=Paper=
{{Paper
|id=Vol-1182/paper6
|storemode=property
|title=Enhancing Flexibility in Clinical Trials using Workflow Management Systems
|pdfUrl=https://ceur-ws.org/Vol-1182/paper6.pdf
|volume=Vol-1182
}}
==Enhancing Flexibility in Clinical Trials using Workflow Management Systems==
https://ceur-ws.org/Vol-1182/paper6.pdf
ENHANCING FLEXIBILITY IN CLINICAL TRIALS
USING WORKFLOW MANAGEMENT SYSTEMS
Albert Murungi, Josephine Nabukenya
Information Systems Department, School of Computing and Informatics Technology
Makerere University Kampala
Kampala, Uganda
murucomus@gmail.com, josephine@cit.ac.ug
Abstract . In the medical environment, clinical research has been predomi-
nantly paper-based. The Case Report Form (CRF) is an example of paper-based
tools used in documenting clinical trials. A clinical trial can be understood to be
a business process. This also means that automating the clinical trial can be re-
ferred to as a workflow process. In this research, we seek to automate the clini-
cal trial in order to support the medical practitioners to ably translate their natu-
ral language format including CRFs using a workflow system. However we al-
so address the need for flexibility in workflow models in the clinical trials that
operate under specific guidelines in form of protocols. To attain the required
level of flexibility in clinical trial workflow models, we use the constraint-based
modeling language. The results from the validation indicate that the designed
workflow modeling framework meets the required level of flexibility with re-
spect to its usefulness and usability.
Keywords: workflow modeling, clinical trials, process models, flexibility
1 Introduction
The past decade has seen business process modeling become an important means in
business process reorganization and management projects [1]. This is because model-
ing offers a way to capture the implicit process knowledge of an organization and also
documents it explicitly in a formal or semi-formal way. However, despite the evolu-
tion of business process modeling, highly trained advisors with sufficient domain
expertise are still required to perform evaluations of the different business process
models [1]. In the same respect, [12] also observes that business process design tasks
are still being done by specialized engineers. This alone is a hindrance to the general
adoption of business process modeling by other domain knowledge practitioners. This
research seeks to resolve the concern of enabling knowledge workers such as medical
practitioners to easily translate the volatile and ever dynamic domain specific business
processes without entirely changing the existing process and workflow models.
Research on business process application has been targeted at application domain
areas such as the banking sector [4], and engineering [12], among others. Substantial
research has also been carried out in medical informatics to address some of the busi-
ness processes within the medical field such as clinical trial (CT) protocol writing
[17], computational model-based decision support tools [9, 22], and the use of mark-
up languages to transfer existing free text clinical protocols into a computer-
interpretable format [20, 19]. CT processes offer a prime area of study for constantly
changing and evolving business processes (BP) since they are no exception to unpre-
dicted situations. However, amongst all these processes, flexibility was key aspect
and this research has looked at ways of enhancing the same within key business pro-
cesses. Process flexibility needs to be incorporated into the business process modeling
efforts for the CTs mainly for two reasons; first, to enhance exception handling as
well as take care of both planned and unforeseen changes. Secondly, to continually
identify process related tasks for the respective business so as to identify improve-
ment gaps. Through process flexibility, we steadily aim to ensure that the designed
workflow models achieve both stability and enhance efficiency in clinical practice.
Whereas process flexibility can be interpreted as the ability to deal with both fore-
seen and unforeseen changes, by varying or adapting specific parts of the business
process that are affected by them, whilst retaining the essential format of those parts
that are not impacted by the variations [15], in this research context flexibility is as
much about what should stay the same in a process as what should be allowed to
change [21].
To this end, this research seeks to address how best to enhance flexibility in work-
flow models using an example of CTs. The perceived benefits of this transition in-
clude reduced cost and time, enhancement of flexibility, as well as improved support
for design-time and run-time process modeling activities in workflow modeling and
particularly in CTs. We comprehensively discuss the concept of flexibility in relation
to the CT process in section 2. To address our research question, we first identified
the process-related tasks in CTs, from which we derived the requirements that were
finally translated into workflow models as presented and discussed in sections 3, 4
and 5, respectively. Section 6 provides the case study conducted to test the proposed
guidelines, including validating the usability and usefulness of the designed models.
Finally we provide directions to future research in section 7.
2 CLINICAL TRIALS AND WORKFLOW MODELING
Amongst the major areas of conduct within CTs is the collection of clinical data
from study subjects by physicians, nurses, and any other appropriately trained medical
personnel. [12, 8] also emphasizes the fact that, “one of the basic goals of CTs is to
obtain data for the safety and/or effectiveness of a drug or a device in order to get that
drug or device to market”.
To achieve the above, suitable workflow languages need to be used in designing a
workflow system that can be used to address the domain specific tasks. For example
[5] have used Business Process Modeling Notation (BPMN) to model some of the CT
phases as models. Furthermore, there is a number of existing approaches to clinical
guidelines modeling in form of computer interpretable guidelines (CIGs) such as
GuideLine Interchange Format (GLIF), EON, PRODIGY, PROforma amongst others
[25, 22, 26]. Despite the benefits of these languages including reduced time and cost of
service delivery, they are still unable to address key concerns such as flexibility in CT
BPs within the area of healthcare provision. [23] observes that using a model-driven
approach facilitates a high degree of flexibility whereby process models can be adapted
to fulfill new requirements. The modified process models can then be used to enact the
emergent BPs.
2.1 Flexibility within Clinical Trial process Models
Flexibility – involves dealing with both foreseen as well as unforeseen changes to
processes and the ability to respond to these changes without necessarily having to
change the entire process model [11]. Flexibility is observed as what stays the same not
what changes. Indeed, a process can only be considered to be flexible if it is possible to
change it without needing to replace it completely [19]. Hence flexibility is effectively
a balance between change and stability that ensures that the identity of the process is
retained [11]. In the context of this research flexibility is considered as the ability to
respond to emerging changes without necessarily changing an entire process model
with the aim of ensuring stable process models despite the dynamics in the operating
environment.
