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Cardiology departments receive many outpatients from primary care services and it is necessary to differentiate which patients need special attention. One-stop clinics were deployed in a hospital in Salamanca (Spain) to triage such patients, separating those who needed further examination and those who were discharged.Data (covering December 2018—August 2020) was explored and there was an iterative process in which clinicians, process miners and technical staff at the hospital interacted in special interviews or Data Rodeos. Interactive Process Indicators (IPIs) were generated. During Data Rodeos data quality problems arose and were tackled, input data was cleaned and preconditioned, process activities were discovered and modelled.The original assumption that the iterative implementation of the IPI would allow clinicians and managers to have a deeper understanding of the one-stop cardiology clinics process, was evaluated and validated by them. After each iteration, they found that the IPI was more useful and near to the reality they see everyday.The final IPI was easy to interpret by the clinicians. In the end, many key indicators were extracted, but most importantly, clinicians had a comprehensive tool that they could use by themselves, without technical assistance, to extract and interpret different indicators at any time, providing a high-quality source of information to improve patient-centered daily medical care.

The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.

The way patients are treated in Hospital Emergency Departments changes during the year, depending on many factors. One key component is weather temperature. Some seasonal maladies are tightly related to temperature, such as flu in cold weather or sunburn in hot weather. In this study, data from a hospital in Valencia was used to explore how harsh weather changes affect the emergency department, obtaining information about probable impacts of global warming effects in healthcare systems. Process mining techniques helped in the discovery of changes in the Emergency Departments. Some illnesses, such as heat stroke, are more prevalent during heatwaves, but more interestingly, the time to attend patients is also higher. Rapid changes in temperature are also analyzed through Process Mining techniques.

Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents&rsquo, health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident&rsquo, s activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.

Introduction The expected delay of transport between patient location and percutaneous coronary intervention (PCI) centre is paramount for choosing the adequate reperfusion therapy in ST-segment elevation myocardial infarction (STEMI). The central region of Portugal has heterogeneity in PCI assess due to geographical reasons. However, this data is usually presented numerically without providing a visual distribution of patients. Purpose We aimed to analyse the impact of distance to PCI centres on mortality in patients with STEMI through visual maps of patients' flow by using an experimental process mining tool, integrated in EIT Health's project PATHWAYS. Methods Using the Portuguese Registry of Acute Coronary Syndromes (ProACS), we retrospectively assessed patients with an established diagnosis of STEMI, geographical presentation specified, reperfusion option identified (PCI, fibrinolysis or no reperfusion), short-term outcomes defined as discharge or in-hospital death. With the 2 317 patients that fulfilled the criteria, we used a process mining tool to build national and regional models that represent the flow of patients in a healthcare system, enhancing differences between groups. Results Colour gradient in nodes and arrows changes from green to red, with green representing a lower number of patients as opposed to red. In the national model, most patients from all regions had PCI. Mortality was similar between PCI and fibrinolysis groups (4%) but higher in those without reperfusion (9%). In the central region model, one third of the patients were more than 120 minutes away from a PCI centre. Despite that, almost one third of these patients had PCI instead of fibrinolysis. In this model, fibrinolytic therapy had higher in-hospital survival rate than PCI (98% vs. 94%). Overall mortality was higher in the central model compared with the national model (6.92% vs. 5%). Central region had less PCI (53% vs. 73%), more fibrinolysis (15% vs. 7%) and more patients with no reperfusion (32% vs. 20%). Conclusion In the ProACS registry, mortality was higher in the central region compared with national data. Even though global interpretation of these findings is limited by underrepresentation from certain central areas, process mining offers an easily understandable view of patients flow. With its statistical upgrade and continuous development, this tool will facilitate the analysis of big data and comparison between groups. Funding Acknowledgement Type of funding source: Public grant(s) – EU funding. Main funding source(s): EIT Health

This chapter is a descriptive use case of the Interactive Process Mining paradigm in one of the most significant problems of healthcare in relation to the management of chronic conditions, Type 2 Diabetes Mellitus. The chapter firstly performs a comprehensive description of the disease, its origins, evolution and standard of care, to thereafter adapt the concepts of Interactive Process Mining to trigger big quantities of heterogeneous observational data into actionable information for patient’s evaluations. The use case is focused in the analysis of Glycated Hemoglobin, a major indicator of blood glucose levels and a key factor in the management of the disease. The chapter is a preliminary approach to the application of Interactive Process Mining in healthcare

