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DSML4PTM: A Domain-Specific Modelling Language for Patient Transferal Management

  • FHNW University of Applied Sciences and Arts Northwestern Switzerland
  • Eastern Switzerland University of Applied Sciences

Abstract and Figures

This paper presents a domain-specific modelling language for patient transferal management (DSML4PTM). To foster reusability within the modelling community, existing modelling languages were taken into account as far as possible and then extended as was needed by the application domain. The language was developed through iteration following the design science research methodology. For requirements elicitation purposes domain expertise and healthcare standards were taken into account. The new modelling language was evaluated first with respect to the elicited requirements and then through the creation of two models reflecting a reference process and an application scenario. Next, an evaluation on the perceived usefulness and cognitive effort of the language was performed using a focus group with modelling and domain experts.
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A Domain-Specific Modelling Language for
Patient Transferal Management
Emanuele Laurenzi1,2,3, Knut Hinkelmann1,3, Ulrich Reimer2, Alta van der Merwe3, Pascal Sibold1,
and Rainer Endl
1FHNW University of Applied Sciences and Arts Northwestern Switzerland, Von-Roll Srasse, 10, 4600 Olten, Switzerland.
2IPM-FHSG, Institute of Information and Process Management, University of Applied Sciences St. Gallen,
Rosenbergstrasse, 59, 9001 St. Gallen, Switzerland.
3Department of Informatics, University of Pretoria, Lynnwood Rd, 0083 Pretoria, South Africa.
{emanuele.laurenzi, knut.hinkelmann}, {emanuele.laurenzi, ulrich.reimer, rainer.endl},
{alta.vdm, knut.hinkelmann},
Keywords: DSML4PTM, domain-specific modelling language, domain-specific conceptual modelling, metamodeling,
model-driven approach, modelling languages extension.
Abstract: This paper presents a domain-specific modelling language for patient transferal management (DSML4PTM).
To foster reusability within the modelling community, existing modelling languages were taken into account
as far as possible and then extended as was needed by the application domain. The language was developed
through iteration following the design science research methodology. For requirements elicitation purposes
domain expertise and healthcare standards were taken into account. The new modelling language was
evaluated first with respect to the elicited requirements and then through the creation of two models reflecting
a reference process and an application scenario. Next, an evaluation on the perceived usefulness and cognitive
effort of the language was performed using a focus group with modelling and domain experts.
Recent statistical findings released by Eurostat (2017)
revealed that healthcare is still a main expenditure in
developed countries. Germany has the lead among
EU members with a current healthcare expenditure
equivalent of 11.0% gross domestic product (GDP),
while Switzerland has an even higher expenditure
(11.4%). These percentages have the tendency to
even increase according to The World Bank (2014)
and governments are reacting by imposing pressure
on healthcare providers, especially on hospitals to
lower cost. However, hospitals should still provide a
high quality of service (Lenz et al. 2012), which
require processes and activities to be as optimal as
possible. This, however, results in a huge challenge
due to the complexity of the domain. In fact, many
structured and ad-hoc processes that involve a broad
range of crucial decisions typically can take place
across organizations and among actors with different
expertise. One process that reflects such a complex
environment is the so-called transferal management
process, which is also called transitional care or
hospital discharge management/planning. Parry et al.
(2008) defined transferal management as “a set of
actions designed to ensure the coordination and
continuity of care received by patients as the transfer
between different locations or levels of care”. This set
of actions, also called administrative pathways,
includes medical information and excludes the
treatment of the patient, which rather refer to clinical
pathways (Lenz & Reichert 2007).
The Patient Radar Project (Reimer & Laurenzi
2014) addresses the above-mentioned issues by
enabling intersectoral collaboration between acute
hospitals and rehabilitation clinics, where (a)
rehabilitative expertise is brought early into the acute
somatic treatment loop, and (b) demand for
rehabilitation treatment is considered as early as
possible. Such a collaboration takes place within the
complex settings of the transferal management
domain, where many domain experts are involved,
i.e. from acute hospitals, rehabilitation clinics, and
finally health insurances for cost reimbursements.
In our work, the main idea is to adopt a model-
driven approach that provides all the relevant
concepts and decision types of the transferal
management domain in a form that is familiar to the
domain experts. The ultimate goal is to enable domain
experts designing and/or adapting models in a
meaningful and less error-prone way, and therefore to
help produce quality domain models.
For this, a domain-specific modelling language
for patient transferal management (DSML4PTM) is
The paper is organized as follows: Section 2
provides the motivation for using DSMLs. Section 3
introduces the most relevant DSMLs for this work.
Next, the adopted methodology is described in
section 4. In section 5 we will describe the new
metamodel as well as the procedure we followed.
Section 6 presents the implementation of the new
metamodel in the ADOxx metamodeling toolkit.
Finally, evaluation and conclusion are described in
section 7 and 8, respectively.
In order to ensure a common understanding on the
term “modelling language” we refer to the framework
developed by Karagiannis & Kühn (2002) - A
modelling language is defined by notation, syntax
and semantics. The notation specifies the visual
representation of the modelling language, while the
semantics define the meaning of the syntactic element
by establishing a mapping to a semantic schema. The
modelling procedure determines how to apply a
modelling language to create models.
Models are defined in the metamodel, which is
created by a metamodeling language. The metamodel
itself is defined in a metametamodel (see Fig. 1).
According to different authors (Karagiannis & Kühn
2002, Atkinson & Kuhne 2003, Laarman & Kurtev
2010) these 3 levels are sufficient. The Object
Management Group (OMG 2015), for instance,
proposed metamodeling framework (MOF) for
model-driven engineering (OMG 2015) with exactly
this number of levels.
