A Data Analysis Methodology for Process Diagnosis and Redesign in Healthcare

Department of Management EngineeringDespite the disruptive and continuous development of healthcare environments, it still faces numerous challenges. Many of these are connected to clinical processes within the healthcare environment, which can be resolved through process analysis. At the same time, through the digitalization of healthcare, information from the various stakeholders in hospitals can be collected and stored in hospital information systems. On the basis of this stored data, evidence-based healthcare is possible, and this data-driven approach has become key to resolving medical issues. However, a more systematic data analysis methodology that covers the diagnosis and the redesign of clinical processes is required. Process mining, which aims to derive knowledgeable process-related insights from event logs, is a promising data-driven approach that is commonly used to address the challenges in healthcare. In other words, process mining has become a way to improve business process management in healthcare. For this reason, there have been numerous studies on clinical process analysis using process mining. However, these have mainly focused on investigating challenges facing clinical processes and have not reached a virtuous cycle until process improvement. Thus, a comprehensive data analysis framework for process diagnosis and redesign in healthcare is still required. \ud We identify three challenges in this research: 1) a lack of guidelines for data analysis to help understand clinical processes, 2) the research gap between clinical data analysis and process redesign in healthcare, and 3) a lack of accuracy and reliability in redesign assessment in healthcare. Based on these problem statements, this doctoral dissertation focuses on a comprehensive data analysis methodology for process diagnosis and redesign in healthcare. In particular, three frameworks are established to address important research issues in healthcare: 1) a framework for diagnosing clinical processes for outpatients, inpatients, and clinical pathways, 2) a framework for redesigning clinical processes with a simulation-based approach, and 3) a framework for evaluating the effects of process redesign. The proposed methodology has four steps: data preparation, data preprocessing, data analysis, and post-hoc analysis. The data preparation phase aims to extract data in a suitable format (i.e., event logs) for process mining data analysis. In this step, a method for obtaining clinical event logs from electronic health record data mapped using the common data model needs to be developed. To this end, we build an event log specification that can be used to derive event logs that consider the purpose, content, and scope of the data analysis desired by the user. After compiling the event logs, they are preprocessed to improve the accuracy and validity of the data analysis. The data analysis phase, which is the core component of the proposed methodology, consists of three components for process mining analysis: clinical process types, process mining types, and clinical perspectives. In the last phase, we interpret the results obtained from the data analysis with domain experts and perform a post-hoc analysis to improve clinical processes using simulations and to evaluate the previous data analysis results. For the first research issue, we propose a data analysis framework for three clinical process types: outpatients, inpatients, and clinical pathways. For each category, we provide a specific goal and include suitable fine-grained techniques in the framework which are either newly developed or based on existing approaches. We also provide four real-life case studies to validate the usefulness of this approach. For the second research issue, we develop a data-driven framework in order to build a discrete event simulation model. The proposed framework consists of four steps: data preparation and preprocessing, data analysis, post-hoc analysis, and further analysis. Here, we propose a mechanism for obtaining simulation parameters from process mining analysis from a control flow and performance perspective and automatically build a reliable and robust simulation model based on these parameters. This model includes realistic arrival rates and service times in a clinical setting. The proposed framework is constructed with a specific goal in mind (e.g., a decrease in waiting times), and the applicability of the framework is validated with a case study. For the final research issue, we develop a framework for evaluating the effects of process redesign. Two types of indicators are used for this: best practice implementation indicators to assess whether a specific best practice has been applied well or not and process performance indicators to understand the impact of the application of best practices. These indicators are explicitly connected to process mining functionalities. In other words, we provide a comprehensive method for assessing these indicators using clinical event logs. The usefulness of the methodology is demonstrated with real-life logs before and after a redesign. Compared to other existing frameworks in healthcare, this research is unique in constructing a healthcare-oriented data analysis methodology, rather than a generic model, that covers redesign in addition to diagnosis and in providing concrete analysis methods and data. As such, it is believed that this research will act as a motivation to extend the use of process mining in healthcare and will serve as a practical guideline for analyzing and improving clinical processes for non-experts.clos

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Optimising cardiac services using routinely collected data and discrete event simulation

Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces

Ontology reuse and synthesis for modelling and simulation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe proliferation and ubiquity of SemanticWeb technologies have transformed the way computer society reshapes its technology through knowledge integration, knowledge reuse and knowledge sharing. Ontology, one of the Semantic Web components, is a way to represent domain knowledge into a human-understandable and machine-readable format. Ontology in simulation has been seen as a conceptual model of a system in an explicit and unambiguous manner, where it can be applied to better capture the modeler’s perspective of the domain. Regarding an ontology for simulation modeling, by reusing ontologies, it helps to reduce time and effort in attaining the domain knowledge, and at the same time assist in domain understanding. For a semantically-richer simulation ontology, it is useful to engage with real data and existing ontologies. This research contributes a rigorous method that extracts domain knowledge, synthesizes processes performed within the domain, and builds a minimal and viable ontology for simulation modeling, knownas aMinimal Viable Simulation Ontology (MVSimO). The research method initially applies ontology selection techniques in Ontology Reuse Framework (ORF) to obtain suitable existing ontologies for reuse. ORF incorporates a module extraction technique during the domain conceptualization phase, where the modules will represent domain knowledge as sub-ontologies. Formal Concept Analysis is later applied to the real-world data to reveal the process details of the domain. Finally, the development of MVSimO is completed by the derivation of event semantic of the processes. The effectiveness of ontology selection and synthesizing methods, is reviewed by evaluating the selected ontology knowledge extracted, and the detailed ontological model of MVSimO. The evaluation of,MVSimO is performed to determine its agreement to the established simulation model of the domain. The evaluation results are encouraging, providing concrete outcomes of the new technique of ontology reuse and new development to the research area

Emergent Behaviors in a Resilient Logistics Supply Chain

This PhD dissertation addresses vulnerabilities in logistics supply chains, such as disruptions from pandemics, natural disasters, and geopolitical tensions. It underscores the complexity of supply chains, likening them to socio-technical systems where resilience is key for managing unexpected events and thriving amidst adversity. The focus is on leveraging smart business objects—exemplified by “smart pallets” with sensing and computational capabilities—to augment real-time decision-making and resilience in supply chains. When strategically positioned within the supply network, these smart pallets can provide key insights into the movement of goods, enabling a rapid response to disruptions through real-time monitoring and predictive analytics. The dissertation investigates centralized, decentralized, and hybrid approaches to decision-making within these networks. Centralized methods ensure uniformity but may neglect local specifics, while decentralized ones offer adaptability at the risk of inconsistency. A hybrid model seeks to balance these extremes, combining broad guidelines with local autonomy for optimal resilience. This research aims to explore how such smart objects can anticipate and react to emergent behaviors, thereby augmenting supply chain resilience beyond mere performance indicators to actively managing and adapting to disruptions. Through various chapters, the dissertation offers an exploration, from designing resilient architectures and evaluating business rules in real-time to mining these rules from data and adapting them to evolving circumstances. Overall, this work presents a nuanced view of resilience in supply chains, emphasizing the adaptability of business rules, the importance of technological evolution alongside organizational practices, and the potential of integrating novel techniques such as process mining with multi-agent systems for better decision-making and operational efficiency