Generally, we observe that flexibility is significantly critical because the health
domain and specifically CTs do operate in a fluid environment although still being
guided by well defined protocols. There are a number of foreseen as well as unforeseen
changes within the CT processes. This would necessitate the need to vary or adapt
those parts of the BPs that are affected by the changes, whilst retaining the essential
format of those parts that are not impacted by the variations.
Up to now, there have not been any broadly adopted proposals or standards offer-
ing guidance for developing flexible process models able to deal with the different
changes experienced within processes. Instead most standards focus on a particular
notation (such as XPDL, BPEL, BPMN, among others) and these notations typically
abstract from flexibility issues [22]. It is imperative to note that the approaches and
mechanisms of implementing flexibility can significantly impact the way workflows
are designed. Also, the complexities involved in having to explicitly specify how pro-
cesses flow and interact with each other needs to be minimized, and hence making it
much simpler to design and enact workflow models. Therefore, we aim to simplify the
way modeling is done especially by the knowledge workers with limited expertise in
working with complicated modeling languages taking into consideration the need to
address the identified flexibility concerns.
2.2 Enhancing Flexibility in Workflow Modeling using the Declarative
Language
Workflow modeling mainly follows two categories of languages: procedural and
constraint-based modeling languages respectively. The traditional approaches tend to
use procedural process models (also known as classical modeling languages) to ex-
plicitly specify the execution procedure. Examples are BPEL, YAWL, UML, amongst
others [28, 29, 39]. On the other hand, constraint-based models implicitly specify the
execution procedure by means of constraints, that is, any execution that does not vio-
late constraints is possible [38, 20]. Examples among others include Logical Tem-
poral Language (LTL), and ConDec [30, 1, 31]. Constraint-based models are consid-
ered to be more flexible than traditional models because of their semantics [20, 32].
As such, a new paradigm shift towards a declarative approach introduces declarative
models that specify what should be done without specifying how it should be done
[19, 31, 1]. The declarative approach is based on constraints, that is to say, anything is
possible as long as it is not explicitly forbidden. The Declare framework [33] that
provides for multiple constraint-based languages is currently implemented and has
been adopted for use in this research. Declare is extendible and without any pro-
gramming it can be configured to support additional constraint-based languages.
Furthermore, instead of explicitly defining the ordering of activities in models, De-
clare models rely on constraints to implicitly determine the possible ordering of activ-
ities, implying that any order that does not violate constraints is allowed [33, 38]. By
using such a declarative approach, different types of flexibility become possible [28,
33]. The core of the declare workflow management system consists of the following
basic components: Designer, Framework and Work list. The Designer component is
used for creating the so-called constraint templates, to design concrete process mod-
els, and to verify these models. The Framework enacts instances of process models.
Moreover, it also conducts ad-hoc changes of running instances. While the Frame-
work centrally manages the execution of all instances, each user uses his/her Work
list component to access active instances.
From the preceding discussion, we observe that the declarative modeling approach
presents the opportunity to address the flexibility concerns. The underlying con-
straints provide the general boundaries around which work is executed. Furthermore,
the defined constraints can be declared either as mandatory or optional implying that
if an activity breaches a mandatory constraint then all subsequent activities in the
workflow cannot be executed. For example, in a CT, assuming the protocol states a
specific exclusion criterion for participants, some of the conditions within the exclu-
sion criteria could be tagged as either mandatory or optional. Therefore, for as long as
a person presents any of the conditions that fall within the mandatory ones, they are
automatically excluded from participating in the study. With a declarative modeling
approach, this would be fairly simple to achieve as compared to when using a proce-
dural approach that might cause over specification before achieving a similar result.
3 Research Approach
In order to design the framework for BPs workflow modeling, the design science
(DS) method was followed. Design Science (DS) aims at producing artefacts that con-
tribute to the body of knowledge and are relevant to the community [37, 40]. Design
science also aims at developing methods and an instance of a system for a given set of
user requirements represented as models [40]. Design science therefore seeks to devel-
op new ideas, practices, technical capabilities and products that will enable users to
analyse, design, implement and use information systems effectively and efficiently. It
also aims at developing methods and an instance of a system for a given set of user
requirements represented as models. Such methods are known as theories that pre-
scribe how to effectively develop an information system [40]. This method consists of
three cycles namely, the relevance cycle, the design cycle and the rigor cycle [13, 14,
15, 40].
The ‘Relevance Cycle’ deals with identification a problem or an opportunity in a
given application domain. The business environment is explored to determine the busi-
ness needs, an input to the design cycle, as well as to define an acceptance criteria for
testing produced artefacts [40, 41]. In the relevance cycle, we adopted the CT envi-
ronment and identified processes that can be used to model workflows while factoring
in the aspects of both flexibility and support. The designed models were again put back
into the CT environment and subjected to test usability and usefulness within the CT
community.
The ‘Rigor Cycle’, involves thorough review of past knowledge in terms of foun-
dations and methodologies to identify applicable knowledge; knowledge that can be
applied in solving a given problem. It also ensures that the artefacts produced by the
research are innovative and add to the existing knowledge base. The Rigor cycle also
provides input to the design cycle in terms of appropriate theories and methods for
construction and evaluation of artefacts [41, 40]. The rigor cycle involved an extensive
literature review on the current states of practice in the CT environment and its rela-
tionship with flexibility in workflow modeling.