Figure 1: Metamodelling Hierarchy (Strahringer 1996)
There is a distinction between domain-specific
modelling languages (DSML) and general-purpose
modelling languages (GPML), An example for a
GPML are UML class diagrams. A GPML provides
the user with a high degree of freedom, and thus
might lead to inconsistency and representations that
do not make sense. A way to overcome these
problems is by extending the semantics of the
metamodel (Pérez & Porres 2013, Lodderstedt et al.
2002, Felfernig et al. 2000) by adding constraints that
restrict the varieties of models. For this purpose,
OMG extended the UML metamodel introducing the
Object Constraint Language standard (OMG 2014).
This approach, however, increases the complexity of
the modellers’ task as they either have then to know
all the constraints, or they are hindered by the system
to make certain modifications to a model when they
would violate a constraint.
Domain-specific modelling languages (DSMLs),
shift this complexity to the metamodel level, where
constraints are introduced in order to ease the
modelling on level 1 (see Fig. 1) (Fowler 2011, Frank
2010, Gray et al. 2008, Mernik et al. 2005, van
Deursen et al. 2000). For this, domain-specific
modelling languages (DSMLs) promise to enable
domain experts designing models in a meaningful and
less error-prone way, and therefore support producing
quality models (Kelly & Tolvanen 2008). Moreover,
allowing domain experts dealing directly with
language constructs they are familiar with, leads to
significant impact on their learning curve. It was
already observed by Hudak & Paul (1996) that,
domain experts can quickly learn the language and its
applicability improves. In other words, DMSLs bring
the benefit of high understanding of models (hence,
the underlying reality) among domain experts,
fostering not just productivity in design-time, but also
the optimization phase, where paint points are rapidly
identified and actions can be taken accordingly.
A common approach to develop a DSML consists
of several iterative phases until a version of the
language is delivered that is mature enough. The
order of these phases is typically as follows: (1)
capturing domain requirements, (2) defining concrete
syntax, abstract syntax and related semantics
(metamodel) (3) testing the language with end users.
The latter can lead to further iterations in case some
requirements of the language are not fulfilled. The
main complexity resides in phase “2”, which is
according to Cho et al. (2012) is crucial even for
language development experts.
In the following section, we present some of the
most relevant DSMLs in healthcare.
Shenvi et al. (2007) created a DSML to model an
electronic Patient Care Report (ePCRs). The
metamodel includes domain-specific modelling
constructs such as patient demographics, vital signs
and medications. Each of these constructs has several
attributes that define the visualization and behavior of
that element. Additionally, connections between
constructs are defined.
Mathe et al. (2009) have designed a visual DSML
(Clinical Process Management Language) for
capturing treatment protocols. They argued that at
that time there existed no widely accepted visual
language for capturing treatment protocols, and
generic software modelling languages, such as UML,
are not designed to capture medical knowledge.
Burwitz, et al. (2013) have evaluated modelling
languages for their suitability to model clinical
pathways. They started with defining domain-specific
requirements and found out, that existing languages
such as BPMN do not fulfil all of them. The available
languages mainly fail to represent variable flows and
evidence-based decisions. The authors therefore
decided to create their own DSML (called CP-Mod),
based on the Clinical Algorithm. CP-Mod needed to
be extended to be able to model complex health care
processes and to reduce the existing deficits by
addressing the requirements that are only partly or not
at all met.
Heß et al. (2015) have done a similar work, which
can be seen as an advancement of the work from
Burwitz, et al. (2013). Also Heß, et al. (2015) came
up with an extended list of domain-specific
requirements. They recognized that no language fully
covers these extended requirements to model clinical
pathways. They argue: “to realize the potential
benefits of CPs (clinical pathways), a comprehensive
modelling method accounting for peculiarities of
hospitals’ action system and IS is required. The core
of such a method should be a Domain-Specific
Modelling Language for CPs”. They further mention
that widely accepted business process modelling
techniques to model CPs lack in fostering
communication between stakeholders and in quality
support from tools. Thus, they decided to extend the
metamodel of the language MEMO OrgML to create
a DSML for the modelling of clinical pathways. The
newly created language DSML4CPs reconstructs the
professional terminology from the medical domain
with a special focus on oncology.
Braun, et al. (2015) have also noticed the need for
domain-specific concepts to model clinical pathways.
However, they propose to use BPMN for the
extension as it is widely used for modelling business
processes. In their work, they use the language CP-
Mod to derive requirements for the extension of
BPMN and to create the language BPMN4CP. One
advantage of the solution is that several perspectives
on a clinical pathway, such as resource, process or
document perspective, are possible to be modelled.
They have revised their BPMN4CP approach based
on its practical application. This led to an evolution
of BPMN4CP from version 1.0 to version 2.0 to cover
additional business requirements (Braun et al. 2016).
Neumann et al. (2016) also developed a domain-
specific extension of the BPMN modelling language.
Their objective was to model and execute
interoperative surgical workflow in the integrated
operating rooms.
It is becoming a common practice to support
experts by utilizing modelling languages that cover
the requirements of the underlying domain (Neumann
et al. 2016, Braun, Burwitz, et al. 2015, Braun,
Schlieter, et al. 2015, Burwitz et al. 2013). However,
to the best of our knowledge, so far research on
DSMLs in healthcare mainly focused on clinical
pathways (CPs). On the contrary of administrative
pathways, CPs refer to the treatment activities of
patients. Therefore, we can state that there is a gap in
the literature to provide a domain-specific modelling
language for the patient transferal management.
In this work, we developed the DSML by adopting
the design science research (DSR) approach of
Vaishnavi & Kuechler (2004):
1. Awareness of the problem: In this phase, we
elicited requirements from the transferal
management domain. For this, we could mainly
rely on the activities performed in the research
project Patient Radar. Namely, they carried out
interviews and workshops with modelling and
domain experts, from which a reference model
and an application scenario were created.
