoning [43] and it includes the following steps: (a)

referral, (b) data gathering, (c) data analysis and

problem identification, (d) selection of a frame of

reference, (e) selection of treatment objectives, (f)

selection of treatment methods, (g) implementation

of treatment plan, (h) ongoing assessment of patient,

(i) ongoing revision of treatment plan, and (j)

discharge planning.

All steps of each traditional clinical reasoning

model were carefully adopted within the new

integrated model, whilst new ones were introduced.

This is clearly illustrated in Table I that provides a

correlation of the steps of each traditional model with

those of Anadysis.

Participants – settings

Twelve healthcare professionals (2 medical doctors,

2 senior nurses, 4 senior physiotherapists and 4

senior occupational therapists) working in the

neurological and the Accident and Emergency

(A&E) units of an acute general hospital participated

in the study. After they had been informed about the

main objectives of the study they provided formal

written consent for their participation. Each health-

care professional was required to have working

experience of at least 3 years in stroke patients’

assessment and treatment as well as to be a member

of a multidisciplinary team.

Procedure

A diagrammatic representation of the general

procedure is depicted in Figure 2. The 12

healthcare professionals were randomly assigned to

two equal groups – the control and the experimental

group – by flipping a coin for the purposes of a pre-

test post-test control group experimental design

[44 – 46]. The inclusion of the control group was

made in order to establish the internal validity of this

study in terms of enhancing the control for extra-

neous variables [47]. Data collection was made

through the medical records of patients who had

suffered from a stroke or a TIA. Specifically, for the

pre-test condition (baseline), data were collected

from the medical records of 12 patients who had

been assessed and treated by the members of both

groups. For the post-test condition, however, the

participants of each group had to attend a training

course, especially designed for this study. The

objectives of the training course (training 1) deliv-

ered to the control group were to collect information,

describe, explain, clarify and agree on issues regard-

ing the current clinical reasoning models that were

used in the UK according to each participant’s

specialty. On the contrary, all members of the

experimental group attended the same training

course (training 2) irrespectively of their professional

specialty. The objectives of this training course were:

(a) to collect information about the model of clinical

reasoning that each healthcare professional was using

in his/her daily clinical practice which would facilitate

an efficient discrimination between the steps of the

respective and those of the new integrated model

named Anadysis; and (b) to describe, explain, clarify

and agree on issues concerning the Anadysis clinical

reasoning model which was developed by the

primary researcher. It was also agreed that the

healthcare professionals would use the Anadysis

model whenever enough time was available for the

application of such a novel model, setting patients’

safety as their first priority. Data from this condition

Table I. Correlation of the steps of each traditional model with the

steps of Anadysis model.

Traditional

clinical reasoning

models

Steps included

in both the

traditional and

Anadysis models

Steps included

only in the

Anadysis model

Medical doctors 1, 2a, 3, 4b, 6, 7c 5, 8, 9, 10

Nurses 1, 2a, 4b, 5, 6, 7c, 8 3, 9, 10

Physiotherapists 1, 2a, 4b, 5, 6, 7c, 8 3, 9, 10

Occupational therapists 1, 2, 4b, 5, 6, 7, 8, 9 3, 10

aThe part ‘Cognition, communication and behaviour problems in

interaction with the main health problems’ of this step is included

only in the Anadysis model; bThe part ‘Prevention, remediation,

restoration, adaptation and adjustment in treatment goals’ of this

step is included only in the Anadysis model; cThe part ‘The

complexity of physical, cognitive, communication and behavioural

problems’ of this step is included only in the Anadysis model.


Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

were collected from the medical records of 37

different patients.

