Opportunities for System Dynamics towards the Support of

Technological Developments in Stroke Treatment Domain

Julia Kantorovich

and Jukka Ranta

VTT Technical Research Centre of Finland, Tekniikantie 21, Espoo, Finland

Keywords: System Dynamics, Stroke Diagnosis and Treatment, Technology Developer Support.

Abstract: Data driven solutions can facilitate and enhance stroke diagnostics and at the same time management of stroke

prevention and treatment in a cost-effective way. However, the potential and the utilization of data and AI

analytics in stroke solutions are largely neglected. At the same time, the process to enter to medical domain

for technology developer is not straightforward. There is a need for common vocabularies and design tools to

engage medical professionals in interaction with technologists during the research and development phase to

let them know what is needed. This paper valorises the opportunities for System Dynamics to support

technology developers in the developing of innovative solutions and applications for stroke diagnosis and

treatment. In addition, the value of System Dynamics to support the impact analysis (health outcome, decision

quality, care costs, etc.) and hereby to facilitate the business and market uptake of new innovative solutions

in this domain is demonstrated.

1 INTRODUCTION

Annually, approximately 15 million people

worldwide suffer a stroke with global projections that

the number of stroke survivors will rise to 77 million

by 2030 (Béjot et al., 2016; WSO, 2021). Following

transient ischaemic attack (TIA), at 5 years, the risk

of recurrent stroke is 18.3% and at 10 years following

stroke, the cumulative risk of recurrence is 39.2%,

with higher death and disability noted with recurrent

events. Furthermore, although 10.5% to 18.2% of

patients with TIA will have a stroke within 90 days,

more than 31-61% of the TIA patients are

misdiagnosed (Dawson et al., 2009; Sadighi et al.,

2019). Such high rates of cardiovascular morbidity

and associated disability indicate the need for

effective secondary prevention actions. Moreover,

rapid and accurate diagnosis and treatment of stroke

is important to improve health outcomes. A

significant delay in treatment that may happen due to

misinterpretation of stroke symptoms or inability of a

person to perform necessary follow-up actions, might

cause death, permanent disabilities, as well as more

expensive treatment and rehabilitation.

https://orcid.org/0000-0001-7598-6175

https://orcid.org/0000-0002-1376-542X

There is a technology that has been developed to

address the needs of accurate and rapid diagnosis and

treatment of stroke. The examples of existing

technological solutions are stroke risk calculation

tools, computer-aided first stroke symptoms

recognition software, remote diagnosis- and

rehabilitation which is supported by telemedicine and

mobile solutions (e.g. Chen et al., 2018; Bat-Orgil,

2021). However, the potential of technological

solutions, data combinations and artificial

intelligence (AI) to support more advanced data-

driven decision support in the TIA and stroke

diagnostics are not fully leveraged (Ding et al., 2020;

Ali et al., 2020). Respective improvements have also

a massive business potential. They can facilitate

differential diagnostics, triaging, and management of

cerebrovascular conditions in a cost-effective way.

The Stroke-DATA research (StrokeData, 2020) is

setup to deal with these challenges and to propose a

number of data-driven technological solutions to

reduce the diagnostic time, to improve the outcome of

the diagnosis and secondary prevention as well as to

improve the satisfaction of patients and care-givers,

and effectiveness of overall stroke treatment

processes (see Figure 1).

Kantorovich, J. and Ranta, J.

Opportunities for System Dynamics towards the Support of Technological Developments in Stroke Treatment Domain.

DOI: 10.5220/0010983700003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 743-750

ISBN: 978-989-758-552-4; ISSN: 2184-4305

743

Figure 1: Envisioned Stoke-DATA solutions.

However the experience gained during the first six

months of the project had revealed that this task is far

from straightforward. It can be cumbersome for

technology developers to grasp the complex domain

of TIA- and stroke treatment, to master the needs and

to find a respective niche towards the technological

development and business success. Moreover, it is

also not an easy task to engage medical professionals

into discussion on needs, to demonstrate the ability of

technology, and to prove the impact value of the

proposed solution to various stakeholders involved in

the stroke care processes.