Additionally, health-related standards and
documentation were taken into account together
with literature review.
Next, a categorization was made to
differentiate procedural from declarative
information as it is recommended by von Halle
& Goldberg (2010). That is, we distinguished
between process logic (i.e. which activities take
place including prescribed flow and more ad
hoc activities) and business logic (i.e. how to
make decisions including structured and
unstructured logic). Additional declarative
categorizations concerning data representation,
documentation and organization management
were included. Table 1 shows one of the 50
Table 1. Excerption from list of requirements
constructs to
model decision
according to the
Entry, exit,
inclusion and
exclusion criteria
for the different
types should be
according to the
2. Suggestion: In this phase, we took into account
all the requirements elicited in the previous phase
and suggested a new metamodel accordingly.
3. Development: In this phase, we implemented the
metamodel as well as the graphical notation for
the concrete syntax in the ADOxx Development
4. Evaluation: In this phase, we first evaluated the
new language with respect to the elicited
requirements. Then, we investigated its
applicability by modelling both the reference
model and application scenario created in the
awareness of problem. Finally, a focus group
session was performed to evaluate the perceived
usefulness and cognitive effort of the language.
All phases were used to continuously refine the
The design approach for our DSML is based on the
two principles proposed by Karagiannis et al. (2016):
- Considering already existing standard
modelling languages, with related applications
and lessons learned. Emmenegger et al. (2016),
for example, combined several modelling
languages to enhance workplace learning.
- Specialization of language constructs
according to requirements elicited from a
specific domain. Hinkelmann et al. (2016), for
example, extended BPMN to allow specifying
both business requirements for the cloud
service discovery and cloud service
The procedure used to define the new language
was the following:
According to the gathered requirements we (a)
identified the most suitable existing modelling
languages; (b) removed the unneeded modelling
elements (as also Silver (2011) suggests) from the
selected languages; (c) added the domain-specific
elements; (d) integrated the language constructs that
belong to different modelling languages, and finally
(e) added constraints among the remaining language
constructs. Steps (b) and (c) can also be inverted.
These steps were embedded into a two-tier
approach that refers to the metamodeling hierarchy
introduced in Fig. 1, see also (Reimer & Laurenzi
2014). (a), (b), (c) and (d) are all part of the abstract
syntax, which resides in level 2 of the metamodel
stack (see Fig. 2). They all contain semantics. The
identified metamodels in (a), for example, already
contain their own semantics. Moreover, while for (b)
semantics is removed, it is added for (c). Integrating
model elements among each other also imply to
additional semantics, (see (d) in Fig.2). Additional
semantics are added in terms of new relations among
metamodel elements, (see (e) in Fig. 2). Modelling
elements added in (c) are then represented in the form
of concrete syntax together with the remaining
metamodel elements (see level 1 of Fig. 2). In level 1,
new models can be created and existing ones can be
adapted to reflect different scenarios.
Figure 2: Suggested approach
The two-tier approach allows the following:
- On Level 1, administrative pathways from
acute hospitals and rehabilitation clinics are
combined into a coherent discharging
processes. Moreover, new processes can be
accommodated or the existing ones adapted as
the discharging process that fits one acute
hospital might differ for another clinic.
- On Level 2, the new metamodel can be
extended further to cover new domain
requirements, e.g. transferring patients from
acute hospitals to nurse facilities.
About 100 concepts and 300 attributes were added
in the metamodel. Due to space restrictions, we do not
list all the elements, but we rather motivate the
selection for each metamodel with respect to the most
relevant requirements. For each of the metamodels,
we then describe the rest of our procedure by
mentioning some of the extended elements as well as
the integration among metamodel elements.
5.1 Process Modeling
Requirements for structured and unstructured
It is necessary to combine predefined, clearly
structured process parts with more ad hoc process
steps, as some activities and conditions are known in
advance while the execution of others depends on
human judgment or external events. For instance,
before performing the patient’s disposition in the
rehabilitation clinic, the transferal manager should
release all the necessary data and documents related
to the patient (i.e. sequence flow). Conversely, the
responsible physician from the rehabilitation clinic
might discuss with the main physician from the acute
hospital about the patient’s therapy. The activity
execution of this activity is up to the responsible
physician rather than dictated by a sequence flow.
BPMN 2.0 (OMG 2011) is the most widespread
notation for modelling business processes. Therefore
it was selected to model activities and conditions,
which are known in advance and their flow can thus
Figure 3: Extended elements of BPMN
be modelled. However, we removed unnecessary
BPMN elements, and extended it with new elements
to satisfy our list of requirements (see concepts in
light blue bubbles in Fig. 3).
The requirement for the process progress was
addressed by adding a new concept “Status”. This
includes the percentage attribute that reflects the
actual state of the process (see the related concrete
syntax in the point 6 of figure 8).
To model those activities and conditions that cannot
be embedded in the process flow, we chose CMMN
(OMG 2016a) the OMG standard for case
management modelling. Again, we removed the
unneeded elements as well as extended new ones.
“Sentry” and “Entry Criterion” were kept, while for
“Discretionary Task” specialisation like Update
Disposition and Perform Rehab Conference were
The integration with the BPMN elements took place
by connecting the sentry to the task. According to our
proposed semantics, a task can be performed either as
a subsequent activity as part of a flow, or as soon as
the sentry evaluates to true. Moreover, a task can have
one or more sentries. The latter expresses the “OR”
condition. The same applies also in presence of an
input flow and at least one sentry.
The discretionary task concept is a subclass of the
manual task (see bubbles with blue outline in Fig. 4).