All participants of both groups had already

obtained the necessary skills in order to keep records

regarding their assessments and interventions into

the medical notes of the patients. Thus, no specific

guidelines were given to the participants by the

experimenter regarding the writing style into the

medical notes. Therefore, the participants would

continue to fill in the medical notes as they used to,

considering the contents of each model as guidance

only. However, the experimenter occasionally re-

minded all participants of the guidelines provided in

each training course, disseminating in a written

format any relevant information referring to the steps

of any clinical reasoning model whenever it was

necessary. Finally, each participant informed the

researcher whether a patient was assessed using

either model – current or the Anadysis – by filling in a

form, which included the patient’s personal details

(i.e., name, address, date of birth) and his or her

hospital number.

Outcome measures

The effectiveness of any clinical reasoning model

would be assessed if measurable data for that clinical

reasoning process could be collected. In clinical

practice, clinical reasoning involves the diagnosis, the

planning and the treatment processes of patient

problem solving and management [26,48]. Conse-

quently, the effectiveness of either clinical reasoning

model was assessed by collecting data for diagnosis,

planning and treatment of the patients based on the

clinical information provided by all participants

within the medical records of these patients and

then by comparing this clinical information to the

UK National Clinical Guidelines (NCG) for Stroke

[49]. In fact, a transformed version of these guideline

statements as a protocol was structured in a yes/no

algorithm format [50] for ensuring the reliability of

data collection across all five clinical reasoning

models. Thus, data on the occurrence or non-

occurrence of the protocol components and sub-

components were collected. In total, 186 protocols

were filled in based on the medical records of 49

patients in both pre-test and post-test conditions.

Furthermore, inter-rater reliability was collected on a

random sample of 50% of all of the measurements.

Another healthcare professional acted as the second

rater in all cases who was naive to the experimental

conditions and to the assignment of the participants

in each group. Agreement was calculated by dividing

the number of agreements by the number of

agreements plus disagreements and then multiplying

the result by 100%. Total average reliability was

95% (range, 89 – 100%) across all of the protocol

components.

Results

Table II depicts the median percentages of the

correct responses in clinical reasoning for the control

and experimental groups across the four disciplines

in both the A&E and neurological units. For clarity,

‘correct responses’ equate with the occurrence of the

NCG for Stroke protocol components and sub-

components as specified in the above section. During

the pre-test condition and in the absence of any

training in clinical reasoning, minor differences

were noticed between the two groups across each

healthcare discipline in both professional statuses

Figure 2. A diagrammatic representation of the general procedure employed in the study.


Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

(i.e., A&E and neurological units). These baseline

data were important due to common difficulties in

randomization when a small sample (i.e., n¼ 12) is

employed, which indicated that the two groups

were, in fact, equivalent at the beginning of the

study. Then, in the post-test condition wherein the

four traditional and the Anadysis models were

introduced, median percentages of correct responses

in clinical reasoning were higher for the experimental

group. Specifically, the median percentages of

correct responses for the medical doctors of the

experimental group by using the Anadysis model

in the neurological units presented a difference of

16.6% in comparison to the control group, a

difference of 16.6% for the nurses, and 16.7% for

the physiotherapists, while the median percentage

for the occupational therapists remained the same for

both control and experimental groups. Similarly

in the A&E units, the median percentages of

correct responses were higher for the experimental

group. That is, a difference of 14.6% was

reported for the medical doctors of the experimental

group in contrast to the control group, a difference

of 12.5% for the nurses, 18.7% for the physiothera-

pists, and 10.5% for the occupational therapists,

respectively.