The ‘Design Cycle’ is the core of design science. It involves the building and eval-
uating of artefacts following a set of guidelines defined by Hevner et al. [40]. The
artefacts produced by design science are: constructs: which are concepts or a ‘lan-
guage’ (e.g. modelling primitives used in modelling tools) that are used to define and
communicate problems and solutions; models: these are descriptions of a real world
problem and possible solutions using constructs e.g. process models; methods: these
define ‘processes’ that is ‘ways’ of performing a set of activities to achieve a given
goal; and instantiations: these are working systems that show that the constructs, mod-
els or methods can actually be implemented [40, 37]. In the design cycle, evaluations
were carried out using a suitable mix of metrics related to flexibility. We also identi-
fied modeling requirements specific to clinical trials and then used these requirements
to inform the design of suitable workflow modelling guidelines as well as a modelling
framework. We chose the DS method because it provides a well-defined approach to
establish ways of enhancing flexibility within workflow models through a structured
and iterative process that is self checking through the design cycle. Additionally DS
has been successfully used in other related researches [21, 24].
A workflow modeling framework in form of a set of guidelines was designed to
guide the modeling process. A case study was conducted to test the proposed guide-
lines, in addition to validating the designed workflow models using a number of per-
sonnel involved within CTs including doctors, data clerks, and a data manager. The
sampled category was representative of the key functional roles within the CT process
and hence had expert knowledge on the processes they are involved with during the
CT. The participants were chosen randomly except for cases where there was only a
single person in the role and a total of 15 participants were identified for the study.
4 Exploratory Study on CT Business Process Environment
In order to design our workflow modeling framework, an exploratory study was
conducted in the CT application domain using a Ugandan Health Institution as the case
study. A random sample of 15 respondents comprising of different categories of
knowledge workers such as data technicians, medical workers, and nurses among oth-
ers were contacted with the intention of identifying the process related tasks involved
within CTs, the nature of the BPs and how that impacted the way these knowledge
workers conducted their work, the challenges faced by the current BPs and how some
of the identified challenges could be improved. Questionnaires were applied and re-
sponses collected, analysed and presented. Below we discuss our findings and derived
requirements:
• Identification of process related tasks in CTs – it was observed that CTs do
involve a number of different BPs, tasks and activities with some occurring more fre-
quently than others. Some of the tasks identified included data collection, data analysis,
diagnosis, prescription, referrals, and patient selection, amongst others. However, the
occurrence of these process-related tasks is entirely dependent on the nature of the CT
being conducted. The data management process was one of the commonly occurring
BPs. The BPs are composed of tasks and the conditions that determine the order of
these tasks.
• Nature of BPs within CTs – the case study results did indicate a high percent-
age of manual tasks (73%) as compared to the combined automated tasks (27%) within
the CTs. This means that commensurately, the BPs associated with these tasks were
also of a manual nature. This alone is a challenge because of the associated demerits
that come along with having manual processes related to more time, cost and effort
required to achieve business goals.
4.1 Challenges faced during CTs process related tasks execution:
i. Flexibility of performing tasks within BPs – there exists a very low level of flexi-
bility (47%) in performing tasks within CT BPs, as compared to 20% of respondents
who felt that the BPs were flexible. However, 33% of the respondents were not sure
about the flexibility levels within the BPs.
ii. Ease of designing processes for CTs – it was observed that it was not easy
(57%) for knowledge workers to design new BPs as compared to 10% of ease.
iii. Support within clinical trial BPs – the study revealed a low level (40%) of
support within CTs. Based on a high-medium-low index; the results indicated a
33%-27%-40% score respectively. The respondents did attest that there is limited
support both at build time and runtime offered to the users. Within the current CT
BPs there is limited support for verification, performance analysis, and monitoring
of the BPs; therefore it is not easy to enforce correct execution of activities, learn
from processes, monitor process instances, and enforce correct changes.
iv. Reusability of current business processes – the findings also show that reusa-
bility of BPs within CTs was low (27%). However 40% indicated otherwise, and
33% were not sure. This is partly explained by the fact that the BPs and their related
tasks are mainly manual, inflexible, and with limited support as already observed
above. These factors compounded together make it extremely hard for the
knowledge workers to reuse some of the existing BPs.
v. Ease of modeling workflows for current BPs – the results also reflected a high
percentage (67%) for inability to easily model workflows in CTs. 27% were not
sure, while only 6% did confer to ease of modeling workflows for BPs within CTs.
The results above do provide an insight into the existent problem within CTs. The
results do also reveal that it is not easy for knowledge workers to design workflow
models for their BPs. In the following section we discuss the requirements used in
designing the workflow modeling framework. The requirements were derived from the
preceding challenges.
4.2 Requirements for the Proposed Workflow Modeling Framework
Based on the findings of the exploratory study, we derived four key requirements
for the workflow modeling framework. The requirements identified were primarily as
a direct result of the responses received pertaining to the challenges faced during exe-
cution of CT process related tasks. The requirements include the following:
Flexibility enhancement - the ability for knowledge workers to easily make chang-
es without having to entirely change the parts of the BPs that are not impacted by
these changes.
Support features provision - the ability to provide sufficient support tools and
mechanisms to detect and mitigate issues that affect performance of workflow
models.
Reusability of process instances - the ability for knowledge workers to make use of
existing CT process instances without need to completely redesign them.
Interpretability of workflow models by workflow engines - the ability of workflow
management systems to correctly execute the designed workflow models.
5 Designing of the Workflow Modeling Framework
A workflow modeling framework is an essential aspect of this research because it
attempts to address the challenges that were identified in the exploratory study dis-
cussed in section 4 above. The framework offers a graphical schematic in form of a
flow chart that represents the steps required to be followed by knowledge workers
during workflow modeling. With the four key requirements identified above, the
framework offers an integrated but easily usable approach for workflow modeling.
