Supporting Governance in Healthcare Through Process Mining: A Case Study

Healthcare organizations are under increasing pressure to improve productivity, gain competitive advantage and reduce costs. In many cases, despite management already gained some kind of qualitative intuition about inefciencies and possible bottlenecks related to the enactment of patients' careows, it does not have the right tools to extract knowledge from available data and make decisions based on a quantitative analysis. To tackle this issue, starting from a real case study conducted in San Carlo di Nancy hospital in Rome (Italy), this article presents the results of a process mining project in the healthcare domain. Process mining techniques are here used to infer meaningful knowledge about the patient careflows from raw event logs consisting of clinical data stored by the hospital information systems. These event logs are analyzed using the ProM framework from three different perspectives: the control flow perspective, the organizational perspective and the performance perspective. The results on the proposed case study show that process mining provided useful insights for the governance of the hospital. In particular, we were able to provide answers to the management of the hospital concerning the value of last investments, and the temporal distribution of abandonments from emergency room and exams without reservation

Clinical Interactions in Electronic Medical Records Towards the Development of a Token-Economy Model

The use of electronic medical records (EMRs) plays a crucial role in the successful implementation of the Universal Healthcare Law which promises quality and affordable healthcare to all Filipinos. Consequently, the current adoption of EMRs should be studied from the perspective of the healthcare provider. As most studies look into use of EMRs by doctors or patients, there are very few that extend studies to look at possible interaction of doctor and patient in the same EMR environment. Understanding this interaction paves the way for possible incentives that will increase the use and adoption of the EMR. This study uses process mining to understand simulated doctor-patient interaction, with the goal of developing interaction features and a token economy framework to increase EMR adoption. Results from the process mining showed that current EMR interaction remains low, and highlighted the need for interaction features to promote preventive healthcare. Moreover, process mining from the simulated logs showed that consistency and time are important factors in encouraging usage. Activity category, relative frequency of activity, relative case frequency of activity and average time spent on activity are features that may serve as the foundation for a token economy framework for EMRs

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

An analysis of students’ behaviour in a Learning Management System through Process Mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe exponential growth and transformation of the Internet and information technology in recent years led to the development of several analytical tools. As is the case with process mining, it emerged to fulfill the need to extract and analyze information from event logs by representing it in the form of process models. Process mining is an acclaimed tool and proved crucial in several areas, from healthcare to manufacturing and finance. Nevertheless, and despite the crucial role of digital systems in supporting learning activities and generating large amounts of data about learning processes, limited research focused on process mining applied to the educational context. Therefore, the aim of this dissertation is to apply a process-oriented approach and demonstrate the applicability of process mining techniques to explore and analyze students’ behavior and interaction patterns, based on data collected from Moodle, the widely used Learning Management System. We cover definitions of process mining, education, and a detailed search of the existing literature on educational process mining during this work. Furthermore, the paper analyzes and discusses the findings of the study that combines process mining techniques, specifically process discovery implanted in the Disco tool, with cluster analysis. Through the application of these two techniques, it was possible to recognize the relationship between the students’ behavior registered in the process models and the success of the students in the course, along with the general and specific information about the students’ learning paths. Besides, we obtained findings that allow us to predict the group of students at risk of failing. Finally, with the analysis of these results, we were able to provide improvement proposals and recommendations to enhance the learning experience

Seeing the Signs of Workarounds: A Mixed-Methods Approach to the Detection of Nurses’ Process Deviations

Workarounds are intentional deviations from prescribed processes. They are most commonly studied in healthcare settings, where nurses are known for frequently deviating from the intended way of using health information systems. However, workarounds in healthcare have only been studied using qualitative methods, such as observations and interviews. We conduct a case study in a Dutch hospital and use a mixed-methods approach that draws not only on interviews and observations, but also on process mining, to detect and analyse eight workarounds that occur in a clinical care process. We contribute to theory by demonstrating that it is possible to use data to determine the occurrence of a rich variety of workarounds found using qualitative methods. Practically, this implies that workarounds that are identified qualitatively can be further analysed and monitored using quantitative methods. Once identified, workarounds also provide an attractive starting point for organisational learning and improvement

The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database

There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models

Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining

[EN] In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations.This project was partially funded by Fondecyt Grants 1150365 and 11130577 from the Chilean National Commission on Scientific and Technological Research (CONICYT), the Ph.D. Scholarship Program of CONICYT Chile (CONICYT-Doctorado Nacional/2014-63140180), the Ph.D. Scholarship Program of CONICIT Costa Rica and by Universidad de Costa Rica Professor Fellowships.Rojas, E.; Sepúlveda, M.; Munoz-Gama, J.; Capurro, D.; Traver Salcedo, V.; Fernández Llatas, C. (2017). Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining. Applied Sciences. 7(3):1-29. https://doi.org/10.3390/app7030302S12973Welch, S. J., Asplin, B. R., Stone-Griffith, S., Davidson, S. J., Augustine, J., & Schuur, J. (2011). Emergency Department Operational Metrics, Measures and Definitions: Results of the Second Performance Measures and Benchmarking Summit. Annals of Emergency Medicine, 58(1), 33-40. doi:10.1016/j.annemergmed.2010.08.040Jansen-Vullers, M., & Reijers, H. (2005). Business Process Redesign in Healthcare: Towards a Structured Approach. INFOR: Information Systems and Operational Research, 43(4), 321-339. doi:10.1080/03155986.2005.11732733Grol, R., & Grimshaw, J. (1999). Evidence-Based Implementation of Evidence-Based Medicine. The Joint Commission Journal on Quality Improvement, 25(10), 503-513. doi:10.1016/s1070-3241(16)30464-3Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Radnor, Z. J., Holweg, M., & Waring, J. (2012). Lean in healthcare: The unfilled promise? Social Science & Medicine, 74(3), 364-371. doi:10.1016/j.socscimed.2011.02.011Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Neumuth, T., Jannin, P., Schlomberg, J., Meixensberger, J., Wiedemann, P., & Burgert, O. (2010). Analysis of surgical intervention populations using generic surgical process models. International Journal of Computer Assisted Radiology and Surgery, 6(1), 59-71. doi:10.1007/s11548-010-0475-yFernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. 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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

A Multi-level Approach for Identifying Process Change in Cancer Pathways

An understudied challenge within process mining is the area of process change over time. This is a particular concern in healthcare, where patterns of care emerge and evolve in response to individual patient needs and through complex interactions between people, process, technology and changing organisational structure. We propose a structured approach to analyse process change over time suitable for the complex domain of healthcare. Our approach applies a qualitative process comparison at three levels of abstraction: a holistic perspective summariz-ing patient pathways (process model level), a middle level perspective based on activity sequences for individuals (trace level), and a fine-grained detail focus on activities (activity level). Our aim is to identify points in time where a process changed (detection), to localise and characterise the change (localisation and characterisation), and to understand process evolution (unravelling). We illus-trate the approach using a case study of cancer pathways in Leeds Cancer Centre where we found evidence of agreement in process change identified at the pro-cess model and activity levels, but not at the trace level. In the experiment we show that this qualitative approach provides a useful understanding of process change over time. Examining change at the three levels provides confirmatory ev-idence of process change where perspectives agree, while contradictory evidence can lead to focused discussions with domain experts. The approach should be of interest to others dealing with processes that undergo complex change over time