Process modelling and mining frameworks have demonstrated their efectiveness across diverse domains, including healthcare. However, existing frameworks often lack explicit guidance on learning from best practices. For instance, the case of Frozen shoulder (FS), a condition with multiple treatment options and varying outcomes. Understanding how care paths difer among patient groups and determining the most efective approach remains a challenge. By identifying this gap, our research employs the Process Mining Project Methodology in Healthcare ( 2) alongside the MIMIC-IV dataset to uncover distinctions in care paths among diferent age groups and genders. This experimental validation seeks to identify optimal strategies for addressing Frozen shoulders through ontological concepts. The study concludes by presenting a set of open challenges, aiming to guide future research in healthcare by integrating ontological concepts to learn from the best and optimal care paths. It is important to note that while this research doesn't ofer a singular solution, it contributes significantly by opening a new dimension of ontological research. Specifically, it delves into how various care paths can be compared and aligned with the help of ontological foundation.
Medical professionals often question whether there is a diference in the treatment procedures
followed by subgroups of patients diagnosed with the same disease [
In the context of information science and knowledge representation, an ontology is a formal
and explicit specification of a shared conceptualization [
This research aims to identify carepath for diferent subpopulations and learn from best
practices. Since age and gender play a role in the development of a disease such as FS [
Therefore, the objective of this research is to find the diferences and similarities between the care paths for diferent subpopulations and learn from best practices with the guidance of ontological foundations.
As an example scenario, we used the MIMIC-IV database and analysed the procedures
followed by FS patients; this database contains data on approximately 300,000 patients that
were admitted to a tertiary academic medical centre in Boston, the USA, between 2008 and 2019
[
This paper is structured as follows. The state of the art will be described in section 2. Section 3 will describe the methodology used with the title of "An example Scenario: Frozen Shoulder Exploration with the Application of Process Mining (PM)". Section 4 will discuss the subpopulation comparison through ontological foundations. Finally, in the last section 5, the paper concludes with opening insightful research directions to the reader.
Process mining techniques can be used for various purposes in the healthcare domain, e.g., with
BPMN diagrams to get the graph edit distances. [
The tool BPMNDif Viz can be used to find graph similarity measures. It takes as input two
BPMN diagrams and gives the minimal graph edit distance (GED) as a result. The GED can
be defined as the minimum number of steps required to transform one graph into another [
Visual comparison can be used to diferentiate between the care paths followed by subgroups
of patients and the tool BPMNDif Viz can be used for that. BPMNDif Viz allows for a choice
between six comparison algorithms: Greedy, TabuSearch, Genetic, AStar, Ants and simulated
annealing. [
more precise and accurate results. The Genetic algorithm only gives an approximation of the
GED [
Subpopulation comparison based on visual aspects or graph edit distances provides us with an initial view. However, the robustness of these comparisons should be strengths beyond the statistical figures for making decisions in real situations, especially in complex domains like healthcare.
Ontological foundation is one of the potential approaches that can be used to ensure the
accuracy of domain structures. Ontological concepts are hierarchical domain structures that
provide a domain theory, have a syntactically and semantically rich language, and a shared and
consensual terminology [
The work of [
In the following section, we will methodically elaborate on a specific scenario to illustrate the
design and comparison of subpopulations. Throughout this example, we will highlight the
potential role that could be played by ontological concepts in shaping and evaluating these
subpopulations. The methodology to be used in this research is called Process Mining Project
Methodology in Healthcare ( 2) [
During this phase, we chose specific subgroups to explore various care paths and organized
the sequence of events. Additionally, we conducted thorough background research on frozen
shoulder and process mining in healthcare, as detailed in the [
In order to get access to and query the MIMIC-IV database, Google Cloud Platform BigQuery2
was used. Since the MIMIC-IV database stores the diagnoses given to the patients at the end
of their ICU stay using the International Classification of Diseases (ICD) Version 9 and 10
codes, the first step was to find the ICD codes associated with frozen shoulder. This was
found in the D_ICD_DIAGNOSES table [
shoulder", "Adhesive capsulitis of unspecified shoulder", "Adhesive capsulitis of right shoulder" and "Adhesive capsulitis of the left shoulder." It is important to note that there is a possibility that a patient is given more than one diagnosis associated with the frozen shoulder in a single hospitalization, e.g., a patient can be diagnosed with both M7501 and M7502.