System Dynamics and Systems Thinking models

have potential to connect various stakeholders and to

provide technology developers with means to grasp

the complexity of stroke domain. System Dynamics

is based on Systems Thinking and Group Model

Building principles. Systems Thinking helps to learn

the definitive characteristics of the systems, how

systems fit in a larger context of day-to-today life,

how they behave and how to manage them (Sterman,

2000). Group Model Building has been found to be

a useful method for engaging different

stakeholders to both elicit their perspectives to

address difficult and complex problems and share

those perspectives and expertise (Richardson,

2007).

Consequently, the objective and the first

contribution of this research is to valorise the

opportunities for System Dynamics towards the

supporting of technology developers in the

developing of innovative solutions (applications) for

stroke diagnosis and treatment. The second

contribution of this research is to demonstrate a value

of System Dynamics to support the impact analysis

(health outcome, decision quality, care costs, etc.) and

hereby to facilitate the business and market uptake of

new innovative solutions in this domain.

This paper is organised as follows. Chapter 2

gives an introduction to the stroke domain and its

respective challenges and possibilities for the

technology development. The background related to

the System Dynamics modelling and its application to

stroke treatment are presented in chapter 3. The

identified opportunities and needs for System

Dynamics are also discussed there. The first

modelling efforts are presented to support the

respective discussion in Chapter 4. Chapter 5

concludes the paper, outlining also the aspects of next

steps of research.

2 STROKE AND TECHNOLOGY

A stroke is a medical condition in which poor blood

flow to the brain causes cell death. There are two

main types of stroke: ischemic, due to lack of blood

flow, and haemorrhagic, due to bleeding. Both cause

parts of the brain to stop functioning properly. Signs

and symptoms of a stroke may include an inability to

move or feel on one side of the body, problems

understanding or speaking, dizziness, or loss of vision

to one side. Signs and symptoms often appear soon

after the stroke has occurred. If symptoms last less

than one or two hours, the stroke is a transient

ischemic attack (TIA), also called a mini-stroke

(Donnan, 2008).

Early recognition of stroke is deemed important

as this can expedite diagnostic tests and treatments

and thus reduce the severity of damage. In fact, more

rapid and accurate diagnosis and early preventive

treatment, “the 90-day stroke risk” can be decreased

by 80% after the TIA episode. Accordingly all

attempt and means are needed to decrease the time

from symptom onset to acute stroke treatment.

Advanced age is the most important however

unmodifiable risk factor for stroke, as stroke rates

double for every 10 years of age after the age of 55.

The INTERSTROKE study performed in 22

countries identified 5 risk factors which together

accounted for 80% of the population-attributable-risk

for stroke, namely hypertension, current smoking,

abdominal obesity, poor diet, and lack of physical

activity (O'Donnell et al. 2010).

Furthermore, organization of stroke treatment and

care has advanced beyond stroke units and in-hospital

phase, and it includes multiple overlapping processes

including primary prevention, emergency medical

systems, acute care and rehabilitation, secondary

prevention to avert stroke recurrence and long-term

follow-up supported by public education, community

campaigns, and research.

Technology can potentially support and enhance

health outcomes in all treatment stages, we call them

stroke ‘care path’ stages. For example, in case of

incident of stroke, the sooner a diagnosis is made, the

earlier the treatment can begin and the better the

HEALTHINF 2022 - 15th International Conference on Health Informatics

744

expected outcome is for the patients. An MRI scan is

very useful in detecting ischemic stroke, however it is

usually not available in pre-hospital phase

(emergency room, home) due to its cost. Clinical tests

like the Face Arm Speech Test (FAST) are helpful

tools used by neurologists and trained nurses, but

there may not be professional help immediately

available to conduct the tests.

