=Paper=
{{Paper
|id=Vol-2906/paper1
|storemode=property
|title=Decision-support Simulation of Patient Treatment Process
|pdfUrl=https://ceur-ws.org/Vol-2906/paper1.pdf
|volume=Vol-2906
|authors=Camelia Maleki
|dblpUrl=https://dblp.org/rec/conf/caise/Maleki21
}}
==Decision-support Simulation of Patient Treatment Process==
https://ceur-ws.org/Vol-2906/paper1.pdf
Decision-support Simulation of Patient
Treatment Process
Camelia Maleki[0000−0002−7818−2559]
Ghent University, Department of Business Informatics and Operations Management,
Tweekerkenstraat 2, 9000 Ghent, Belgium Camelia.Maleki@ugent.be
Abstract. The transition from being a medical student, with limited
responsibilities and a high level of supervision, to becoming a medical
intern or newly qualified doctor, with medical responsibility for individ-
ual patients and less supervision, is difficult and stressful. Occasionally
medical trainees experience high levels of uncertainties in diagnosing pa-
tients’ issues and making decisions about proper treatment for them
which may be life-threatening for patients. Therefore preparing medi-
cal interns and junior doctors with the skills necessary to deal with real
medical experiences is crucial. For efficient acquisition of medical knowl-
edge and skill without compromising patient safety, a simulation-based
approach can be considered as an appropriate option. The main contri-
bution of this research lies in developing a decision-support simulation
tool for the patient treatment process to assist medical interns and junior
doctors to transform their theoretical knowledge into practice. To do so,
we propose the combined process mining and data mining techniques to
analyze and discover patient treatment process models to support the
construction of simulation models.
Keywords: decision-support simulation · Process mining · patient treat-
ment process · Knowledge intensive process.
1 Research context
The transition from being a medical student, with limited responsibilities and
a high level of supervision, to becoming a medical intern or newly qualified
doctor, with medical responsibility for individual patients and less supervision,
is difficult and stressful. Especially, being able to identify the patient’s issues,
make decisions, and prepare a plan for the patient is a challenge. Medical interns
and junior doctors experience high levels of anxiety, less confidence in diagnostic
skills and decision-making. Because finding the right diagnosis and initiating a
treatment plan in real cases is different from the student’s perspective, where
decisions seemed more clear-cut. Sometimes these uncertainties in choosing the
right diagnostic tests, prescriptions, and procedures, and the treatment follow-up
put patients lives at risk [1, 2].
Clinical Guidelines (CGs) offer the best practice in medical activities and
play an important role in improving medical quality as well as reducing risks.
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 Camelia Maleki
However, evidence in CG is essentially a form of static knowledge. It captures
the generalities of patients classes and also always assumes necessary resources
are available [3]. But patient treatment process is dynamic and influenced by a
variety of patient-related factors (e.g. patient socioeconomic status, emergency
conditions, patient history, etc.) and features of hospitals (e.g. medical supplies,
equipment, and infrastructures, etc.). Therefore, due to diversity, variability, and
uncertainty of factors in patient treatment process, CG can not cover the whole
process [4, 5]. For efficient acquisition of medical knowledge and skill without
compromising patient safety, a predictive simulation approach can be consid-
ered as an appropriate option [6]. The simulation model is close to the clinical
experience, representing real-life characters involved in the clinical process. It
helps and supports the decision making procedure, reduces costs, increases pro-
cess transparency, and as a final objective provides a good quality of training
for medical trainees and new doctors based on real-world processes without any
harm to patients.[7, 6]
A detailed simulation of the patient treatment process requires modeling
and analysis of this process from multiple points of view such as medical knowl-
edge and decision-making points. Recently, business process management (BPM)
has become to be considered a key valuable asset in the healthcare domain. It
is increasingly adopted by healthcare organizations because it helps improving
healthcare processes by taking into account the increasing complexity in patient
treatment and the continuous reduction of available resources. Various modeling
languages have been developed to cover different types of processes [8]. However
As pointed out by some authors [9, 10], most of the existing BPM methods are
suitable for procedures that can be relatively easily represented in the form of
well-defined stable tasks and activities. But many healthcare processes exhibit
characteristics that pose significant challenges to common process management
techniques. One important such class is Knowledge-Intensive Processes (KIPs)
that are usually unrepeatable, collaboration-oriented, and mostly unpredictable.