The framework (figure 1) consists of ten key steps that need to be performed by
knowledge workers in order to design their domain specific workflow models. The
workflow modeling framework hence offers a graphical guideline to workflow mod-
eling that can be used by these knowledge workers. The framework provides both a
set of guidelines as well as a way of achieving flexibility within designed workflow
models. The framework empowers knowledge workers to easily model their business
processes based on the provided guidelines. By following the steps (1-10) in the
framework in figure 1 and using a modeling tool such as Declare, fragments of flexi-
bility are used to fill the pockets of flexibility [36]. Pockets of flexibility can be de-
fined as the different process fragments that would ideally constitute a workflow but
are not executed until runtime when required. A special workflow activity called the
build activity provides the rules for concretizing the pocket with a valid composition
of workflow fragments [36]. The build activity is embedded within the Declare tool
and is basically an abstraction layer of meta-models composed of various rules for
selecting flexibility fragments from amongst the available options. It is also this meta-
modeling concept that ensures reusability of the workflow models. The concept of
meta-models is discussed in more detail by [3, 34, 35] amongst others. The steps in
the framework (see figure 1) are explained in more detail here below;
Step 1: Protocol Analysis to identify BPs – Clinical trials usually follow a protocol
that defines what needs to be done in order to achieve the goals of the study. Through
analysis of the protocols, the different business processes can be identified.
Step 2: Identify the tasks/activities associated with each of the processes – There
are some logically related activities associated with each of the identified processes,
together with the conditions that guide their order of execution. The conditions form
the constraints amongst the tasks. Once the processes have been identified, task iden-
tification for each of the processes drills a level deeper into the process to identify
what activities constitute the process.
Step 3: Choose a modeling language, for example ConDec, DecSer Flow – Using
the declare tool, a modeling language of choice is selected and used to design the
workflow model. The modeling is done using the graphical user interface (GUI) of
the declare tool. The declare GUI provides an abstraction to the complex LTL which
simplifies the task of both process modelling as well workflow modelling. This will
eventually be used to translate the natural language text used within the protocols into
automated workflow models.
Step 4: Specify the process model using the language – The process model will
provide a representation of a collection of logically related activities that are per-
formed to achieve a given clinical trial process outcome such as data analysis, or data
verification, etc. The chosen modeling language is used to specify both the process
and workflow models.
Step 5: Define the constraints – As discussed earlier in section 2, the nature of de-
clarative modelling is such that any activity can be performed as long as it does not
violate the set constraints. Mandatory or optional constraints can be defined to ensure
that we are able to achieve the business requirements. Mandatory constraints are con-
straints that cannot be violated and provide a boundary within which the model oper-
ates. In the context of this research the constraints can also include semantical con-
straints that are guided by expert advice from the medical practitioners. These may
also include certain government policies and regulations as well as other regulatory
requirements. Optional constraints are constraints that will only provide a warning if
violated but will not stop the model from executing. In the context of this research
optional constraints may include for example using a different substitute drug from
the prescribed one in cases of drug shortages.
Step 6: Error Detection, for example performs semantical verification or syntacti-
cal verification – The declare tool has got embedded features that can be used to per-
form model verification. During the verification, we can determine whether the de-
signed model has errors with the defined constraints or the control flow of the model.
Step 7: Address any identified gaps - Error correction – In order to correct the
identified gaps within designed models and basing on error messages displayed by the
declare tool, rightful steps are taken to resolve these gaps and errors either by amend-
ing the applied constraints or re-modeling the entire process again. Error correction is
necessary to ensure that the designed workflow models achieve their intended pur-
pose.
Step 8: Test and validate process model – Select suitable metrics to validate the de-
signed workflow model. Also, validation criteria can be agreed upon from which
different metrics can be identified from time to time. Validation ensures that the de-
signed workflow model is fit for purpose and can be executed by the workflow en-
gines.
Step 9: Enact the model – The designed model at this stage is ready to be executed.
This can also be referred to as run time phase of the workflow model. At runtime the
designed models should perform the work that they are designed to do. Runtime is
achieved by executing the loaded workflow models using the Work list component of
the declare tool. Each medical practitioner can access the work list portal using their
own individual credentials for purposes of privacy. Also, based on the assigned per-
missions, access to different workflow models can be restricted.
Step 10: Monitor processes – This is an ongoing process that constantly checks the
processes to ensure that they are still relevant and accurate. It is during this step that
we monitor to see if changes can be introduced into the existing models and the im-
pact of these changes to the models. The monitoring process may also include review-
ing of process logs which can subsequently aid process mining using process mining
tools.
Identify Workflow Modeling Requirements
Natural Language
Workflow Modeling Framework Text
Step 9: Enact
model
Support features
Step 2: Declarative modelling such as
Step 1: Identify Step 3: approach (e.g declare) verification and
Protocol Choose Step 8: test performance
Process and validate
Analysis Modeling analysis
related workflow
tasks language
model Flexibility
Ease of use
Step 4:
Process Reusable
Step 7: Pockets of
model process
Error/gap flexibility
models
closure
Step 5:
Constraints Step 6: Error
identification Workflow
models
Step 10: Monitor processes
Workflow
Engines
Fig. 1. Workflow modeling framework
5.1 Case study: Modeling the CT Natural Language Text to a workflow model
In figure 2, we demonstrate a piece of natural language text modelled to a work-
flow model of the same process based on the hematologic tests activity of a CT pro-
cess. The hematologic tests activity is composed of several activities that are conduct-
ed during the execution of the CTs. Some of the activities within this extract are made
of sub-processes where the actual execution occurs. The process includes activities to
determine eligibility into the CT, acquiring consent, enrolment of subjects, study drug
dosage information, survey activities about the patients including height, weight,
blood pressure levels, x-ray results, as well as test results including urinalysis, and
blood tests amongst others.
Using the graphical user interface of the Declare tool, the activities mentioned
above are modeled and subsequently both mandatory and optional constraints are
applied to the resulting model. After modeling the process, it then becomes the core
process (open instance). All subsequent process instances are then based on this core
process model. Only the process fragments required for a given process instance are
activated at runtime. As these fragments are added, they form pockets of flexibility
and it is through these that flexibility is achieved. However, because of the nature of
constraint-based modeling, all activities are modeled in no specific order and it is the
constraints that determine the ordering of activities. This implies that it is possible to
run the activities in any order as long as the constraints are not violated and it is for
this reason that modeled activities appear to be simply scattered on the modeling in-
terface in no particular order. We verify the designed workflow model using the in-
built feature within the Declare tool. In case of errors in the designed model, these are
flagged by the verification module within the Declare tool.