The computer-aided stroke presence assessment

over facial motion weaknesses and speech inability

for patients with suspicion of stroke showing facial

paralysis and speech disorders in an acute setting

using for example camera and speech recognition

software on mobile phone is one example of

technology proposed by researchers (Khriyenko, et

al., 2018; Yu et. al, 2020). Other examples are the

computerized decision support tools, which are based

on risk scoring and may provide access to expert

advice, improve GPs' diagnostic accuracy (in primary

care setting), limit emergency department referrals of

high-risk patients and prompt GPs to initiate

secondary prevention in case of specialist

consultation is anticipated to be delayed.

Furthermore, numerous smartphone apps are

available that can assist with stroke rehabilitation and

recovery process.

Artificial neural networks are a powerful AI tool

for automatic diagnostics of diseases and has a

potential in decision-making support. Machine

learning and compute vision have been applied in

clinical informatics and have shown commercial

potential in symptom detection and classification

(Wang & Luo, 2016).

Finally, large databases of patient data are being

captured in hospitals which, if accessed, provide a

wealth of information about disease treatment and

prevention. Handling data sets and analysis of data

have become a major growth area of interest globally

(Marshall, 2016). Mobile medical technology is

expanding with multiple diagnostic and monitoring

platforms using mobile app systems which can

require new ways of approaching to data analytics.

Overall, it was predicted that technologies such as

telehealth, eHealth, big data and AI would have

significant 30-50% impact on the improved mortality

rates in acute care cases by 2025 (Polycom, 2015).

Last but not least, data platform economy has

emerged (Baltimore, et al., 2016). There are many

players such as Amazon, Google, Uber that are

making business and creating value with platforms.

Platform economy is based on data, components,

algorithms and applications that are creating an

infrastructure in which the platform-based markets

and ecosystems operate. However, this approach is

not yet fully utilized in the fragmented and highly

regulated healthcare market. In order to be successful

in the business perspective, the technology providers

should either build platform solutions that are

complementary between each other, or platform

solutions that are complementary between the

stakeholder players in their target market.

However, the process to enter to medical domain

of stroke treatment for technology developer is not

straightforward. There is a need for common

vocabularies and design tools to engage medical

professional in interaction with technologists during

the research and development phase and to let them

know what's needed.

System Dynamics is a perspective and a set of

conceptual tools that have been used decades to study

the structure and dynamics of complex systems such

urban and industrial systems (Forrester, 1961, 1969).

Later, System Dynamics has been also leveraged in

other fields including healthcare domain to support to

master the complex health processes, to plan actions

and to affect the respective domain policies.

However, its value to support technology developer

is not yet exploited widely. The related existing

research and the opportunities for System Dynamics

are further discussed in the following (Section 3).

3 SYSTEM DYNAMICS

System Dynamics is a Systems Thinking based

approach for examining how certain things in the real

world change over time. The system’s internal

structure, which is represented by system components

and the cause-and-effect connections among them,

determines the dynamic behaviour of the system and

how it responds to changes (Sterman 2000).

The causal loop diagrams and stock-and-flow

diagrams are used in system dynamics to capture the

interactions between components. Causal loop

diagrams consist of variables connected by arrows

denoting the causal influences among the variables

and the feedback loops, chains of causal links that

balance or reinforce on themselves, in the system.

Stock-and-flow diagrams highlight the

accumulation and flow of information, materials,

financial assets and people in and between the

components, respectively.

Overall, modelling is an iterative process of scope

selection, hypothesis generation, causal

diagramming, quantification, and reliability testing.

Qualitative models can be used to discuss and to

promote structural insights and the behaviours of the

system, thus, quantitative simulations allow users to

Opportunities for System Dynamics towards the Support of Technological Developments in Stroke Treatment Domain

745

see how different choices (selected parameters) lead

to different plausible futures. The models are

powerful tools for communicating across sectors and

for motivating stakeholders to work together to make

systemic changes in their systems. Furthermore,

System Dynamics models can be used to tackle ‘data-

poor’ problems. The information base for the

conceptualisation and formulation of System

Dynamics models can be based on experts’ opinion

and they can be also broader than the numerical

database applied in operations research and statistical

modelling. Group Model Building (GMB) is a tool to

acquire expert knowledge and to identify modelling

needs. Group Mode Building refers to a system

dynamics model building process in which the

stakeholders are actively involved in the process of

model construction that explore questions such as:

what is exactly the problem faced? How did the

problem situation originate? What are the underlying

causes? (Rouwette, et. al., 2020). On the other hand,

system dynamics can become very complex when

real world situations with lots of variables are

modelled. Some issues that may rise are related to the

data availability, domain understanding, and

modelling systems’ boundaries and uncertainties.