At run-time, discretionary tasks that are involved in
the sequence flow are skipped if none of the attached
sentry evaluates to true. For example, the
PerformRehabConference is a discretionary task that
can be executed by the rehab if the patient case is
complex or simply if he/she wants to discuss the case
with the physician in the acute hospital.
5.2 Control Element Model
Requirements for decisions on the process flow
Complex decisions are made along the discharging
process. For example, the acute hospital decides on
whether to start performing the admission for the
incoming patient (e.g. preparing all the needed
resources). This is only possible after the transferal
date has been agreed upon between the acute hospital
and rehabilitation clinic and after the cost
reimbursement form has been sent to the health
insurance. Another example is on decisions that need
to be taken if the patient’s situation worsens.
Control Element Model
As it is already claimed by Hinkelmann (2016),
sentries can be specified in the so-called Control
Element Model in order to enable reuse of conditions
and events. The metamodel elements that are taken
into account from the Control Element Model are
“On-Condition” and “If-Condition”. Figure 4 shows
the extended elements in the light orange bubbles as
well as their integration with the sentry element of the
Figure 5: Some extended elements from DMN, their integration and additional constraints
5.3 Decision Modeling
Requirements for decisions on the activity level
The decision logic for complex decisions along the
discharging process has to be modelled on the activity
level. For example, the application of the right
discharging criteria permits to derive the most
suitable rehabilitation type and interface (e.g. from
acute hospital to inpatient neurological rehabilitation,
rather than to a rehabilitation with compulsory
medical monitoring). In Switzerland, discharging
criteria are specified in the DefReha© standard issued
by the organization H+ Swiss Hospital (H+ 2016).
In order to model decision logic we selected the DMN
standard (OMG 2016b). The metamodel concerning
the Decision Requirements and the Decision Logic
were reused and integrated into the metamodel of the
new DSML. The analysis of the DefReha© document
has shown, that the decision to determine the
rehabilitation category and relevant inclusion and
exclusion criteria are of central importance for the
domain in Switzerland. Figure 5, shows the extended
elements in light green bubbles (due to space
limitation, we could not list all of them). The
integration took place via the decision element, which
refers to the business rule task and the discretionary
Additional constraints were added among the
extended elements. In section 6 (arrow 5 in Figure 8)
we describe an example on how the DMN elements
are related with each other.
5.4 Documents and Knowledge
Requirements for structured documents
The modelling has to comply with healthcare
standards like the International Classification of
Functioning, Disability and Health (ICF) standard
(World Health Organization 2016).
Representing the relevant process documents is
also a need: The cost reimbursement form
(abbreviated as KoGu) is the most important
document in the transferal management process. In
case it is definitely rejected, the discharging process
most likely comes to an end due to the lack of
financial support.
Another important document is the hospitalization
form, which contains all the needed information for
the transferal manager to start the process. The
process progress along the patient hospitalization
should provide visibility to crucial events such as case
and patient accepted by the clinic, first assessment
performed, discharging date agreed, etc. Then, the
medication list form that has to be updated after the
transferal is confirmed.
Finally, each document belongs to a category. For
instance, the hospitalization document belongs to the
administrative data, the medication list belongs to the
medical data, while the assistance list belongs to the
care status data.
Requirements for document representation
There is a need to represent the status and versions of
document. For example, the KoGu document should
include the status (i.e. ready, sent, rejected or
accepted), while the ICF document should include
both the trend and the status of each category.
Documents and Knowledge Model
Complying with many standards as well as
accommodating many relevant documents were
addressed by selecting the Documents and
Knowledge Model adopted in the European project
LearnPAd (De Angelis et al. 2016). This provides a
way to structure documents and knowledge
representations. For example, we introduced the new
concept “KoGu data object” as a specialized data
object. The required status of the KoGu data object
was modelled by means of attributes (Figure 6 shows
the concrete syntax of the KoGu data object with
related status as well as the hospitalization document
with related version on top of the icon).
Figure 4: Some extended elements from CMMN, Control
Element Model and their integration
Figure 6: Example of status and version on extended
data objects
Constraints for the defining the structure of data
and documents were also added. Due to the very large
collection of data and documents, we grouped them
into medical data, administrative data, care status data
and process progress. Each group contains several
data and documents (e.g. see assistance data and
special medication data connected with the care status
in Fig. 7).
Additional constraints addressed the need of
indicating the status of data/documents. For instance,
the metamodel contains a connection between the ICF
standard to the ICF qualifier status, and between the
KoGu document and its status (see Fig. 7). The same
applies on other four elements, i.e. process status,
physical transfer status, medication list status, and
acceptance status. These “Status” elements are
determined based on the progress of the data
collection and are used to aggregate the overall
“Status” in the DSML4PTM model.
Bubbles with light yellow in Fig. 7 depict some of
the specialized elements, while connections to other
colored bubbles show their integration with other
metamodel elements, i.e. from BPMN (in light blue)
and from DMN (in light green).
Figure 7: Some extended elements from Document and
Knowledge Model, their integration and additional
5.5 Organizational Model
Requirements for a structure on roles and care units
Making the structure of the organization explicit in a
model supports the domain expert to identify quickly
and easily who is performing what role in which care
unit or rehab clinic.
Organization Model
The Organizational Model as proposed in
Emmenegger et al. (2016) provides constructs for
assigning personnel to roles, which in turns are
assigned to organization units. This structure ensures
that a user can easily browse through an organization
and find a suitable person or business unit.
We extended the organizational metamodel as
follows. The role element was specialized as:
Administrative Staff, Nurse, Acute Physician,
Rehabilitation Physician, Patient, Patient
Disposition and Transferal Manager. Next, the
organizational unit was specialized as: Acute
Hospital, Rehabilitation Clinic, Care Unit, Non-
intensive Care Unit, Intensive Care Unit, Emergency
Room, Site of Care and Health Insurance.