To apply process mining algorithms to the data, the cases, events, start times and end times have to be defined. For both the subgroup process comparison and bottleneck analysis, a case is a patient’s admission to the hospital and the events are the procedures that the patients were billed for.
Since the start and end times were not stored for the subgroup process comparison, the sequence number was used instead to indicate the order in which the procedures were carried out.
In this phase, the CSV files on the subgroups were entered into ProM, converted into XES files and visualised using the LogVisualiser (LogDialog) plugin. Table 1 gives an overview of the number of cases and events per subgroup, given by the LogDialog. Also, further filtering was required to find the diferences in care paths between the diferent patient groups. This was done using the Filter Log on Event Attribute Values plugin, where specific procedures were ifltered out from the care paths.
This phase involved finding the diferences in the care paths between the diferent subgroups and the bottlenecks in the medications taken and the procedures followed by patients during their ICU stays. To do this, process models were created in ProM3 and Disco4.
The Inductive Miner plugin was chosen because it gives the best fitness, i.e., the degree by
which the process models generated can recreate the cases in the event log [
4https://fluxicon.com/disco/ paths slider set to 0.8. These settings were chosen so that the Petri net and the Inductive visual Miner models are equivalent. Secondly, Mine Petri net with Inductive Miner was used to create static process models that can be used for visual comparison, with a noise threshold of 0.2 to allow for slight deviations. Lastly, in order to convert the Petri net models into BPMN diagrams so that they can be loaded into BPMNDif Viz to get the GED, Convert Petri net to BPMN diagram was used.
The process models created in ProM and Disco for the subgroup process comparison and bottleneck analysis can be found in the author’s GitHub repository5.
When comparing the care paths of the subgroups, three keywords will be used. Firstly, parallel will be used when two procedures occur but the order in which they occur does not matter. Secondly, sequence is used when one procedure follows another. Lastly, exclusive will be used when only one of two procedures can occur.
Also, visual comparison is performed in BPMNDif Viz using the TabuSearch algorithm with
maximum expansions and tabu list size set to 100 as this gives precise results faster than other
algorithms [
Visual comparison was made in BPMNDif Viz for the care paths followed by male and female FS patients, resulting in a final score of 167 using the TabuSearch algorithm. 37% of the elements matched between the care paths, 33% of the elements were deleted and 30% of the elements were added. Table 2 shows the procedures that are only performed on either female or male FS patients, but not both.
The procedure "Other repair of shoulder" can be done in parallel with "Division of joint capsule, ligament, or cartilage, shoulder" in male patients while in female patients, these procedures are performed in sequence. Furthermore, it is performed in sequence with "Rotator
localhost:8080/ru_pais_vkr_war/comparison/fourth_step 1/2 localhost:8080/ru_pcais_uvkr_wfar/rcomeparispon/fouarth_sitepr" in male FS patients. However, in fe1/2male patients, these processes are exclusive. This can be seen in figures 1a and 1b.
As can be seen in figures 1a and 1b, the procedure "Synovectomy, shoulder" is always the last process in male FS patients, in case it is performed. In female patients, it is exclusive to "Rotator cuf repair", while in male patients, they can occur in sequence, where "Rotator cuf repair" is the first procedure and "Synovectomy, shoulder" is the last procedure to take place.
In this phase, the insights obtained in the previous phase were used to suggest improvements and learn from care path by considering the best practices. In this phase, the stakeholders, e.g., medical professionals, decide on the path to be followed to implement the improvements.
This phase was conducted with an expert physiotherapist at Fysiotherapie Polman in Enschede, The Netherlands, in order to discuss and evaluate the results of this research. Thereby, based on the discussion of the results found with the physiotherapist, which is based on his 8 years of experience working as a physiotherapist for FS, more insight was gained on patients of FS. In particular, there is a ratio of approximately 7:3 between female and male FS patients. This could be because female patients ask for help earlier on. Based on his experience, there is no diference in the care paths followed by male and female FS patients. Also, it was mentioned that the age group between 40 and 60 years old is more prone to developing FS and this applies to both genders. Furthermore, older people, i.e., those aged above 60, are more likely to experience FS after shoulder trauma. In this age group, they are less likely to get surgery as it is an invasive procedure. In general, depending on the health conditions of the patient, the older they are, the more they are at risk of developing complications.