The System Dynamics modelling was actively

used in healthcare research to address a range of

issues (Davahli et al., 2020; Darabi et al., 2020), such

as organizing healthy community programs and

policy initiatives, improving processes and costs of

primary and acute healthcare as well as health

equality, developing new approaches for chronic

disease prevention and control, addressing the disease

epidemiology including work in heart disease,

diabetes, HIV/AIDS, cervical cancer and other

diseases. However, the existing effort is very much

dedicated to the medical side of the problem and

towards the enhancement of outcome and quality of

healthcare systems’ processes. The value of

systematic thinking and system dynamics to support

technology developers is not yet exploited. Therefore,

the aim of this study is to address this gap by

valorising the opportunities for System Dynamics to

support the development of new data driven solutions

in stroke treatment domain, more specifically:

 How System Dynamics modelling can support

technology developers in the process of designing

new innovative solutions in the domain of stroke

diagnostics and treatment.

 How System Dynamics modelling can engage

medical professionals in interaction with

technology developers to acquire the needs.

 How System Dynamics approach can support

technology developers in successfully taking their

product to the market and the decision makers in

the process of planning for the procurement of a

new technology.

4 DEVELOPER SUPPORT

As discussed earlier, data driven approaches can

potentially facilitate and enhance differential

diagnostics, triaging and at the same time

management of stroke prevention and treatment in a

cost-effective way. However, the potential of the

mobile solutions and in particular utilization of data

combinations in the TIA and stroke risk evaluation

and diagnostics are largely neglected. At the same

time, possible efficient use of the available data, via

artificial intelligence, in the form of more advanced,

data-driven decision support systems is not yet under

development.

Accordingly, the focus of our first modelling

efforts has been put on improving our understanding

about the role of data in the stroke treatment domain.

We started with domain analysis (facilitated by

literature review and interviewing experts),

consequently the initial models have been created to

valorise the role of the data in various stages of stroke

treatment and care path. At the next step, the Group

Model Building (GMB) Workshop has been

organised to connect medical experts and

technologists and to collect medical experts’- and

technologists’ opinions on first models towards their

adjustment. The aim of GMB was to facilitate the

discussion and obtain more insights on the aspects

related to 1) what overarching data based stroke

treatment tools could be and 2) what is needed for a

data driven tools to become a successful product.

On the first point, more specifically:

What is the valid data to be used in TIA & stroke

diagnostics? What data need to be collected for TIA

& stroke service development? What data sources can

be used? What kind of solution and data combination

would work for TIA & stroke prevention? What is the

required quality of data to support the development of

algorithms to be used in effective stroke diagnostics?

How data is related to stroke ‘care and treatment

quality’ and ‘health outcome’?

On the second market uptake point, more

specifically:

What is needed for a data tool to become a

successful product and what stakeholders are needed

and what kind of ecosystems are to be created? What

will facilitate the adoption of developed solutions by

end users? How shall we orchestrate the connected

health ecosystem for the solution, so that it supports

HEALTHINF 2022 - 15th International Conference on Health Informatics

746

the strategies and creates value for patients, hospitals

and technology providers?

The modelling effort and workshop discussions

have led us to the initial definition of two models

“Data flow” and “Market Uptake” models, which

are discussed in the following.

4.1 Stroke-DATA Models

The model to facilitate planning of data driven

decision tools is presented in Figure 2. As a

conceptual framework it aims to structure discussions

on the relation between existing data, opportunities

for technological solutions, and the eventual value of

a tool. It gives an overview of three distinct layers,

listing from top to bottom: historical data available

for development, features and functionalities of the

tool, care pathway and impact of using the tool.