The organizational metamodel was then integrated
with the process model as follows:
- Extended organizational unit elements are
associated with the extended pool elements
from BPMN.
- Extended role elements are associated with
the extended lane elements from BPMN. For
example, the role transferal manger
(subclass of role) is associated with the
transferal manager (subclass of Lane).
The new metamodel was implemented in ADOxx
Development Toolkit (
Figure 8, shows part of the application scenario
modelled in DSML4PTM. The figure also shows how
model elements that belong to the above described
metamodels are related to each other.
Arrow 1 shows the connection of the acute
hospital from the process to the organizational unit.
Arrow 2 shows the connection of the data object
“Hospitalization” with the related document, which is
modelled in the Document and Knowledge Model.
Arrow 3 shows the notebook with all data values
of the KoGu document.
Arrow 4 shows the connection going from the
sentry attached to the discretionary task “Re-apply
DefReha Criteria” to the related Control Element
Model. A Sentry contains optional events (ON part)
and conditions (IF part). According to the semantics
borrowed from CMMN, the execution of the task is
possible if both ON-part and IF-part evaluate to true.
Arrow 5 shows the connection of the business rule
task “Apply DefReha Criteria” with the decision
construct “Decide on Rehabilitation Suitability”
modelled in the Decision Requirements Diagram. The
latter construct takes the input “patient data” and
“medical information” (both modelled in the
Document and Knowledge model). It refers to the
knowledge source “DefReha©”, which is an external
PDF. Additionally, rules for selecting the
rehabilitation type are expressed in decision tables.
Hence, all the sub-decisions that reflect each
rehabilitation type are modelled and used as input for
the “Decide on Rehabilitation Suitability” concept.
For example, the neurological rehabilitation type has
three interfaces: Inpatient Rehabilitation,
Compulsory Medical Monitoring and from Medical
Monitoring to Inpatient Rehabiliation. These are
modelled as further decision concepts that in the
metamodel are connected with the “Neurological
Rehabilitation Suitability” concept.
Arrow 6 depicts the connection from the process
status element to all those attributes for which the
status is determined, e.g. data released, first
assessment done, Rehab conference done, transfer
date, KoGu ready, etc.
Additionally, the integration of CMMN and
BPMN allows specifying the actor who would
perform a discretionary task by placing the task on the
appropriate lane.
The new modelling language was evaluated on its
completeness with respect to the elicited domain
requirements. DSML4PTM was implemented in
ADOxx and both a reference process and an
application scenario were successfully modelled after
the implementation. These models were then used in
a two-hour focus group session with four modelling
experts and one domain expert for an evaluation on
the perceived usefulness of the language and its
cognitive effort. In the first hour, the new modelling
language was introduced to the participants and then
a walk-through session explained how the new
language can be applied. In the next hour, the
participants had a hands-on session and they were
asked to extend the reference process with two new
1. DefReha criteria should be re-applied in case
the KoGu document is rejected.
2. KoGu document needs to be revised as some
information is missing.
Figure 8: Part of the reference model implemented in ADOxx
Finally, a questionnaire was provided to perform
a qualitative evaluation. The questionnaire was
developed based on the guidelines proposed by Tullis
& Albert (2013). It was conducted on individual basis
and within a time frame of at least half an hour. The
questionnaire contained 5 sections, i.e. four questions
on general background; eleven questions on modeling
background; three open questions on the perceived
usefulness of the new modelling language; five open
questions on the cognitive effort of the new modelling
language; and three open questions as a feedback for
future improvements.
In the following, we summarize the outcome of
the questionnaire with respect to the perceived
usefulness and cognitive effort.
Perceived usefulness: All participants agreed on
the high potential of the DSML in fostering
communication and collaboration among domain
experts. Modelling experts stated that the new
language simplifies the modelling process, which
helps improve model quality. They also emphasized
that the language can be seen as a basis for the
integration of different information systems (e.g.
health information systems, patient administration
systems, health records) and automated verification.
Cognitive effort: All participants agreed on the
ease-of-use of the new language, for which a metric
is the amount of time that is required to learn the
usage of a language. All the participants stated that
the usage of the language can be learned within a
short period of time. This was backed by the fact that
participants could propose meaningful models for the
two new scenarios within less than half an hour. Of
course, to apply the language in real settings would
need more time to get acquainted with it. Further
metrics we looked at are the appropriate abstraction
of language elements and their default values, the
graphical notation of the concrete syntax, and the
support at design time in creating meaningful models.
The latter was mentioned as one of the main strength
as during the hands-on session participants perceived
they could not connect model elements with each
other randomly. In line with this, modelling experts
mentioned the reduction of modelling mistakes as a
strength of the new language. One agreed upon
drawback of the language is the difficulty to
understand when to use a sentry rather than a
gateway. We noticed that during the hands-on
session, those with a strong BPMN background
tended to use gateways only. One suggestion was to
encourage the usage of sentries as a best practice
while using gateways only if really needed, e.g. in
case the visualization of the flow is fundamental
The evaluation was also done quantitatively by
comparing the reference model created in
DSML4PTM with the equivalent one created in
BPMN. The latter contained approximately 15%
more elements. In fact, in complex scenarios (e.g.
when KoGu is rejected) the specification of most of
the events and conditions that would be modelled
with gateways can be replaced by sentries (that is also
valid for tasks that are embedded in a flow).
Based on the elicited requirements from the
awareness phase, a domain-specific modelling
language for transferal management was developed.