In particular, Table III presents the median

percentages of the correct responses in clinical

reasoning explicitly for the diagnosis, planning, and

treatment processes for both groups. In the pre-test

condition no significant differences were demon-

strated across each discipline for the two groups. In

the post-test condition, however, there was a general

tendency for the median percentages of correct

responses in diagnosis, planning, and treatment to

be higher when the Anadysis model was used by the

experimental group. That is, for the medical doctors

of the experimental group the median percentages of

correct responses for diagnosis, planning and treat-

ment processes were higher by 14.3%, 10% and

28.6%, respectively, as opposed to the median

percentages of the control group. Likewise, the

results for the nurses revealed that the median

percentages of correct responses for the diagnosis,

planning and treatment processes of the Anadysis

group were higher by 28.6%, 10%, and 14.3%,

respectively in comparison to the median percen-

tages of the control group. For the physiotherapists

similar results were obtained; the median percen-

tages of correct responses were higher for the

experimental group by 21.5% (diagnosis), 20%

(planning) and 14.3% (treatment). Finally, the

median percentages in diagnosis and planning for

the occupational therapists of the experimental group

were higher than those of the control group by 7.1%

and 5% whereas scoring was the same (i.e., 85.7%)

for the treatment process.

Tab

leII

.T

he

med

ian

per

cen

tages

of

the

corr

ect

resp

on

ses

incl

inic

alre

aso

nin

gfo

rth

eco

ntr

ol

and

exp

erim

enta

lgro

up

sac

ross

the

fou

rd

isci

plin

esin

bo

thth

eA

&E

and

neu

rolo

gic

alu

nit

s.

Pre

-tes

tP

ost

-tes

t

Nu

mb

ero

f

pro

toco

ls

Med

ian

(%)

of

corr

ect

resp

on

ses

for

the

con

tro

lgro

up

(ran

ge)

Nu

mb

ero

f

pro

toco

ls

Med

ian

(%)

of

corr

ect

resp

on

ses

for

the

exp

erim

enta

lgro

up

(ran

ge)

Nu

mb

ero

f

pro

toco

ls

Med

ian

(%)

of

corr

ect

resp

on

ses

for

the

con

tro

lgro

up

(IQ

R)

Nu

mb

ero

f

pro

toco

ls

Med

ian

(%)

of

corr

ect

resp

on

ses

for

the

exp

erim

enta

lgro

up

(IQ

R)

Med

ical

doc

tors

Neu

rou

nit

27

2.9

(70

.8–

75

)2

75

(75

–7

5)

77

5(7

5–

80

.1)

79

1.6

(86.4

–9

5.8

)

A&

Eu

nit

25

8.3

(58

.3–

58

.3)

26

0.4

(58.3

–6

2.5

)8

60

.4(5

4.1

–6

2.5

)8

75

(70.8

–7

9.1

)

Nurs

es

Neu

rou

nit

25

2(5

0–

54

.1)

25

0(5

0–

50

)8

50

(45

.8–

54

.1)

86

6.6

(58.3

–7

0.8

)

A&

Eu

nit

24

3.7

(41

.6–

45

.8)

24

3.7

(41.6

–4

5.8

)7

45

.8(3

7.5

–5

1)

75

8.3

(53

–6

2.5

)

Phys

ioth

erapis

ts

Neu

rou

nit

55

0(4

5.8

–58

.3)

44

7.9

(45.8

–5

4.1

)1

05

4.1

(50

–62

.5)

10

70

.8(6

0.4

–7

7)

A&

Eu

nit

34

1.6

(37

.5–

50

)2

43

.7(4

1.6

–4

5.8

)1

04

5.8

(37

.5–

50

)1

06

4.5

(56.2

–6

6.6

)

Occ

upati

onal

ther

apis

ts

Neu

rou

nit

57

0.8

(62

.5–

79

.1)

57

0.8

(62.5

–7

5)

10

75

(68

.7–

79

.1)

10

75

(70.8

–7

7)

A&

Eu

nit

36

2.5

(54

.1–

62

.5)

35

8.3

(58.3

–6

2.5

)1

06

2.4

(52

–70

.8)

10

72

.9(6

4.5

–7

7)


Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

Discussion

Traditional clinical reasoning models are limited to

support the new advances in information technol-

ogy and evidence-based practice within healthcare

[10,51]. Gaining a better understanding of clinical

reasoning processes has important benefits for

healthcare professionals and their employing organi-

sations [50,52]. Thus, a new integrated model (i.e.,

Anadysis) was developed and preliminarily assessed

by comparing its use to non-use across four different

healthcare disciplines. Data were obtained for the

three main parts of the clinical reasoning process –

diagnosis, planning, and treatment – and results

demonstrated that Anadysis could be an effective

model within the field of clinical reasoning. Im-

portantly, it was shown that healthcare professionals

could employ the same model irrespectively of their

discipline (i.e., medicine, nursing, physiotherapy,

occupational therapy) which might have influenced

the pattern of relationships among them and their

patients [53]. For example, an in-depth analysis of

anecdotal data showed that by using Anadysis,

medical doctors could communicate with patients

more efficiently, nurses and occupational therapists

used standardized protocols more consistently for

the patients’ assessment, and physiotherapists could

more often follow a holistic approach (e.g., assess

both motor and psychological, social or communica-

tion impairments) in stroke patients’ healthcare

management. Significantly, the effectiveness of this

clinical reasoning model was assessed by collecting

objectively measurable data based on the UK

National Clinical Guidelines for Stroke which was

further enhanced by conducting an inter-rater

reliability agreement.

Clinical reasoning is regarded as a complex task

that involves the identification and management of

patients’ health needs [54]. Hence, it was critical that

contemporary developments within healthcare were

carefully considered whilst Anadysis was being

developed. Firstly, predicted outcomes in conjunc-

tion with clinical judgement provide the basis for

selecting an appropriate therapeutic programme or

intervention and for evaluating the effectiveness of

interventions and programmes [1,55]. Secondly, the

need for scientific evidence regarding efficacy of

treatment methods becomes more evident than ever,

as principles of both patient-centred and evidence-

centred practice must govern the planning of any

treatment programme and the selection of the

specific interventions [56]. Thus, any clinical man-

agement plan should indicate the specific treatments

selected as well as their sequence, intensity, fre-

quency, and expected duration [57]. Thirdly, rou-

tine follow-up care after discharge should give

high priority to preventing further complications or

other injuries. Thus, outpatient evaluation and

training must be designed not only to monitor

progress towards rehabilitation goals, but also to

build skills, confidence, self-awareness and com-

pensation strategies in a variety of settings [58].

Table III. The mean percentages of the correct responses of the three part-processes in clinical reasoning across the control and experimental

groups for the four disciplines of healthcare professionals, in both the A&E and neurological units.

Pre-test Post-test

Median (%) of correct

responses across the

protocol components for

the control group

(range) (n¼24)




the experimental group

(range) (n¼22)




the control group

(IQR) (n¼ 70)



protocol components

for the experimental group

(IQR) (n¼70)

Medical doctors

Diagnosis 71.4 (57.1 – 85.7) 71.4 (57.1 – 85.7) 71.4 (57.1 – 85.7) 85.7 (71.4 – 89.2)

Planning 70 (60 – 80) 65 (60 – 70) 70 (57.5 – 72.5) 80 (80 – 90)

Treatment 57.1 (42.8 – 71.4) 64.2 (57.1 – 85.7) 57.1 (53.5 – 85.7) 85.7 (82.1 – 85.7)

Nurses

Diagnosis 42.8 (28.5 – 57.1) 42.8 (28.5 – 42.8) 42.8 (42.8 – 46.3) 71.4 (64.2 – 85.7)

Planning 50 (40 – 60) 50 (50 – 50) 50 (30 – 52.5) 60 (50 – 60)

Treatment 57.1 (28.5 – 71.4) 49.9 (42.8 – 71.4) 57.1 (42.8 – 60.6) 71.4 (57.1 – 85.7)

Physiotherapists

Diagnosis 57.1 (42.8 – 71.4) 57.1 (57.1 – 85.7) 64.2 (42.8 – 71.4) 85.7 (57.1 – 85.7)