Fig. 2. A sample workflow model based on the hematologic tests process of CT process
5.2 Demonstrating flexibility in a workflow model using the declarative
modeling approach
Core Process – Open instance of a sample CT procedure; the core process can also
be called an open instance because it represents a generic process model from which
multiple instances can be derived from. It consists of pre-defined workflow activities
and pockets of flexibility within the process. The pockets of flexibility have an asso-
ciated set of workflow fragments (a workflow pack may consist of a single activity or
a sub-process) and a special workflow activity called the build activity that provides
the rules for concretizing the pocket with a valid composition of workflow fragments.
To demonstrate flexibility in the workflow models, a generic diagnostic process
model derived from the ‘hematologic tests’ activity is presented and thereafter used to
guide the modeling of three other subsequent process instances. The tasks in the ge-
neric process included receive patient, create file, retrieve file, examine patient, build
activities (blood test, ultra sound, and second opinion activities), make diagnosis, and
receive payment was used.
In each of the derived process instances, different pockets of flexibility are intro-
duced to illustrate flexibility in the process, that is, the degree of stability in the pro-
cess even as changes are introduced. We observed that a certain number of the core
process activities remained stable even as the instances changed (see fig. 4, 5 and 6).
The specification of instances initially consists of a copy of the core process (see fig-
ure 3). As an instance proceeds with execution, the build activities provide the means
of customizing the core process for that particular instance.
Fig. 3. Open instance of the diagnosis process
In figure 4, we illustrate an example of a flexible workflow representing a typical
diagnostic process for investigation of HIV. A new patient coming into the health
centre will first be entered into the system, or the file of an old patient will be re-
trieved. All patients then consult with an attending physician, who will determine
what tests need to be performed, on a case-by-case basis. This forms the pocket of
flexibility for the process. After the tests, the patient is called again by the attending
physician who explains the results of the tests and makes a diagnosis. The patient is
then required to report to accounts to make the required payments before leaving the
health centre.
Process instance 1 - in figure 4 for instance, the ‘chain response’, ‘precedence’, and
‘choice’ constraints help coordinate the execution of the tasks in that process instance
at runtime. Also, in this instance six tasks (‘receive patient’, ‘create file’, ‘retrieve
file’, ‘examine patient’, ‘make diagnosis’, and ‘receive payment’) remain unchanged
and it is only the ‘blood test’ task that is activated from the available build activities.
Nevertheless, given the fact that template building is progressive, the core process
may contain several pockets of flexibility which will be concretized through the asso-
ciated build activities as they are encountered at runtime. As such, the template re-
mains open until the last build activity has completed execution.
Fig. 4. Process Instance 1
Process Instance 2 - In fig. 5 only the build activities required for this particular in-
stance are selected and executed at run time excluding any other process fragments
that are not relevant and thus ensuring stability of the core process model. In this in-
stance, only activities ‘ultrasound’ and ‘second opinion’ are activated within the build
activity amongst all the other available options. Also, in this instance the ‘succession’
constraint introduced between the two active build activities will determine the order
in which they are executed. Activity ‘second opinion’ will only happen after activity
‘ultra sound’ has occurred. There is therefore no need to remodel the entire process
but rather to only include the process fragment relevant for a specific instance as indi-
cated below. The build activity is critical since it provides a set of fragments to
choose from either automatically or manually.
Fig. 5. Process Instance 2
Process Instance 3 – In this instance, new activities including ‘blood test for ty-
phoid’, ‘HIV sero-status’ and ‘BS for MPS (Malaria parasites)’ are introduced into
the process and the subsequent process model is modeled into a workflow. After the
‘examine patient’ activity, the multiple choice constraint enables the selection of at
least one of the activities from the pockets of flexibility indicated by the dotted
boundary in figure 7.
From fig. 4, 5, and 6, we demonstrate how flexibility is achieved in the designed
workflow models through the use of pockets of flexibility composed of flexibility
fragments. The core process of the workflow model later (fig. 3) forms an instance
template from which the subsequent process instances are derived.
Fig. 6. Process Instance 3
6 Framework Validation
The framework validation provided us with a formal procedure to determine
whether the designed framework could be applied by knowledge practitioners, specif-
ically medical practitioners involved with CTs.
6.1 Validation criteria
To validate the designed framework, we developed three broad criteria (usability,
usefulness and flexibility) based on the requirements derived in section 5 and also
following the DS method from [9] as summarized below.
1. Flexibility – the ability for knowledge workers to easily make changes without
having to entirely change the parts of the BPs that are not impacted by these
changes. This was measured using stability and efficiency of the workflow model
designed following the framework guidelines. i). stability – the ability of the
workflow model to appropriately adapt to the changing business processes (the
variations in process instances). It was measured using the number of tasks re-
maining unchanged in the different process instances, and the number of errors
observed upon introduction of changes. ii). efficiency – the degree to which the
time and effort resources are saved during workflow modeling. This was meas-
ured in terms of; the time to design workflows and correct errors, and the level of
output produced during the modeling process.
2. Usability – the ability for knowledge workers to model following the framework
guidelines with limited cognitive effort. Usability was measured using: i). learna-
bility - the ability to follow a given number of steps in the framework to model a
workflow; ii). satisfaction - the ability to arouse happiness in achieving the goal
for which the workflow model was intended for; iii). interactivity - the relation-
ships and inter relations amongst the steps in the guidelines; and iv). under-
standability - the ability for knowledge workers to comprehend the guidelines
and use them correctly (clarity of the: steps that ease interpretation and compre-
hension; definition of terms; and the nature of the language used).