The development procedure is based on principles
that foster the reusability within the modelling
community. Hence, several metamodels were
considered to cover as far as possible the application
domain, i.e. BPMN, DMN, CMMN, Control
Elements Model, Organization Model and
Documents and Knowledge Model. Next, unneeded
modelling elements were removed while others were
added to fulfill the requirements of the transferal
management domain. Then, a metamodel integration
among the remaining modelling elements was
performed. Finally, semantics was mainly borrowed
from the selected metamodels and new one added by
means of constraints among the remaining language
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provides semantically enhanced recommendations
while developing a DSML. Another possible research
direction goes towards the modelling support at
design time. Namely, the retrieval of best practices
modelling patterns according to the syntax and
semantics of models. These patterns will then be
proposed to the modeler for quality improvements.
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... Domain-Specific Adaptation: With the need to address particular application domains, existing modeling languages might not be expressive and concise enough to be adopted. This can lead to the need of adapting existing modeling languages through a domain-specific conceptualization [6,7]. In result, a domain-specific modeling language (DSML) [8,9] is created. ...
... Operators for Adapting a Modeling Language: The adaptation of a modeling language can be done by applying a set of operators on the Language Ontology. In order to determine an initial set of operators, we build on lessons learned from the work of recent research projects, Patient-Radar [6,26] and CloudSocket 5 [27,28]. Thus, the following set of operators were derived to be performed on the Language Ontology, which implies a theoretical foundation based on ontology formalism. ...
... The operators were also validated by applying them on a use case of the Patient-Radar research project. The BPMN modeling language was adapted on-the-fly with modeling elements from the patient transferal domain [6]. Figure 4 shows the screen-shot of Operators 1 and 2 as illustration. ...
The advent of digitalization exposes enterprises to an ongoing transformation with the challenge to quickly capture relevant aspects of changes. This brings the demand to create or adapt domain-specific modeling languages (DSMLs) efficiently and in a timely manner, which, on the contrary, is a complex and time-consuming engineering task. This is not just due to the required high expertise in both knowledge engineering and targeted domain. It is also due to the sequential approach that still characterizes the accommodation of new requirements in modeling language engineering. In this paper we present a DSML adaptation approach where agility is fostered by merging engineering phases in a single modeling environment. This is supported by ontology concepts, which are tightly coupled with DSML constructs. Hence, a modeling environment is being developed that enables a modeling language to be adapted on-the-fly. An initial set of operators is presented for the rapid and efficient adaptation of both syntax and semantics of modeling languages. The approach allows modeling languages to be quickly released for usage.
... BPMN targets process modeling, whereas Archimate targets enterprise architecture modeling. Their particular degree of a domain can serve as a baseline to build modeling languages with higher domain-specificity degree [10,11,12], which in literature are also known as domain-specific modeling languages (DSMLs) [7]. The baseline provides many advantages. ...
... Another emerging term for this activity is "domain-specific adaptation" [13,5]. As shown in [11], domain-specific adaptation also includes integration of different modeling languages and simplification of these by removing unnecessary concepts. In this work, we define a "domain-specific adaptation" as the adaptation of one or more modeling languages. ...
... The Agile and Ontology-Aided Modeling Environment (AOAME) was conceived through the design science research (DSR) approach [25], which provided guidance throughout the construction of the artifact. In particular, the awareness of problem was initially raised by use cases and lessons learned from three research projects targeting three different domains: (1) a patient discharging process among sites of care within the health-care sector [11,26]; (2) business process requirements and cloud service specification for the Business-IT matchmaking in the Cloud [27,28]; (3) workplace learning in public administrations [29]. Each project presented a model-driven solution and domain-specific adaptation activities were performed adopting current engineering life-cycles. ...
Enterprise knowledge is currently subject to ever-changing, complex and domain-specific modeling requirements. Assimilating these requirements in modeling languages brings the benefits associated to both domain-specific modeling languages (DSMLs) and a baseline of well-established concepts. However, there are two problems that hamper the speed and efficiency of this activity: (1) the separation between the two key expertise: language engineering and domain knowledge, and (2) the sequential modeling language engineering life-cycles. In this work, we tackle these two challenges by introducing an Agile and Ontology-Aided approach implemented in our Modeling Environment - the AOAME. The approach seamlessly integrates meta-modeling and modeling in the same modeling environment, thus cooperation between language engineers and domain experts is fostered. Sequential engineering phases are avoided as the adaptation of the language is done on-the-fly. To this end, a modeling language is grounded with an ontology language providing a clear, unambiguous and machine-interpretable semantics. Mechanisms implemented in the AOAME ensure the propagation of changes from the modeling environment to the graph-based database containing the ontology.
... 4) Decisions (subjectiveobjective): Diagnosing and treating different diseases is strongly subjective, depending on individual experience and differing based on clinicians' emotions and mental states (Chang and Hsu 2009;Singh et al. 2020). This applies to experienced professionals and actors with different levels of expertise, e.g., young assistant doctors with limited clinical educations (Laurenzi et al. 2017;Singh et al. 2020). Even though clinicians try to derive objective decisions, they always act with subjective influence and own judgments. ...
... Objective and equal evaluation of patient data Non-prejudices decisions (e.g., objective conclusion based on medical facts) Independent decisions regardless of time or mental state(Chang and Hsu 2009;Gnewuch et al. 2017;Laurenzi et al. 2017;Seeber et al. 2019;Singh et al. 2020)Table 4Interview guideline (German interview questions have been translated into English) ...
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Successful collaboration between clinicians is particularly relevant regarding the quality of care process. In this context, the utilization of hybrid intelligence, such as conversational agents (CAs), is a reasonable approach for the coordination of diverse tasks. While there is a great deal of literature involving collaboration, little effort has been made to integrate previous findings and evaluate research when applying CAs in hospitals. By conducting an extended and systematic literature review and semi-structured expert interviews, we identified four major challenges and derived propositions where in-depth research is needed: 1) audience and interdependency; 2) connectivity and embodiment; 3) trust and transparency; and 4) security, privacy, and ethics. The results are helpful for researchers as we discuss directions for future research on CAs for collaboration in an hospital setting enhancing team performance. Practitioners will be able to understand which difficulties must be considered before the actual application of CAs.