Planning 40 (30 – 60) 40 (30 – 60) 40 (30 – 50) 60 (40 – 70)

Treatment 57.1 (57.1 – 71.4) 64.2 (57.1 – 85.7) 57.1 (57.1 – 71.4) 71.4 (57.1 – 85.7)

Occupational therapists

Diagnosis 71.4 (57.1 – 100) 71.4 (57.1 – 100) 71.4 (71.4 – 85.7) 78.5 (71.4 – 85.7)

Planning 50 (40 – 60) 50 (40 – 60) 55 (40 – 60) 60 (50 – 60)

Treatment 78.5 (57.1 – 100) 71.4 (57.1 – 100) 85.7 (71.4 – 100) 85.7 (71.4 – 100)


Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

Finally, even when the treatment goals have been

fully achieved, a patient’s progress should be

evaluated at regular intervals for any potential

remaining problems [e.g., 59]. As opposed to the

traditional clinical reasoning models, all the above

issues were explicitly addressed in specific steps of

this new integrated model.

The current study was limited to the information

provided in the medical records of the patients.

Therefore, it could be a case in which the healthcare

professionals carried out all the essential steps in a

clinical reasoning process without putting them in

writing. Although a real change in practice (e.g.,

influence of patient care and outcomes) was not

demonstrated, rather a change in ‘reporting’, the

results further indicated that the use of the Anadysis

clinical reasoning model supported the healthcare

professionals to manage changes (step 3), to explore

the patients’ problems thoroughly before considering

alternatives (steps 2a, 3 & 7c), or to involve the

patients and their families in the clinical reasoning

process (steps 4b & 9). However, evidence on how

this model can be used in a team or how it may affect

specific individual and team factors remains unclear.

In addition, the current study employed a small

sample size working in a general hospital, and

therefore, replication with additional healthcare

professionals in other more specialized settings

(e.g., stroke rehabilitation units) needs to be ad-

dressed in future studies. In such a setting, not only

the new model could be integrated along with

specialized models of rehabilitation (e.g., ICF,

ICIDH), but also the temporal aspects of this model

could be measured. This is important as it may be a

case that a tool will often not be used if it takes more

time, despite the fact that decisions might be more

accurate. However, it is worth noting that this

integrated model was developed as a clinical reason-

ing tool which was not meant to explicitly address the

rehabilitation of stroke patients.

It could also be argued that the selection of the

patients used for the study invoked some sort of bias.

However, the comparisons of data during baseline

with those during the experimental condition coun-

ter this suggestion. Furthermore, the scope of this

study was not directed to influence patient care

and outcomes; rather to ‘organize’ the current

available research evidence in relation to different

aspects of patient care. Thus, a random selection of

the patients to be included in a study wherein an

innovative tool was used might potentially cause

some ethical concerns.

It has been well documented that there is a

fundamental mismatch between the way healthcare

professionals manage information during clinical

reasoning and the way it is processed by computer-

ized clinical decision support systems [17,60 – 61].

Alongside some other current initiatives in the

development of decision-making tools (e.g., [62])

this study may also become an important considera-

tion in the further development of clinical decision

support systems [10 – 11]. Thus, with the use of a

comprehensive model of clinical reasoning, such as

Anadysis, useful and intuitive clinical decision sup-

port systems could be designed within the scientific

area of health informatics [7,63 – 64]. The effective

introduction of clinical decision support systems to

the task of clinical reasoning will help improve the

continuity and quality of patient care, promote

evidence-based clinical practice, develop health care

staff to manage information better in a world that is

expecting more ‘‘information empowered’’ profes-

sionals. [1 – 2,65 – 66].

Acknowledgements

Portions of this study were presented at the

14th World Federation of Occupational Therapists

Congress in Sydney, Australia, in July 2006.

The support of the Greek National Scholarship

Foundation – 2nd Division – Overseas Scholarships

Section following a successful participation of the

first author in a national written examination is

gratefully acknowledged. A special thanks to all

participants for their cooperation in conducting

this study.