3. Usefulness – the degree to which the workflow models enhance knowledge
workers’ effectiveness through the benefits offered by process automation. This
is measured using i). Practitioner perceptions towards the framework ii).
Recommendability for usage of the modeling framework to other users.
6.2 Validation environment
In validating the framework, we followed the action research cycle and its steps
(planning, action, monitoring and reflection) to engage the medical practitioners first
in workshop sessions with the intention of introducing a change to the exiting form of
practice [2] and secondly evaluating or collecting evidence using questionnaires re-
gards the change that the we introduced. During the workshop sessions, the Declare
tool was installed on the participants’ computers and also printed guidelines distribut-
ed. Participants were guided on how to model their workflows. After three workshop
sessions, questionnaires were administered to the participants to seek their feedback
on the validation criteria. The responses received were collated and respective re-
sponse percentages also computed. A sample population of 15 participants was se-
lected from our case organisation. The selection was based on the purposive sampling
method of Shajahan [25], because we needed knowledgeable participants about the
CT BPs. The sample population was composed of doctors, data managers and clinical
officers.
We worked with a clinical study team involved with the ‘datafax study’ in the case
organization. The ‘data fax’ study has both manual as well as automated processes
which include the following; patient registration, clinical diagnosis, data management,
and reporting. We chose the patient registration, data collection during patient diagno-
sis using the case report forms (CRFs), and data validation processes. These provided
an opportunity for our research to analyze several activities and sub processes embed-
ded within the BPs.
6.3 Validation results
Usability – Four parameters were used to measure the degree of usability of the
Workflow Modeling Framework as shown in table 1.
Table 1. Validation results when using the patient registration process at case organization
Question Yes (%) No (%)
Framework learnability 73.3 26.7
Framework understandability 80 20
Satisfaction 93.3 6.7
Framework interactivity 86.7 13.3
Average percentage 80 20
a) Learnability – the framework was found to be highly learnable (73.3%). This was
owed to the logical and systematic arrangement of the modeling guidelines. It
was also attributed to the ability of the participants to follow the steps in the pro-
vided framework guidelines to model their workflows.
b) Understandability – this scored highly (80%) as the participants felt that the use
of simple language, clear definition of terms, and lack of ambiguous confusing
jargons made it easy for them to comprehend what the framework entailed. How-
ever, a section of the participants (20%) required more effort in trying to under-
stand the framework, an aspect that could have been attributed to their traditional
way of working.
c) Satisfaction – the framework also scored highly (93.3%) at user satisfaction by
the participants. This was attributed to the simplicity of the workflow modeling
framework, reusability of process models, and the ability to easily verify and ana-
lyse workflow model performance issues. Only 6.7% of the participants posted a
differing opinion and we could not quite readily establish the reason for that alt-
hough we postulated it could have been a direct effect of resistance to change to-
wards the new work processes.
d) Interactivity – this scored 86.7%. The participants found the framework to be
highly interactive due to the inter-linking and inter-related steps in the guidelines.
e) Usefulness – this was measured using the practitioners’ perceptions towards the
framework. The responses recorded in this respect included ‘‘I’m impressed that
I can see which constraints are being violated in the workflow model’’, “I am
able to save my process instance model and reuse it later”, “ I do not have to be a
programming expert to do these models” among others. These positive remarks
indicated that the framework enabled the practitioners to effectively model work-
flows using the different components and recommended modeling tools. Addi-
tionally, we also measured the usefulness of the framework through
recommendability – From the results in table 2, it was observed that 80% of the
respondents were affirmative in regard to whether they would recommend the
framework or not for use by other clinical trials. The respondents attributed this
to the benefits they observed in using the framework including reduction in ef-
fort, cost and time in performing activities. They felt empowered by the frame-
work because it provided a set of guidelines that sufficiently guided workflow
modeling on their part.
Table 2. Framework recommendability
Validation criteria Yes (%) No (%)
Framework recommendabil- 80 20
ity
f) Flexibility – this was measured using efficiency and stability of the workflow
models.
Table 3. Analysis of flexibility in the designed workflow models
Mean Std De- Std Error of
viation mean
Time to model Open In- 3.20 0.447 0.200
stance
No. of process tasks in 2.20 0.447 0.200
open instance
Execution time (process in- 2.40 0.548 0.245
stance 1)
Execution time (process in- 2.20 0.447 0.200
stance 2)
Execution time (process in- 1.60 0.548 0.245
stance 3)
No. of process tasks re- 1.20 0.447 0.200
maining unchanged (process
instance 1)
No. of process tasks re- 1.20 0.447 0.200
maining unchanged (process
instance 2)
No. of process tasks re- 1.80 0.447 0.200
maining unchanged (process
instance 3)
No. of errors in process in- 1.00 0.000 0.000
stance 1
No. of errors in process in- 1.00 0.000 0.000
stance 1
No. of errors in process in- 1.00 0.000 0.000
stance 1
Time to resolve errors in 1.00 0.000 0.000
process instance 1
Time to resolve errors in 1.00 0.000 0.000
process instance 2
Time to resolve errors in 1.00 0.000 0.000
process instance 3
a) Efficiency – this was measured based on the time taken to complete modeling a
workflow, and the time taken to resolve errors as well as the level of productivity
over a specific period of time versus the number of process tasks modeled within
that time.
i. Time – we further observed that less time was taken to model process in-
stances of the work packages within CTs compared to using other traditional
methods. This is illustrated from the measures of central tendency, where on
average the modeling time was between 15-20 minutes with a standard devi-
ation of 0.447 and a standard error of 0.2. This indicated that all responses
were close to the average modeling time noted therein. Without use of work-
flow modeling systems, it was observed to take an average of more than 20
minutes to complete execution of a similar process scenario. With regard to
error resolution, it did not take a very long time to resolve any identified er-
rors within the workflow models. The observed error resolution time was on
average less than 1 hour.