Different experts tend to have a strong focus on their

own topic. This model and respective discussions

helped them to see there interdependencies and

construct an overview while discussion potential

tools.

The top part summarizes the types of existing data

that can support the design of the tool, in particular

developing the analytics and the underlying

technological infrastructure. Considering a

hypothetical tool, the detailing of the data both places

constraints on the analytics that can be implemented

while design of the tool defines which of the existing

data should be acquired. Focus should not be limited

to constraints imposed by the data but also include

exploration of opportunities in discussion between

the experts, i.e. not only prune out ideas presented by

the technology experts. Actual access to the data and

also level of available detail are of concern as data

security and privacy protection limit how and for

what purposes these data may be used.

Below the data cloud are the specific use cases,

analytics, and functionalities that can provide added

value in the stroke care pathway. A key consideration

here is to bridge the gap between what the available

data can support and what generates added value

when implemented in the care pathway. This is

mainly the domain of expertise of the technology

developers.

The bottom part visualizes the influence of data

availability during care, impact on care quality, and

eventually on health outcomes. The causal effects run

down, from available data to care quality to health

outcome, and to the right, from one care stage to the

next. We are considering a tool based on using data

and analytics to support care both in each stage and to

integrate the care across stage. The purpose of the tool

Figure 2: Conceptual model of factors and their dependencies from available data and eventual impact on health outcomes.

Opportunities for System Dynamics towards the Support of Technological Developments in Stroke Treatment Domain

747

is to facilitate better data availability from patient

records, reflect these to research data via the analytics

and thus improve care quality and outcomes.

The availability of data is dependent on both

linking to various currently used records and also to

similar tools used in previous stages of care, i.e.

recording data and making it available in later stages

of care supports a better basis for decision making.

Such transfer of data involves crossing boundaries

between units and wards within a hospital and also

across organizational boundaries. Health outcomes

are dependent on both previous outcomes and quality

of care in the current stage of care. In particular,

failure to provide appropriate care in earlier stages

can lead to irreparable damage and disability.

The different stages of care had their own focused

discussions in a workshop. More focused diagrams,

such as the one presented in Figure 3, were used. Only

one stage of care is included here, the data and app

feature are compacted, and the orange colour

indicates the desired focus of discussions. As an

example, we present here a summary of the results

from discussion on emergency care. Of the tools and

devices, it was noted that they should add as little as

possible to existing equipment in ambulances and

overall the simplicity of use and streamlining into

current protocol was considered crucial for success.

In particular, value was seen in the seamless

integration to existing systems both in ambulance and

hospital emergency department, and further, linking

the care stages by allowing early information to

doctors in hospital prior to patient’s arrival and

possibility of ambulance crew to consult specialists in

hospital.

Also, value was seen in analytics providing more

reliability in differentiating between stroke vs. TIA

vs. other condition and in case it is a stroke, clot vs.

bleeding - in particular when they would reduce the

needed measurements and imaging and the time to

make decisions.

Figure 3: Model for a single stage in the care-path used

during workshop discussions.

Other areas of potential utility were considered in

helping with triage with awareness of current hospital

resource constraints and linking to the patient’s

historical medical records (especially history of TIA

or stroke). Of outcome measures, mortality and

Cerebral Performance Categories Scale were

mentioned but the topic appeared to be difficult to

bring into discussion while discussing hypothetical

data tool properties. This possibly is a topic that only

surfaces once there is a more concrete plan of a tool

or the tool already exists, i.e. the value proposition on

Figure 4: Model of market update dynamics.

HEALTHINF 2022 - 15th International Conference on Health Informatics

748

that level is something of an afterthought. The more

direct effects a data tool could have on care were more

easily covered in the discussions. Timeliness and

correctness of different decisions were brought up

along with possibility of reacting to weaker

indications before severe symptoms develop.

The market uptake model in Figure 4 is in the

format commonly used in Systems Thinking and

depicts a dynamic causal diagram with factors and

actors that can influence whether the tool is

eventually widely used and profitable for the

members of the business ecosystem. The causal

directional dependencies are represented using

arrows. Colouring of some of the variables indicates

grouping into monetary factors (orange), opinions

and perceptions (magenta), and initial investments

(green).