The Emergency Department is one of the most critical areas that exist in a hospital. Emergency Department is usually formed by a set of professionals that offers multidisciplinary assistance due to the high variability of patients that can be treated. The application of Interactive Process Mining methodologies and technologies can offer a new perspective for analyzing the aspects that should be taken into account to understand the behaviour of the processes in an Emergency Department. In our study, we have analyzed different Key Performance Indicators that are being usually used in Emergency Department. From that, we have offered a new view, using Interactive Process Indicators, that not only offers the required numerical view but also offers a process-oriented view that supposes a quick and understandable way for discovering the reasons behind of these indicators.

In this chapter we analyse how interactive models and Process Mining can be used for creating Dynamic Risk Models as a new way to stratify the patient’s behaviour with chronic conditions. Chronic conditions are long duration diseases that require ongoing management over a period of years or decades, so individuals’ behaviour should be taken into consideration. This is now possible thanks to the enormous amount of available data in medical systems. Current understanding of risk models for chronic diseases, relies on static snapshots of variables or measures, rather than ongoing, dynamic feedback loops of behaviour considering changes and different states. However, diseases are not static, they evolve towards different destinations, especially when talking about chronic health problems. Following the Interactive Process Mining paradigm, it is proposed a method for discovering different dynamic risk models for chronic diseases, based on the stratification of individuals’ behaviour. As a result, there were created two different IPIs for two chronic conditions, obesity and hypertension, with the goal of understanding diseases dynamic processes.

In this chapter, the Interactive Process Mining framework is presented with a focus on the advantages of the combination of the paradigms and how it can support new challenges in the healthcare domain. Interactive Process Mining is an emerging paradigm that offers a broad range of capabilities in the health field. This framework promotes a data-driven approach, as in machine learning techniques, but offering a human understandable view like knowledge-driven techniques, mixing the best parts of both worlds.

Putting in the Interactive Process Mining paradigm into practice in the medical domain requires the use of tools, algorithms, and methodologies that not only leverage the presence of the expert in the loop but also, achieve the engagement of the expert by providing process views that can be human readable in the easiest possible way. In this chapter, we analyze some available frameworks, techniques, and algorithms in the process mining literature and provide guidelines based on the multidisciplinary interaction of health professionals to be prepared to offer the best process indicators in the most optimal way.

In previous chapters, we saw how Interactive Process Mining (IPM) is key in discovering models from reality, while having a way of validating those models by the expert. In this chapter, we will show how vast amounts of real-time data, obtained through Internet of Things (IoT), may be used in order to create accurate models of human behaviour. This enables the classification of behaviour with different patterns by their heuristic distance. Data from a subject’s location at home across 70 days is used in order to show different types of conduct. The specific classification in groups is shown through workflows to the user, who will be able to validate the behavioural models. A calendar view of the groups, along with maps with the difference between behaviours are also shown. The power to show fine grain information about the subject’s conduct with data from IoT is shown.

Healthcare is a challenging field. The solution is not to provide the most accurate data science models at whatever price. There is a need for creating medical evidence, but there is no medical evidence if they are not understandable by health professionals. In this scenario, Interactive Process Mining methodology supposes a clear advantage over other data science methodologies due to its capability to discover, and present the real medical information in a human-understandable way. However, to leverage this advantage it is crucial to provide the correct tools, algorithms and frameworks to deepen in the necessities of health professionals providing effective solutions in daily practice. The objective of this chapter is to analyze the barriers that are hindering the interaction between medical experts and intelligent systems and highlight the new challenges that we should face up to achieve a more adequate digital health transformation.

Since the emergence of the Evidence-Based Medicine paradigm, the formalization of medical processes is one of the big aims for the standardization of healthcare. In the literature, there are different approaches to the definition of these processes. On one hand, knowledge-based Clinical Decision-Making technologies provide tools for formalizing specialized knowledge as described in clinical guidelines and textbooks, or stated by clinical experts. On the other hand, Clinical Process Management technologies, rely on a data-driven approach that can infer medical processes from data available in healthcare databases. This chapter aims to analyse these two prominent approaches for supporting clinical experts in the representation of medical processes, in search of a solution that takes advantage of both, and towards a new way of building formalized medical processes in a more efficient, precise, and usable way.