Their execution is heavily dependant on various interconnected tasks that are
performed by knowledge workers. We can see numerous examples of KIP in the
patient treatment procedures [11–13].
In recent years, the need to deal with KIPs has emerged as a leading research
topic in the BPM domain, due to the prominent role of knowledge workers in
modern organizations [12]. BPM researchers have recently recognized the need
to extend existing approaches to support KIPs and meet their challenging re-
quirements, like integrating knowledge and decision dimensions with the original
process, which actual BPM frameworks are not able to handle adequately [14].
Process mining is a Business Intelligence (BI) tool that can address some of these
issues by amalgamating the knowledge of information technology and manage-
ment science. Applying process mining in healthcare contributes to extracting
the knowledge of processes and decision points [15]. Unlike many mainstream BI
and data mining tools which are data-centric, process mining is process-centric
and aims to bridge the gap between data mining and BPM. The combination of
both process models and data allows new forms of knowledge-centric process an-
Decision-support Simulation of Patient Treatment Process 3
alytics, which leads to the understanding of the process diversity and complexity,
as well as the real behavior of resources and the patients. [16]. In this study, we
try to establish and promote understanding of the decision support simulation,
KIPs, and nature of the process characteristics to generate forward-looking and
explainable patient treatment process training solutions.
2 Research Goal
The main contribution of this research lies in developing a decision-support sim-
ulation tool for the patient treatment process to assist medical interns and junior
doctors to transform their theoretical knowledge into practice. This prototype
can be complementary to clinical guidelines. To that purpose, we propose the
combined process mining and data mining techniques to analyze and discover
patient treatment process models to support the construction of simulation mod-
els.
3 Challenges
From the technical perspective, applying process mining to healthcare data is
challenging due to data quality, veracity, and complexity [17]. Based on existing
works and literature reviews, the following limitations can be listed :
- In reality, care providers support multiple, simultaneous, diverse pathways
for patients with highly variable personal needs, and many of the interac-
tions, events, and decisions are not stored in information systems.
- Data sources are heterogeneous and hard to use jointly (patient file, vital
signs, medical history)
- Health-care processes (clinical pathways) are inherently variable and un-
structured, therefore performing the process mining on all the available
events inevitably creates incomprehensible spaghetti-like models.
4 Objectives and Research questions
Through this study, we aim to achieve three main objectives which are compre-
hensively discussed in the following sections.
4.1 Objective 1: Determine an integrated decision-driven process
modeling approach
KIPs such as patient treatment flow, require flexibility and scalability in model-
ing, as well as profound integration of data and decisions into the process [18].
The goal of this step is to propose a decision-driven approach to support flexible
KIP healthcare processes. There is a wide range of process modeling languages
available that claim to be able to model this type of process, both imperative
4 Camelia Maleki
ones (i.e. Business Process Model and Notation (BPMN), Petri nets) and declar-
ative ones (Declare and Case Management Model and Notation (CMMN)) [19].
Yet, it is demonstrated that current approaches do not incorporate all the details
needed. More specifically, they are unable to model decision logic, which is im-
portant when attempting to fully capture these processes [20, 21, 14]. The recent
introduction of the Decision Model and Notation standard (DMN) provides an
opportunity for shifting in favor of a separation of concerns between the decision
logic and process model. Decision modeling, and especially DMN, provide an
apt paradigm for representing knowledge-intensive and complex decisions that
are based on multiple inputs and stages [22, 23, 19, 24].