At the core is the Utility from APP, which

represents the various benefits (and drawbacks)

resulting from using the app (and its data backend

functionality) as a part of the care processes along the

care pathway. The benefits are dependent, first of all,

on the tool being used (Acceptance by professionals)

and then on integrating data (Technical data

integration quality; both historical research data and

case specific patient data) and developing analytics

that serve as basis for decision support functionality

(Investment into analytics development).

Using the app leads to savings in the active care

stage and later life and also to improved quality of

care and thereby health outcomes. Evidence of

improved quality of care and health outcomes

accumulates over time and can lead to positive

compensation decision by the national health

insurance and also more effective marketing to

various healthcare organizations. Though dropped

from the visualization, it was noted in the workshop

that such accumulation of evidence is enhanced by

co-operation also with researchers.

Implementing the app in the healthcare processes

is dependent on both organizations accepting it

(Accept by hc org) and individual professionals

accepting it (Acceptance by professionals), i.e. it is

made available by the organization and the employees

actually use it. As the doctors are respected experts in

their field and often also participate in management,

they have much say in the decisions of the

organizations. Therefore, there is the interplay,

arrows in both directions, of acceptance by healthcare

organization and the professionals. There was a

comment in the workshop that this interplay can vary

between organizations and also over time, which

should be taken into account when planning

marketing activities. Further, the degree to which

professional uses the app depends on how it fits into

existing routines, i.e. Ease of use with existing hc it

systems and Improves existing processes.

Income from licences and subscriptions allow

further investments into developing the tools, which

helps to better meet user needs and improving

outcomes. The income also allows further

investments into marketing, thus widening the user

base. Also, positive and profitable experiences of

cooperation between the business ecosystem leads to

greater Trust among business ecosystem members.

This allows arrangements to open more of the IPR to

the members, thus improving the interaction between

components of the app.

5 CONCLUSIONS

This paper has presented the initial System Dynamics

models to support technology developers in entering

the complex domain of stroke diagnosis and

treatment. The modelling work has been supported by

the Group Model Building activities, where the

medical experts, technology developers and

researchers met to provide expertise and to share open

issues, challenges and opportunities for the

technology development in the stroke treatment

domain. During the workshop it was acknowledged

that the resulted models and the respective

discussions can service various stakeholders in

grasping the emerging important role of data and its

impact on stroke care quality and health outcomes as

well as what makes data tool to become a successful

product and what kind of ecosystems need to be

created to facilitate market uptake.

The level of details presentable in a system model

is coarse with regards to individual variables of the

model. They essentially are constrained to a few

words. On the other hand, a large number of

variables, i.e. detail on how many different things the

model tries to account for, results in a model unusable

in a workshop as the overall big picture is lost. In the

authors’ experience, presenting a preliminary model

based on e.g. interviews helps start the discussion as

it provides something to criticize and expand upon.

However, such initial models need to be sufficiently

simple to be understood after a short presentation.

As for the future work, a set of quantitative

simulations will be developed to support the

examination of care quality outcomes in various

stages of stroke treatment care path. Moreover, the

market uptake model will be quantitatively

instantiated to study the dependences between the

Opportunities for System Dynamics towards the Support of Technological Developments in Stroke Treatment Domain

749

initial technological investments and various impacts

such as care quality and cost savings.

The modelling and workshop with the ecosystem

members aimed to focus and strengthen the

ecosystem. It was brought up in the workshop that,

though the current activities of the ecosystem focus

on the stroke and a specific type of tools, longer time

horizon objectives can cover also other diseases and

tools. Thus the market uptake model could be

expanded or restructured to account for development

of new, as of yet unspecified, tools and applications.

For example, going from an uptake model to

ecosystem evolution model the specific “app” in the

model could be replaced by an evolving portfolio of

offerings from the ecosystem and the definitions of

other components of the model would need to be

redefined accordingly.

ACKNOWLEDGEMENTS

This research was funded by Business Finland under

Stroke-DATA project

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