=Paper=
{{Paper
|id=Vol-3618/forum_paper_16
|storemode=property
|title=Stroke management: defining and assigning goals to stakeholders (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3618/forum_paper_16.pdf
|volume=Vol-3618
|authors=Anouck Chan,Thomas Polacsek
|dblpUrl=https://dblp.org/rec/conf/er/ChanP23
}}
==Stroke management: defining and assigning goals to stakeholders (short paper)==
https://ceur-ws.org/Vol-3618/forum_paper_16.pdf
Stroke management: Defining and assigning goals to
stakeholders
Anouck Chan1,*,† , Thomas Polacsek1,†
1
ONERA, BP74025 - 2 avenue Édouard Belin, FR-31055 TOULOUSE CEDEX 4
Abstract
Some organisations have high level objectives to meet but need a way of knowing how. We present
here an application of a previously published method to a medical case study. This method consists
in assigning sub-goals of a high-level goal to the actors of an organisation in order to guarantee the
satisfaction of the high-level goal. The application is organised in a modelling session with a domain
expert and produces goal models. Feedback from the domain expert on the method is proposed.
Keywords
goal requirements, goal elicitation, goal modeling
1. Introduction
When a patient receives medical care, many different actors are involved for providing the best
possible care. Each actor is part of the continuum of care and may act at different times and for
different reasons. They must therefore adapt to each patient’s situation and to the actions that
other actors have taken or will take in the future. Because of the complexity of the interactions
between the different actors and the technical nature of the care professions, it can be difficult
to have an overall view of the objectives of each actor and to understand how these fit into
the overall objective of patient care. Traditional methods such as lists and brainstorming are
quickly outdated in such a complex situation. In this article we present the application of a
method for eliciting and refining goals to a medical case study : optimal management of stroke in
adult patients The method is presented and applied on aeronautical case study in [1]. It consists
in refining an abstract High-Level Goal (HLG) that an organisation wants to satisfy into concrete
and satisfiable goals. Each concrete goal is assigned to an existing actor in the organisation who
is capable of satisfying it. Furthermore, the satisfaction of all concrete goals ensures that the
HLG is satisfied. The method is based on the knowledge and know-how of a domain expert as
well as goal modelling. We have worked with a domain expert who is a medical doctor with
four years of experience in physical medicine and rehabilitation. The model expert is one of the
authors of this article. The physician’s objectives are (1) to obtain a clear graphical model of a
ER2023: Companion Proceedings of the 42nd International Conference on Conceptual Modeling: ER Forum, 7th SCME,
Project Exhibitions, Posters and Demos, and Doctoral Consortium, November 06-09, 2023, Lisbon, Portugal
*
Corresponding author.
†
These authors contributed equally.
" anouck.chan@onera.fr (A. Chan)
0000-0003-0581-5287 (A. Chan); 0000-0001-9139-7960 (T. Polacsek)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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http://ceur-ws.org
ISSN 1613-0073
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Proceedings
patient’s journey and (2) to identify the range of care needed to manage a stroke. For academic
side, the objectives are (i) to test whether the method can be generalised to an area of case study
different from previous applications and (ii) to produce goal models validated by an expert in
the field of study (i.e. they are accurate representations of the situation studied).
2. Method presentation
In this section, we briefly introduce the method presented in [1] and adapted from [2]. We
start the method with an HLG and a set of actors from the organisation. All the actors in the
organisation want to satisfy the organisation’s goals and are looking for a way to do so. Then,
the HLG is translated into a goal and assigned to an actor in the set. The goal is analysed with
respects to the skills of the actor and then refined into two sub-goals: 𝑔𝑎 which contains the part
of the goal that the actor can satisfy, and 𝑔𝑏 , which contains the rest, such that satisfying 𝑔𝑎 and
𝑔𝑏 induces satisfying the initial goal. The goal 𝑔𝑎 is held by the actor and is labelled satisfiable,
meaning that the actor can satisfy it. The goal 𝑔𝑏 is delegated (i.e. given) to another actor of
the set. This new actor is now solely responsible for satisfying 𝑔𝑏 . Thus, 𝑔𝑏 is examined with
respects to the skills of this new actor, and the process is continued until all goals are labelled
satisfied. All actions (refinement, delegation, labelling) are decided by the experts who are part
of the decision-making entity Global Manager. This method was previously applied to three
HLGs of an aeronautical company.