References

1. O’Neill ES, Dluhy NM, Chin E. Modelling novice clinical

reasoning for a computerized decision support system. J Adv

Nurs 2005;49:68 – 77.

2. Norman G. Research in clinical history: Past, present and

current trends. Med Educ 2005;39:418 – 427.

3. Kalogeropoulos D, Carson ER, Collinson PO. Towards

knowledge-based systems in clinical practice: Development

of an integrated clinical information and knowledge manage-

ment support system. Comput Methods Programs Biomed

2003;72:65 – 80.

4. Thompson C, Dowding D. Clinical decision making and

judgement in nursing. Edinburgh: Churchill Livingstone;

2002.

5. Greenwood J. Theoretical approaches to the study of nurses’

clinical reasoning: Getting things clear. Contemp Nurse 1998;

7:110 – 116.

6. Buckingham CD, Adams A. Classifying clinical decision

making: A unifying approach. J Adv Nurs. 2000 Oct;32(4):

981 – 989.

7. Kushniruk AW. Analysis of complex decision-making

processes in health care: Cognitive approaches to health

informatics. J Biomed Inform 2001;34:365 – 376.

8. Hagedorn R. Foundations for practice in occupational

therapy. New York: Churchill Livingstone; 1997.

9. Mattingly C, Fleming MH. Clinical reasoning. Forms of

inquiry in a therapeutic practice. Philadelphia: FA Davis;

1994.

10. Arocha JF, Wang D, Patel VL. Identifying reasoning strate-

gies in medical decision making: A methodological guide.

J Biomed Inform 2005;38(2):154 – 171.


Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

11. Marckmann G. Recommendations for the ethical deve-

lopment and use of medical decision-support systems.

MedGenMed 2001;3:5.

12. Department of Health. The NHS plan: A plan for

investment, a plan for reform. London: The Stationary

Office; 2000.

13. Department of Health. A first class service: quality in the new

NHS. London: Department of Health; 1998.

14. National Health System Information Authority. Learning to

manage health information – A theme for clinical education:

Moving Ahead. 2002 – [cited 2005 Jun 05]. Available from:

http://www.nhsia.nhs.uk/nhid/pages/resource_informatics/ltm_

movingahead.pdf/

15. National Programme for Information Technology (NPfIT). A

guide to the National Programme for Information Technol-

ogy. 2005 Apr – [cited 2005 May 27]. Available from: http://

www.connectingforhealth.nhs.uk/publications/brochures/npfit_

brochure_apr_05_final.pdf/

16. Jones MA, Dalton M. Clinical reasoning for manual

therapists. Edinburgh: Butterworth-Heinemann; 2004.

17. Garner R, Rugg S. Electronic health records: an update on the

Garner project. Br J Occup Ther 2005;68(3):131 – 134.

18. Seale J, Barnard S. Therapy research: Processes and

practicalities. Oxford: Butterworth Heinemann; 1998.

19. Norman GP, van der Vleuten CPW, Newble D, editors. The

International Handbook of Research in Medical Education.

London: Kluwer Academic Publishers; 2002.

20. Haynes B, Haines A. Getting research findings into practice:

Barriers and bridges to evidence based clinical practice. BMJ

1998;317:273 – 276.

21. McAlister FA, Straus SE, Guyatt GH, Haynes RB. Users’

guides to the medical literature: XX. I. Integrating research

evidence with the care of the individual patient. JAMA

2000;283:2829 – 2836.

22. Eddy DM. Clinical decision making: From theory to practice.

A collection of essays from the journal of the American

Medical Association. London: Jones and Bartlett Publishers;

1996.

23. Fortier P, Michel H, Sarangarajan B, Dluchy N, O’Neill E.

A computerized decision support aid for critical care novice

nurses. International Conference held at the 38th Hawaii

International Conference on System Sciences; 2005 Jan 3 – 6;

Waikoloa, Hawai. Hawai: IEEE; 2005. p 141a.