Section 3 of our research shows how diferent care paths of subpopulations derived using process mining and how they compare using BPMNDif Viz tool. Subpopulation comparison based on visual aspects or graph edit distances provides us with an initial view. There is no doubt, that we can argue the robustness of these comparisons in terms of statistical figures. However, the question is, whether these statistical figures are suficient for making decisions in real situations, especially in complex domains like healthcare. Ontological foundation is one of the good approaches that can be used to ensure not only the structural correctness but also the accuracy of domain knowledge in the derived models.
In our research case study, we analyzed the treatment procedures and care paths for FS within two distinct subpopulations. The central focus of this study revolves around understanding the variations in care paths among diferent patient groups and determining the most efective approach. Naturally, the expertise of domain professionals serves as the primary and most iftting source of knowledge for these investigations. Secondly, the ontological foundation can be employed to determine the best care path. Surprisingly, research work is scarce on using ontologies for comparing (assessing similarities or diferences) diferent care paths based on subpopulations.
To address this, our case study explores a research direction on establishing a method for comparing subpopulations within a given knowledge domain, along with defining appropriate evaluation criteria. These criteria encompass the ontological richness and the reliability of methodologies in conceptualization, shareability in terms of sources and granularity, explicitness and formality through implementation tools and formalization language, and adherence to design criteria within the methodological process of building ontologies.
In essence, our proposal leverages the significance of the ontology definition as a foundation for comparison features, ensuring a comprehensive evaluation that goes beyond traditional similarity metrics.
The use of ontology in subpopulation comparison involves various dimensions. Ontologies prove beneficial in comprehending and conceptualization. Below are key discussion points highlighting the ways in which ontology can be utilized for comparing subpopulations. • Conceptual Clarity: Ontologies help to define and clarify the concepts related to subpopulations. By establishing a common understanding of terms, attributes, and relationships, ontology ensures clarity in the representation of diverse sub-groups. • Semantic Interoperability: Ontological representations facilitate semantic interoperability, allowing for the integration of diverse data sources and the comparison of subpopulations across diferent datasets. This is crucial for ensuring consistency and accuracy in comparisons. • Granular Attribute Definition: Ontologies allow for the granular definition of attributes associated with subpopulations. This includes demographic information, medical conditions, or any relevant factors. This granularity enhances the precision of comparisons. • Relationship Modeling: Ontologies capture relationships between entities, enabling the modelling of complex interactions within subpopulations. This is particularly valuable when comparing the influence of diferent factors on health outcomes or other relevant criteria. • Automated Inference: Ontologies support automated reasoning and inference, allowing for the deduction of additional information based on the defined relationships. This capability aids in uncovering hidden patterns or correlations within subpopulations. • Consistent Terminology: Ontologies promote the use of consistent and standardized terminology, reducing ambiguity in the description of subpopulations. Consistency in terminology is crucial for accurate and meaningful comparisons. • Facilitating Data Integration: Ontologies provide a common framework for integrating data from diverse sources, making it easier to compare subpopulations across diferent studies or datasets. This promotes a more comprehensive understanding of variations and similarities. • Enabling Query and Retrieval: Ontologies enhance the eficiency of querying and retrieving relevant information about subpopulations. Researchers can formulate queries using ontological terms, streamlining the comparison process.
While we acknowledge that this list may not be exhaustive, it represents our initial efort to address the multifaceted nature of this complex research. In essence, ontology serves as a powerful tool in subpopulation comparison by ofering a structured, standardized, and semantically rich representation of entities and their relationships. This approach contributes to more meaningful, accurate, and eficient comparisons across diverse subsets of a population.
We would like to thank the physiotherapist at Fysiotherapie Polman in Enschede for sharing important insights on frozen shoulder.