Purpose: In the first step we focus on the identification and documentation
of the KIPs. For this task, we examine both imperative and declarative model-
ing approaches (BPMN, CMMN, and DECLARE) for representing knowledge-
intensive processes. Then to consider the decision logic, the combination of these
languages with DMN as another approach will be investigated. Finally, there will
be a comparison between them based on Knowledge-intensive process ontology
(KIPO) considering the knowledge within their actions, decisions, and specific
working rules [25]. KIPO is based on the Unified Foundational Ontology (UFO)
and is composed of five sub-Ontology that cover different perspectives within a
KIP: Collaborative Ontology (CO), Business Process Ontology (BPO), Business
Rules Ontology (BRO), Decision Ontology (DO), and Knowledge-Intensive Pro-
cess Core Ontology (KIPCO). The following research questions will be answered
for this objective:
RQ1.1: A comparative study of the existing declarative and imperative pro-
cess modeling approaches to identify best possible solutions for modeling KIP
process
RQ1.2: Select a combination of modeling methods to achieve a comprehen-
sive decision-aware process model.
Methodology and steps:
for this objective, a qualitative method is used to model and analyze a real
KIP by various modeling approaches.
- Obtain a clear overview of relevant process modeling approaches from liter-
ature and discuss which certain modeling methods are suitable.
- Define clear modeling rules and guidelines for linking process modeling tech-
niques with DMN to clarifying how the different modeling methods should
be used together as consistent models that cooperate but not obstruct each
other.
- Investigate how the data layer needs to be organized to reach consistency in
the integration of processes, cases, and decisions.
- Evaluate the correlation between process modeling language elements and
KIPO ontology concepts.
- Apply the integrated decision-driven process modeling approach on the knowledge-
intensive patient treatment process.
Decision-support Simulation of Patient Treatment Process 5
4.2 Objective 2: Determine Optimal process mining techniques for
process analysis
Process mining is a relatively young research discipline that sits between com-
putational intelligence and data mining on the one hand, and process modeling
and analysis on the other hand. The idea of process mining is to discover, moni-
tor, and improve real processes (not assumed processes) by extracting knowledge
from event logs. Process mining includes process discovery (i.e., extracting pro-
cess models from an event log), conformance checking (i.e., monitoring deviations
by comparing model and log), social network/organizational mining, automated
construction of simulation models, model extension, model repair, case predic-
tion, and history-based recommendations. The ability to use process mining
techniques for discovering process models and analyzing their performance pro-
vides valuable opportunities for taking advantage of information stored in event
logs. Using these methods not only ensures such procedures can be firmly under-
stood but also generate benefits associated with process efficiency [26]. The most
commonly used algorithms in healthcare for unstructured processes are Heuris-
tics Miner and, Fuzzy Miner. Heuristics Miner is a discovery algorithm that can
generate process models and is very robust in dealing with noise in event logs.
Fuzzy Miner is a configurable discovery algorithm that can generate multiple
models at different levels of detail, helping to deal with unstructured processes
by tuning its parameter [27, 28]. Our strategy for this objective is to evaluate
existing process mining techniques and algorithms to discover novel ways to deal
with KIPs that are flexible, unstructured, complex.
Purpose: For this purpose we suggest four steps: In the first stage event logs
and data need to be extracted from information systems and domain experts such
as medical practitioners. This requires an understanding of ”What can be used
for analysis? In the second stage, the model is constructed by process discovery
techniques and linked to the event log. The discovered process model may already
provide answers to some of the questions and triggers redesign or adjustment
actions. Through the third stage, the relation between an existing process model
is compared with an event log of the same process. This can be used to check if
reality, as recorded in the log, conforms to the discovered model and vice versa.
Ultimately in the fourth stage, The knowledge will be extracted from historical
event data and combined with information about running cases to be used for
decision making and predicts patient treatment path during simulation.
RQ2.1: select a discovery algorithm for obtaining holistic decision and pro-
cess models from recorded data.
RQ2.2: determine a framework to check the conformance of event logs or
the resulting process with the existing real process at hospitals.
Methodology and steps:
- Identify and evaluate existing case studies where process mining has been
applied to healthcare processes.
- Generate a characterization of this project case, including a description of
the most important aspects of main peculiarities of a process such as types
6 Camelia Maleki
of activities, different actors with particular roles, expertise level, knowledge,
etc.
- Select and combine appropriate process mining algorithms and techniques
for process discovery and process analysis.