... In addition, the ontology-based metamodeling approach fosters the adaptation of a modeling language to fit a specific domain. In order to have a common understanding of the term "adaptation" we refer to the work of Laurenzi et al. (Laurenzi et al. 2017), where the following operations were performed in the metamodel layer using existing modeling languages: ...
... Two-tier approach, adapted from(Laurenzi et al. 2017) ...
Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. Typically, modeling languages for humans and machine are different from each other. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language. This chapter describes an approach to modeling that is both cognitively adequate for humans and processable by machines. In addition, the approach supports the creation and adaptation of domain-specific modeling languages. A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modeling language, a graphical notation can be added for each class of the ontology. Every time a new modeling element is created during modeling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.
... In addition, the clinical interpretation of medical information is a cognitively challenging task [4]. This not only applies to experienced professionals but also to actors with different or little expertise such as young assistant doctors [4,5]. Medical specialists' available time is usually limited [2,3] and diseases might evolve and patient dynamics change over time [6,7] making diagnostics a highly complex process [8,9]. ...
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The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.
... Approach. We follow a model-driven which performs domain-specific conceptualization (mapping to well-known benefits [25] [17] [10]) on two levels, where the one targets BP users, while the other targets IT service experts. This allows designing domain-specific models capturing suitable domain knowledge on both levels. ...
Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes.
... Approach. We follow a model-driven which performs domain-specific conceptualization (mapping to well-known benefits [25] [17] [10]) on two levels, where the one targets BP users, while the other targets IT service experts. This allows designing domain-specific models capturing suitable domain knowledge on both levels. ...
Conference Paper
Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes.
This chapter presents a domain-specific modelling language BPMN4MoPla, which extends the standard BPMN for the purpose of supporting business decision making in mobility planning. Specifically, requirements for robotic car movements were derived and used for the modelling language extension. To achieve higher support of decision-making in mobility planning, the new modelling method was implemented as a cyber-physical system. The validation of the method was carried out in two complementary ways: (1) with respect to the extended language by modelling a mobility plan use case with BPMN4MoPla and (2) with respect to the expected execution behaviour by transforming and then executing the BPMN4MoPla model in two different modelling environments that are widely used in research and industry, respectively.
The purpose of this study was to examine the role of innovation competitive advantage on strategic orientation dimensions of (entrepreneurial orientation, market orientation, and resource orientation) on the sustainable growth of SMEs. This study used a total of 217 responses adopting the cross-sectional survey research design from manufacturing SMEs owners/founders. The results were analysed using PLS-SEM via SmartPLS version 3.3.3. The major findings established that entrepreneurial orientation did not directly link with the sustainable growth of SMEs. The study confirmed that market orientation and resource orientation revealed a direct and significant positive link with the sustainable growth of SMEs. Similarly, innovation competitive advantage has a direct significant positive association with the sustainable growth of SMEs. Likewise, entrepreneurial orientation and market orientation have a direct and significant positive relationship with innovation competitive advantage. However, resource orientation did not significantly relate to innovation competitive advantage. The findings validated that innovation competitive advantage mediated the relationship between entrepreneurial orientation, market orientation, and the sustainable growth of SMEs. Finally, innovation competitive advantage did not mediate between resource orientation and sustainable growth of SMEs, which have not previously been proven in empirical studies. This study was designed to address the existing gaps and provide reasons to prove them. This study is indeed one of the remarkable studies in the context of SMEs, which integrates these selected variables into a single model. The originality focused on the role of innovation competitive advantage on strategic orientation dimensions and sustainable growth of SMEs that are relevant to Society 5.0.
The ability to properly respond to a biological disaster such as the Covid-19 pandemic is fostered by making knowledge available quickly and consistently. In absence of a vaccine, knowledge about appropriate actions that prevent the spread of the virus (i.e., non-pharmaceutical interventions - NPIs) becomes essential to save lives. This is especially the case in hospitals, as everyone, including healthcare personnel, is exposed to a high risk of infection. Within this context, decision makers about NPIs in hospitals are constantly under pressure. They have to quickly interpret numerous, lengthy and ever-changing regulatory documents, coming from multiple public authorities for the creation of internal security protocols against the spread of the virus. This is a knowledge intensive task calling for support to ensure an efficient response to a pandemic situation. In this paper, a knowledge base is presented, which consists of an ontology, semantic rules and queries about NPIs. The knowledge base was systematically engineered by considering both (a) official regulatory documents of multiple public authorities and (b) expert knowledge from a Swiss hospital. The ultimate goal of this work is to support decision makers in the creation of consistent security protocols about NPIs. The approach is evaluated via the implemented knowledge base which proves that questions relevant for a decision maker are automatically answered.
Conference Paper
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Modern integrated operating rooms (OR) become an enabling technology for new applications in workflow-driven surgical assistance. For this purpose, surgi-cal activities and OR processes have to be described as Surgical Process Models (SPM). There have been approaches for modeling surgical workflows by using common business process modeling languages, like BPMN 2.0. However, BPMN is a general purpose modeling language, which lacks in representation of domain-specific concepts. The goal of this work is to provide a concept and de-sign for a domain-specific extension of the BPMN modeling language for in-traoperative surgical workflow modeling and execution in the integrated OR.