ii.Productivity – the number of tasks modeled as part of the open instance
within the timescales mentioned above (15-20 minutes) indicated that only a
reasonable amount of effort was required for workflow modeling by medical
practitioners. In this case 17 tasks were modeled on average by the respond-
ents. The results do indicate a standard deviation of only 0.447 with a stand-
ard error of 0.2. We thus observe that less effort was required to carry out
work despite the changing process instances.
b) Stability – we measured stability using; the number of tasks that remain un-
changed with the addition, removal or modification of the designed workflow
models for varying process instances, and the number of errors identified in an
individual process instance model. From table 3, we also observed a large num-
ber of unchanged tasks in the different process instances. This is directly repre-
sentative of the stability that is maintained within the processes irrespective of the
changing process instances as highlighted in the results for process instances 1, 2
and 3. A mean of 1.2 that is representative of an average of 7 tasks is observed
for the first and second instances with a standard deviation and standard error of
0.447 and 0.2 respectively. The third instance had on average 12 tasks that re-
mained unchanged from the original open instance with a standard deviation of
0.477 and a standard error of 0.2. The results do indicate that flexibility was
achieved in the designed workflow models for the different process instances
based on the numbers of tasks that remained unchanged within the changing pro-
cess instance models. With regard to the errors observed during the execution of
the process instances, the results in tables 3 indicated no deviation from the mean
value of the error related variables (number of errors encountered during execu-
tion of process instance 1, process instance 2, and process instance 3 as well as
the time taken to resolve the errors in these three instances). A standard deviation
and standard error of 0 was recorded for all the above mentioned variables. On
average less than 5 errors were observed within all the 3 process instances. These
results thus further indicate that the workflow models were able to sustain a high
degree of stability despite the changing process instances with very few errors in-
troduced into the workflow models as a direct consequence of the changes occur-
ring in the different process instances.
7 Conclusion and Future Research
The research was aimed at enhancing process flexibility in the designing of work-
flow models that could be easily interpreted by workflow engines. The workflow
modeling framework was addressing the gaps (see section 1) that come along with the
use of natural language text involved with CTs used by medical practitioners. As
such, we identified the process-related tasks for BPs in CTs and the challenges faced
by the medical practitioners while using the natural language text involved with CTs.
Several requirements were derived though with specific attention to process flexibility
that were used in the designing of the workflow models and related process instances
in the modeling framework. The workflow modeling framework provides a set of
guidelines that could be used by knowledge workers during the workflow modeling
process. Using the framework we demonstrated an example of a designed workflow
model and corresponding model process instances. The framework was validated
using various criteria including but not limited to flexibility, usability, usefulness.
Notwithstanding the promising results observed from the workflow modeling
framework, more work still needs to be done. First, the research was limited to en-
hancing workflow modeling for CT practitioners using flexible approaches but did not
dwell much on the adoption and subsequent usage of the modeling framework. We
therefore suggest to further study the qualitative and quantitative effects of the appli-
cation and usage of the workflow modeling framework in CT settings. Further re-
search could also be conducted on the identification of ways of better handling docu-
mentation processes especially within CTs with a possibility of integrating a docu-
mentation module in workflow modeling tools. The module would hence serve as a
knowledgebase that can be used as a point for future reference. Lastly, the workflow
modeling framework could further be subjected to more empirical validations in the
CTs and other health related domains to determine its usefulness and usage in enhanc-
ing the way of working.
References
1. Aalst, W. M. P., Pesic, M., Schonenberg, H. (2009). Declarative workflows: Balancing
between flexibility and support. Computer Science-Research and Development, 23(2), 99-
113. URL http://dx.doi.org/10.1007/s00450-009-0057-9.
2. Elliott, J. (1991). Action research for educational change (Vol. 49). Buckingham: Open
University Press.
3. Becker, J., Rosemann, M., & Von Uthmann, C. (2000). Guidelines of business process
modeling. Business Process Management, 241-262.
4. Becker, J., Weiss, B., & Winkelmann, A. (2009, January). Developing a business process
modeling language for the banking sector–a design science approach. In Proceedings of
the 15th Americas Conference on Information Systems (AMCIS 2009).
5. Capretz, M., Toledo, M. (2009). Web technologies in a collaborative platform for clinical
trials. RECIIS, 3(4), 209-223.
6. Friedman, L., Furberg, C., DeMets, D.L. 1998. Fundamentals of Clinical Trials. Springer.
7. Fowler, M. (2005). Language Workbenches: The Killer-App for Domain Specific Lan-
guages? Retrieved from http://martinfowler.com/articles/languageWorkbench.html
8. Headlee, D. (2004). The paper trail: CRFs, Source Documents and Data collection tools.
National Institutes of Health National Cancer Institute Center for Cancer Research Medical
Oncology Clinical Research Unit
9. Heinl, P., Horn, S., Jablonski, S., Neeb, J., Stein, K., & Teschke, M. (1999). A comprehen-
sive approach to flexibility in workflow management systems. ACM SIGSOFT Software
Engineering Notes, 24(2), 79-88.
10. Khan, S. A., Kukafka, R., Bigger, J. T., Johnson S.B. (2008). Re-engineering Opportuni-
ties in Clinical Research using Workflow Analysis in Community Practice Settings.AMIA
2008 Symposium Proceedings.Pp.363-367
11. Lindsay, A., Downs, D., & Lunn, K. (2003). Business processes—attempts to find a defi-
nition. Information and Software Technology, 45(15), 1015-1019.
12. Marks, R.G. (2004). Validating electronic source data in clinical trials. Retrieved from
www.sciencedirect.com
13. March, S.T., and Smith, G.F. (1995) Design and natural science research on information
technology. Decision Support Systems.
14. Peleg, M., Boxwala, A. A., Bernstam, E., Tu, S., Greenes, R. A., & Shortliffe, E. H.
(2001). Sharable representation of clinical guidelines in GLIF: relationship to the Arden
Syntax. Journal of biomedical informatics, 34(3), 170-181.