4.3 Objective 3:Develop Decision support simulation of patient
treatment process
Healthcare process mining presents opportunities for understanding some of the
reality of real patients journeys through care pathways. However, one important
fact about process mining is that it is backward-looking and cannot be used to
answer what if questions. To fill this gap, simulation can help communicate pro-
cess mining discoveries and explore what if scenarios. Simulation offers a vigorous
way to test out variables and potential solutions or changes to a system without
increasing patient risk, wasting precious recourse on untested pilots. This tool
brings new knowledge and allows the evaluation of various scenarios through pro-
cesses [29]. Simulation models are typically created by simulation experts based
on insights from traditional information sources such as process documentation,
interviews, and observations. Issues with these information sources may con-
tribute to the discrepancy between the constructed simulation model and reality
because the perception of the actual process is influenced by the experience of
the human studying it. Moreover, this approach is not easily reproducible as the
model is built on a case-by-case basis [30]. To avoid these biases this study uses
a combination of traditional resources and event logs in simulation construction.
Event loges contain highly relevant information on the actual behavior of the
care process. To extract this information we need process mining techniques. So
Process mining can be used to make better simulation models and to enable the
simulation to run with actual behaviors. On the other hand, simulation can be
used to make process mining more forward-looking and explore different pro-
cess changes. Given the above, it is very natural to combine process mining and
simulation. [29, 31, 32].
Purpose: In this research we use an advanced approach by using a com-
bination of process mining and simulation. process mining techniques are used
to discover a comprehensive simulation model [33]. Meanwhile, a specific form
of decision support approach simulation will be performed based on the current
state of the process [29]. The idea is to enable the incorporation of new data
into an existing simulation model continuously, and thus to allow the model to
dynamically steer the upgrading process. This simulation is like a quick look in
the near future. So it is possible to see what happens if the current situation is
modified (e.g. change in the patient conditions or medical resources). It is also
possible to see the effect of certain decisions in the near future without the need
for any additional modeling effort. [32, 29].
RQ1: Completeness of the simulation in terms of coverage of the behavior
of the patient treatment process.
RQ2: evaluation the quality of simulation as a decision support tool in prac-
tice.
Decision-support Simulation of Patient Treatment Process 7
Methodology and research plan:
This stage will be applied based on the design science research paradigm by
focusing on the design, development, and evaluation of the simulation tool.
- Decide on the purpose of the simulation and what key performance indicators
(KPI) we want to monitor.
- Create a simulation model based on discovered model.
- Evaluate the conformance between the simulated process model and the
process model to compare observed behavior and modeled behavior.
- Simulation performance diagnostics by evaluating specific KPIs proposed in
the first step.
- Select the best alternative based on performance checking results, to continue
or redesign the simulation.
5 Work Plan
The work plan, presented in the three steps, includes the main objectives and
research questions(RQ) to accomplish, along with the publication’s expectations.
Fig. 1. Work plan of the project
Publications Goals
The scientific publication will cover the main areas related to the doctoral
research proposal:
- comparing declarative and imperative modeling languages
- process mining analysis for discovering model
- development of decision support simulation
The initial publications will target doctoral consortium, and more specific
workshops and conferences, which are focused on specific research subjects. this
can provide valuable comments and contributions to the undergoing work. Later,
when the scientific work will be more mature, top-level conferences will become
8 Camelia Maleki
the main objective for publication. At last, in the final stages of the work, journal
publications will be attempt when final and robust results are expected to be
achieved.
6 Conclusion
In conclusion, clinical patient treatment simulation can bridge the gap between
theoretical-medical knowledge and practice by creating a realistic and safe learn-
ing environment for medical students with no or minimal practical experiences
(e.g. interns and residents). The optimal integration of simulation into medical
interns, residents, and newly arrived doctors require additional outcome studies
to determine the effect of simulation-based training on the performance of health
professionals and improving patient outcomes which is the ultimate goal of the
medical profession.
Acknowledgement
This work has been funded by Research Foundation Flanders (FWO) under the
supervision of Prof. Dr. Frederik Gailly, assistant professor at the faculty of
business and economics, Ghent University.
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