Conference Paper
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The design and management of information systems is driven by model-oriented approaches on different levels of abstraction. For instance, enterprise models link the organizational action system and corresponding information systems. Enterprise models are generally perceived as measures to close the gap between business and IT, as it is in the healthcare domain. Clinical Pathways (CPs) represent value-added processes of hospitals and are typically described by respective process modeling languages like BPMN. However, a solitary focus on processes is insufficient for utilizing the potential of the model. Instead, it is rather advisable to consider various perspectives in order to completely represent the organizational action system of a hospital. We therefore propose to extend clinical process models with accordingly required perspectives for the representation of satellite objects, e.g. medical resources. Based on previous work, this paper motivates the approach of (process-based) integrated hospital modeling and presents the architecture and design of a revised BPMN extension for multi-perspective modeling. The applicability of the proposed BPMN extension is demonstrated by modeling a CP part for the treatment of stroke patients, which explicitly integrates the process and resource perspective.
Conference Paper
Full-text available
Clinical Pathways (CPs) can be seen as business processes of hospitals or clinical institutions. Modeling these pathways is an emerging field of research, as it provides promising benefits for systems integration, quality management and documentation. The Business Process Model and Notation (BPMN) provides a range of process-related concepts but naturally lacks in representing specific aspects from the CP domain. Therefore, the BPMN extension BPMN4CP was designed in a previous research project. In accordance with research guidelines from Design Science, the extension ran through an iteration based on its practical application within a telemedical project. Based on several new requirements, the extension was revised regarding to the integration of resources, documents, objectives and quality indicators. These concepts were assigned to particular perspectives and diagrams in order to support model complexity management and provide appropriate diagrams for respective stakeholders. In order to provide a commonly usable extension, these enhancements were implemented as BPMN meta model extension.
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Domain-Specific Languages are used in software engineering in order to enhance quality, flexibility, and timely delivery of software systems, by taking advantage of specific properties of a particular application domain. This survey covers terminology, risks and benefits, examples, design methodologies, and implementation techniques of domain-specific languages as used for the construction and maintenance of software systems. Moreover, it covers an annotated selection of 75 key publications in the area of domain-specific languages. 1998 ACM Computing Classification System: D.3 Keywords and Phrases: Example DSLs, DSL design, DSL implementation, survey. Note: Work carried out under project SEN 1.5, Domain-Specific Languages, sponsored by the Telematica Instituut. 1 Introduction In all branches of science and engineering one can distinguish between approaches that are generic and those that are specific. A generic approach provides a general solution for many problems in a certain ar...
The elements of an enterprise are managed more and more model-based. The state-ofthe- art in the area of modelling of organisations is based on fixed metamodels. Product models are created by using product modelling environments, process models are created in business process modelling tools and organisational models are realised in personnel management tools. Web service models link these business models to information technology. They are created by using standardised languages and common ontologies. Information technology is modelled in tools supporting notions such as workflow or object-orientation. The models of the company’s strategy, goals and the appropriate measurements are described and monitored by using tools supporting management concepts such as Balanced Scorecard. © Springer-Verlag Berlin Heidelberg 2002.
Conference Paper
In this paper we present a hybrid modeling approach which supports the continuous alignment of business and IT in the cloud. Business Process as a Service provides the end-to-end cloud support for business processes instead of single applications. A graphical modelling environment allows non-technical modelers to design business processes and to specify requirements in human-interpretable way. Via semantic lifting, the graphical models can be annotated with classes and values from an enterprise ontology. The BPaaS Ontology contains the relevant classes for the smart Business and IT-Cloud alignment. It supports the modeler in using a standard terminology and thus ensures consistent modeling.
This chapter describes a modeling method that has been conceived to support learning in public administrations. The modeling method foresees the description of both procedures in the public administrations, and the working context of the civil servants. The approach relies on several model types that are used to organize and to relate the knowledge needed by civil servants in order to perform their daily activities. Each model instance describes a view on the concerns expressed by the model type it conforms to. These descriptions intend to provide an easy way for civil servants to retrieve knowledge when they need to learn specific aspects of a procedure, and to make collaboration easier in order to enable the emergence of knowledge related to the procedures themselves. Indeed, the method comes with an infrastructure that allows to automatically set up a wiki-based collaborative platform enabling collaboration and knowledge sharing among the stakeholders involved in the activities of a Public Administration. This chapter mainly reports on the modeling method that was conceived and developed within the FP7 EU research project Learn PAd. Learning aspects, while clearly relevant for the project, will not be directly discussed here.
This chapter describes the Knowledge Work Designer, a modeling method for knowledge work. It is based on two principles: (1) the separation of business logic and process logic and (2) the support of both structures and unstructured knowledge. Process logic can be represented in a structured way in BPMN and in a nonstructured way with CMMN. For real processes there is no strict separation between structured processes and cases. Therefore, the Knowledge Work Designer offers a deep integration of BPMN and CMMN. Business logic can be represented in a structured way using decision tables. Unstructured business logic can be represented in documents. The separation of business logic and process logic allows for simpler process model and easier maintenance.
Regardless of the application domain, both the analysis of existing systems and the creation of new systems benefit extensively from having the system modeled from a conceptual point of view in order to capture its behavioral, structural or semantic characteristics, while abstracting away irrelevant details. Depending on which relevant details are assimilated in the modeling language, modeling tools may support different degrees of domain-specificity. The boundaries of what domain-specific means are as ambiguous as the definition of a domain—it may be a business sector, a paradigm, or a narrow application area. However, some patterns and invariants are recurring across domains and this has led to the emergence of commonly used modeling languages that incorporate such fundamental concepts. This chapter focuses on the metamodeling approach for the hybridization of BPMN, ER, EPC, UML and Petri Nets within a single modeling method identified as FCML, with a proof of concept named Bee-Up implemented in OMiLAB.