3. Working sessions
We organised the method application with the domain expert in three workshops conducted
by videoconference. The first session lasted 30 minutes. The aim of this session was to unfold
the algorithm with the domain expert acting as Global Manager. During the session the model
expert used a free hand drawing tool and the domain expert did not have access to this model.
At the end of this session, a first model was built. The second session, also 30 minutes long, was
aimed at reviewing and consolidating the model obtained in the first session. The focus was on
improving the wording of the objectives and clarifying some medical domain aspects. At the end
of this session, the initial model was complete and the goals were clear and unambiguous for all
participants. The third and final session of 10 minutes was dedicated to the final validation of
the model. This session provided an opportunity to reach consensus on the final model, taking
into account any suggestions for improvement made by the domain expert. During the last two
sessions, the domain expert had access to the model, but modifications were only made by the
model expert.
Between sessions, questions were exchanged by written message. At the end of the three
sessions, the domain expert was interviewed to gather her impressions of the models obtained.
4. Resulting model
The final goal model is presented on Figure 1. The initial goal is B0 : optimal management of
stroke in adult patients. A stroke is an interruption of blood flow to part of the brain and can
Figure 1: English translation of the final model obtained for the HLG Optimal management of stroke in
adult patients.
cause irreversible damage to the brain, leading to motor, cognitive impairment and even death.
At the beginning of the method, the Global Manager decides to assign this first objective
to the Relatives actor. Relatives does not have the skills to fully satisfy the goal, so the Global
Manager chooses to refine it into two goals: B1a: alert that Relatives can satisfy, so it is labelled
satisfiable and put in grey in the model. The second objective is B1b: rescue. As B1b contains
elements of B0 that cannot be fulfilled by Relatives, B1b is delegated to another actor, Emergency
Service, chosen by the Global Manager. The algorithm is pursued. At the end of the method, five
actors have received at least one goal : Rehabilitation service, Relatives, Emergency service, Social
Workers and Other medical teams. Twenty-eight actions have been done including labelling
satisfiable for twelve goals.
5. Feedback from the domain expert
The domain expert appreciated the method concepts and more specifically the delegation
mechanism. In her opinion, delegation highlights the importance of each actor in the care
pathway, while emphasising the notion of continuity and collaboration in their role. In addition,
delegation emphasises the transfer of responsibility for achieving objectives between different
actors. In this application, that responsibility is patient care. These two aspects of delegation
were seen by the expert as strengths of the modelling approach used. However, the domain
expert points out that the method “segments things that are not so segmented in real life”. This
segmentation is particularly significant for goal B4b": prevention which is performed in real
life by both Other medical teams and Rehabilitation service. In order to express this very strong
collaboration between the two actors, we have added a collaboration link in the final diagram.
Both objectives of the domain expert are satisfied at the end of the application. About
objective (1), the domain expert mentions that these models could help to improve people’s
understanding of the system. In order to help this objective, a simplified, colourful models and
explanatory cards are provided to the domain expert at the end of the sessions. These elements
can be used as communication tool to a large public. In addition, the expert mentioned that
using the algorithm enabled her to elicit goals better than traditional methods. Which means
that objective (2) is met.
6. Conclusion
This application allowed us to test our algorithm in a medical context, with a real organisation
and an expert in the field studied. It was a completely different domain from our previous
aeronautical applications. Nevertheless, the method was still globally adapted and allowed us
to build a goal model of the situation under study. The objective (i) consisting in testing the
method on a different domain can thus be considered achieved. The final models are validated
by the domain expert who wants to use them professionally. Objective (ii) is therefore met.
However, there were some deviations from the original algorithm over the course of the
sessions. For example, some refinements were made in more than two goals, or they followed
each other without any delegation. It was also sometimes difficult for the domain expert to
distinguish between refinement and delegation, and the division of time. These points need
further investigation and could contribute to the development of our method and algorithm.
Acknowledgments
The authors would like to express their thanks to Barbara Chan, Stéphanie Roussel and Anthony
Fernandes Pires for their support and valuable suggestions.
References
[1] A. Chan, A. Fernandes-Pires, T. Polacsek, Trying to elicit and assign goals to the right
actors, in: J. Ralyté, S. Chakravarthy, M. Mohania, M. A. Jeusfeld, K. Karlapalem (Eds.),
Conceptual Modeling - 41st International Conference, ER, 2022, Proceedings, volume 13607
of Lecture Notes in Computer Science, Springer, 2022, pp. 413–422.
[2] V. Bryl, P. Giorgini, J. Mylopoulos, Designing socio-technical systems: from stakeholder
goals to social networks, Requir. Eng. 14 (2009) 47–70.