15. Regev, G., & Wegmann, A. (2005). A regulation-based view on business process and sup-
porting system flexibility. In Proceedings of the CAiSE (Vol. 5, pp. 91-98).
16. Reijers, H. A. (2006). Implementing BPM systems: the role of process orientation. Busi-
ness Process Management Journal, 12(4), 389-409.
17. Robson, C. (1993). Real world research. Blackwell: Oxford
18. van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M, van Dongen, B.F., Alves de
Medeiros, A.K., Song, M., Verbeek, (1995). A class of Petrinets for modeling and analyz-
ing business processes. Technical Report 26, Eindhoven University of Technology.
19. Shahar, Y., Young, O., Shalom, E., Mayaffit, A., Moskovitch, R., Hessing, A., & Galperin,
M. (2003). DEGEL: A hybrid, multiple-ontology framework for specification and retrieval
of clinical guidelines. Artificial Intelligence in Medicine, 122-131.
20. Shiffman, R. N., Karras, B. T., Agrawal, A., Chen, R., Marenco, L., & Nath, S. (2000).
GEM: a proposal for a more comprehensive guideline document model using XML. Jour-
nal of the American Medical Informatics Association, 7(5), 488-498.
21. Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.P. (2008). Supporting
flexible processes through recommendations based on history. In: Dumas, M., Reichert,
M., Shan, M.C. (eds) International Conference on Business Process Management (BPM
2008), vol 5240 of Lecture Notes in Computer Science, pp 51–66, Springer-Verlag, Berlin.
22. Tu, S. W., & Musen, M. A. (2001). Modeling data and knowledge in the EON guideline
architecture. Studies in health technology and informatics, (1), 280-284.
23. Weske, M. (2007). Business Process Management: Concepts, Languages, Architectures.
Springer-Verlag Berlin Heidelberg.
24. Workflow Management Coalition (WfMC). (2005). Workflow Process Definition Inter-
face - XML Process Definition Language (XPDL). Document Number WFMC-TC-1025.
25. Shajahan, S. (2007). Research methods for management. 6th Edition, Jaico Impression.
26. Mendling, J., Reijers, H. A., & van der Aalst, W. M. (2010). Seven process modeling
guidelines (7PMG). Information and Software Technology, 52(2), 127-136.
27. Huff, S. M., McClurec, R., Parkerc, C., Rochac, R., Abarbaneld, R., Beardd, N., ... & Zillf,
D. A. (2004). Modeling guidelines for integration into clinical workflow. In Medinfo
2004: Proceedings of the 11th World Conference on Medical Informatics,[San Francisco,
September 7-11, 2004] (Vol. 107, p.174)
28. Pesic, M., Schonenberg, H., & van Der Aalst, W. M. (2009). DECLARE Demo: A Con-
straint-based Workflow Management System. BPM (Demos), 9.
29. Han, Z., Zhang, L., Ling, J., & Huang, S. (2012). Control-Flow Pattern Based Transfor-
mation from UML Activity Diagram to YAWL. In Enterprise Interoperability (pp. 129-
145). Springer Berlin Heidelberg.
30. Pesic, M., Schonenberg, M. H., Sidorova, N., & van der Aalst, W. M. (2007). Constraint-
based workflow models: Change made easy. In On the Move to Meaningful Internet Sys-
tems 2007: CoopIS, DOA, ODBASE, GADA, and IS (pp. 77-94). Springer Berlin Heidel-
berg.
31. Pesic, M., & van der Aalst, W. M. (2006, January). A declarative approach for flexible
business processes management. In Business Process Management Workshops (pp. 169-
180). Springer Berlin Heidelberg.
32. Redding, G., Dumas, M., ter Hofstede, A. H., & Iordachescu, A. (2010). A flexible, object-
centric approach for business process modeling. Service Oriented Computing and Applica-
tions, 4(3), 191-201.
33. Pesic, M., Schonenberg, H., & van der Aalst, W. (2010). The Declare service. In Modern
Business Process Automation (pp. 327-343). Springer Berlin Heidelberg.
34. Chiu, D. K. Li, Q. and Karlapalem, K. (1999). A meta modeling approach to workflow
management systems supporting exception handling. Information Systems, 24(2):159 –
184.
35. Han, L. Yao, Z. Liu, S. and Zheng, Z. (2007). Application and research of ontology in
workflow knowledge metamodel. In Computer Supported Cooperative Work in Design,
2007. CSCWD 2007. 11th International Conference on, pages 675 –680
36. Sadiq,S., Sadiq, W., Orlowska, M. (2001). Pockets of Flexibility in Workflow Specifica-
tion. In Lecture Notes In Computer Science: Proceedings of the Twentieth International
Conference on Conceptual Modeling (pp 513-526), Springer-Verlag, Berlin, Germany.
37. Winter, R. (2008). Design science research in Europe. European Journal of Information
Systems, 17(5), 470-475.
38. Goedertier, S., Vanthienen, J., & Caron, F. (2013). Declarative business process model-
ling: Principles and modelling languages. Enterprise Information Systems, 1-25.
39. Pichler, P., Weber, B., Zugal, S., Pinggera, J., Mendling, J., & Reijers, H. (2012). Impera-
tive versus declarative process modeling languages: An empirical investigation. In F. Dan-
iel, K. Barkaoui & S. Dustdar (Eds.), (pp. 383-394) Springer Berlin Heidelberg.
40. Hevner, R. A, March, T., Park, J., & Ram, S. (2004). Design science in information sys-
tems research. MIS Q. 28, 1 (March 2004), 75-105.
41. Hevner, R. A. (2007). A three cycle view of Design science. Scandinavian Journal of In-
formation Systems. Jarrar, Y. 2000. ERP Implementation and Critical Success Factors, The
Role and Impact of Business Process Management”, of The 2000 IEEE International Con-
ference on Management of Innovation and Technology Conference proceedings, Singapore.