24. Muir N. Clinical decision-making: Theory and practice. Nurs

Stand 2004;18(36):47 – 52.

25. Simmons B, Lanuza D, Fonteyn M, Hicks F, Holm K.

Clinical reasoning in experienced nurses. West J Nurs Res

2003;25(6):701 – 719.

26. Wilkinson J. Nursing process in action. Redwood City, CA:

Addison-Wesley Nursing; 1992.

27. Higgs J, Jones M. Clinical reasoning in the health professions.

2nd ed. Oxford: Butterworth Heinemann; 2000.

28. Jones MA. Clinical reasoning process in manipulative therapy.

In: Boyling JD, Palastanga N, editors. Modern manual

therapy, the vertebral column. 2nd ed. London: Churchill

Livingstone; 1994. pp 471 – 489.

29. Higgs J. Physiotherapy, professionalism and self-directed

learning. Singapore Physiother 1993;14:8 – 11.

30. Jones MA, Christensen N, Carr J. Clinical reasoning in upper

quadrant dysfunction, In: Grant R, editor. Physical therapy

for the cervical and thoracic spine. 2nd ed. New York:

Churchill Livingstone; 1994. pp 89 – 108.

31. Shepard KF, Hack LM, Gwyer J, Jensen GM. Describing

expert practice in physical therapy. Qual Health Res

1999;9:746 – 758.

32. Edwards I, Jones M, Carr J, Braunack-Mayer A, Jensen GM.

Clinical reasoning strategies in physical therapy. Phys Ther

2004;84:312 – 330.

33. Rothstein JM, Echternach JL. Hypothesis-oriented algorithm

for clinicians. A method for evaluation and treatment plann-

ing. Phys Ther 1986;66:1388 – 1394.

34. Jones MA. Clinical reasoning in manual therapy. Phys Ther

1992;72:875 – 884.

35. Robertson L. Clinical reasoning, part 1: The nature of

problem solving, a literature review. Br J Occup Ther

1996;59(4):178 – 182.

36. Schell BB. Clinical reasoning: The basis of practice. In:

Neistadt M, Crepeau EB, editors. Occupational Therapy.

9th ed. New York: Lippincott; 1998. pp 90 – 100.

37. Unsworth CA. Clinical reasoning: how do worldview, prag-

matic reasoning and client-centredness fit? Br J Occup Ther

2004;67(1):10 – 19.

38. Unsworth CA. Clinical reasoning during community health

home visits: Expert and novice differences. Br J Occup Ther

2005;68(5):215 – 223.

39. Mattingly C, Fleming MH. Clinical reasoning. Forms of inquiry

in a therapeutic practice. Philadelphia: FA Davis; 1994.

40. Fleming MH. Clinical reasoning in medicine compared with

clinical reasoning in occupational therapy. Am J Occup Ther

1991;45:988 – 996.

41. Rogers JC. Eleanor Clarke Slagle Lectureship – 1983; clinical

reasoning: the ethics, science, and art. Am J Occup Ther

1983;37:601 – 616.

42. Hopkins HL. Problem solving. In: Hopkins HL, Smith HD

editors. Willard and Spackman’s occupational therapy. 8th ed.

Philadelphia: JB Lippincott; 1993. pp 292 – 294.

43. Pedretti LW. Occupational therapy. Practice skills for physical

dysfunction. 4th ed. New York: Mosby; 1996.

44. Jadad AR. Randomised controlled trials: A user’s guide.

London: BMJ Publishing Group; 1998.

45. Polgar S, Thomas SA. Introduction to research in the health

sciences. London: Churchill Livingstone; 2000.

46. Ferreira-Borges C. Effectiveness of a brief counseling and

behavioral intervention for smoking cessation in pregnant

women. Preventive Med 2005;41(1):295 – 302.

47. Sommer B, Sommer R, editors. A practical guide to

behavioral research: Tools and techniques. New York: Oxford

University Press; 1997.

48. Stefanelli M. European research efforts in medical knowledge-

based systems. Artif Intell Med 1993;5:107 – 124.

49. Intercollegiate Working Party for Stroke. National clinical

guidelines for stroke. 2nd ed. London: Royal College of

Physicians; 2002.

50. Sivakumar A, Haigh A. Implementing evidence-based

practice (or best possible practice) through protocols in an

Accident and Emergency Department in Brent & Harrow. In:

Evans D, Haines A, editors. Implementing evidence-based

changes in healthcare. Oxon, UK: Radcliffe Medical Press;

2000. pp 39 – 64.

51. Montani S, Bellazzi R. Supporting decisions in medical

applications: The knowledge management perspective. Int J

Med Inform 2002;68:79 – 90.

52. Hajdukiewicz JR, Vicente KJ, Doyle DJ, Milgram P,

Burns CM. Modeling a medical environment: An ontology

for integrated medical informatics design. Int J Med Inform

2001;62:79 – 99.

53. Aydin C, Rice RE. Bringing social worlds together: Systems as

catalyst for interdepartmental interactions. J Health Soc Behav

1992;33:168 – 185.

54. O’Neill ES, Dluhy NM, Fortier PJ, Michel H. The

N-CODES project: The first year. Comput Inform Nurs

2004;22(6):345 – 350.

55. Gowland C. Predicting physical outcomes in stroke: Implica-

tions for clinical decision making. International Conference

held at the Sixth Annual Stroke Rehabilitation Conference;

1994 Sep; Cambridge, MA.


Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

56. Higgs J, Alastair B, Mark J. Integrating clinical reasoning

and evidence-based practice. AACN Clin Issues 2001;12(4):

482 – 490.

57. Geyman JP. Evidence-based medicine in primary care: An

overview. J Am Board Fam Pract 1998;11:46 – 56.

58. Greenberg E, Treger J, Ring H. Post-stroke follow-up in a

rehabilitation center outpatient clinic. Isr Med Assoc J 2004;

6(10):603 – 606.

59. Jette DU, Grover L, Keck CP. A qualitative study of clinical

decision making in recommending discharge placement from

the acute care setting. Phys Ther 2003; 83(3):224 – 236.

60. Wetter T. Lessons learnt from bringing knowledge-based

decision support into routine use. Artif Intell Med 2002;

24(3):195 – 203.

61. Kalogeropoulos D, Carson ER, Collinson PO. Towards

knowledge-based systems in clinical practice: Development

of an integrated clinical information and knowledge manage-

ment support system. Comput Methods Programs Biomed

2003;72:65 – 80.

62. Seedhouse D. Ethics: the heart of health care. 2nd ed.

Chichester: John Wiley & Sons; 1998.

63. Patel VL, Kushniruk AW, Yang S, Yale JF. Impact of

computer-based patient record system on data collection,

knowledge organization, and reasoning. J Am Med Inform

Assn 2000;7:596 – 585.

64. Kaplan B. Evaluating informatics applications – some alter-

native approaches: theory, social interactionism, and call for

methodological pluralism. Int J Med Inform 2001;64:39 – 56.

65. Owens DK. Use of medical informatics to implement and

develop practice guidelines. West J Med 1998;168:166 – 175.

66. Caelli K, Downie J, Caelli T. Towards a decision support

system for health promotion in nursing. J Adv Nurs

2003;43:170 – 180.


Dis

abil

Reh

abil

Dow

nloa

ded

from

info

rmah

ealth

care

.com

by

ThU

LB

Jen

a on

11/

12/1

4Fo

r pe

rson

al u

se o

nly.

a new integrated model of clinical reasoning: development, description and preliminary assessment in...

Documents