The use of Real Time Location Systems in combination with Process Mining solutions can produce precise and unobtrusive methods for discovering and analyzing models in hospitals. However, noisy environments and building structures can interfere with RTLS signals and can affect their accuracy. This noise produces an undesirable effect on Process Mining algorithms called Spaghetti Effect. This effect dramatically decreases the human understandability of Workflows produced by the Process Mining discovery algorithms. The use of interactive paradigm for supporting the semi-automated correction of this data can suppose a solution to this problem. In this work, we present an Interactive Trace Correction method based on Error-Correcting (EC) paradigm and using an Edit Distance framework, to correct the data and obtain an insight of the executed process. This method was tested in a surgical area of a hospital in spain using the existing RTLS infrastructure deployed in that environment with 3915 patients collected between March 2012 and May 2013.

To support health professionals in their daily practice it is necessary to provide indicators that show the adequate information to experts that allow them to understand their processes. Experts usually used number-based Key Performance Indicators to have an idea of the behavior of the deployed protocols. In this chapter, we propose Interactive Process Indicators (IPIs) as a solution to the problem of the exaggerated reduction of a process to a number. Interactive Process Indicators use Process Mining technologies to provide rich graph-based indicators that bring a human-understandable view that supports the expert in the better perception of the processes for advanced assessment.

This chapter is an introduction to the book. This book aims to present and deepen in the use of Interactive Process Mining as a new paradigm for integrating health experts in the process of generating new evidence with Data-Driven philosophy. This paradigm combines the application of process mining technologies in healthcare using interactive machine learning paradigm for supporting health professionals in inferring new knowledge from past actions and providing accurate and personalized knowledge for future decisions and improve patients’ treatments and quality of life.

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community? This research received no external funding

[EN] Patients with type 2 diabetes have a higher chance of developing cardiovascular diseases and an increased odds of mortality. Reliability of randomized clinical trials is continuously judged due to selection, attrition and reporting bias. Moreover, cardiovascular risk is frequently assessed in cross-sectional studies instead of observing the evolution of risk in longitudinal cohorts. In order to correctly assess the course of cardiovascular riskinpatientswithtype 2 diabetes, weappliedprocessminingtechniquesbasedontheprinciples of evidence-based medicine. Using a validated formulation of the cardiovascular risk, process mining allowed to cluster frequent risk pathways and produced 3 major trajectories related to risk management: high risk, medium risk and low risk.This enables the extractionofmeaningful distributions, such as the gender of the patients per cluster in a human understandable manner, leading to more insights to improve themanagementofcardiovasculardiseasesintype2diabetes patients., This work was supported by European Commission Grant No 600914 (MOSAIC Project).

[EN] There are some studies and methods in the literature to understand customer needs and behaviors from the path. However, path analysis has a complex structure because the many customers can follow many different paths. Therefore, clustering methods facilitate the analysis of the customer location data to evaluate customer behaviors. Therefore, we aim to understand customer behavior by clustering their paths. We use an intuitionistic fuzzy c-means clustering (IFCM) algorithm for two-dimensional indoor customer data; case durations and the number of visited locations. Customer location data was collected by Bluetooth-based technology devices from one of the major shopping malls in Istanbul. Firstly, we create customer paths from customer location data by using process mining that is a technique that can be used to increase the understandability of the IFCM results. Moreover, we show with this study that fuzzy methods and process mining technique can be used together to analyze customer paths and gives more understandable results. We also present behavioral changes of some customers who have a different visit by inspecting their clustered paths.

Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining. ispartof: Artificial Intelligence In Medicine vol:109 ispartof: location:Netherlands status: Published online

World Health Organisation defines overweight and obesity as abnormal or excessive fat accumulation that represents a risk to health. Obesity and overweight are associated with increased risk of comorbidities and social problems that negatively impact quality of life. Due to the complexity of the problems, it is necessary to classify obesity based on a set of factors rather than a simple increase in body weight. The objectives of this work were to examine BMI and data available from comorbidities associated to obesity, from a dynamic perspective thanks to the use of process mining tools, in order to obtain patterns of patients’ behaviours. On the other hand, to develop a set of human-readable and contextualised interactive process indicators (iPIs) in the field of obesity, related conditions support health professionals to interact with the process. Modelling iPIs as enhanced views will help the professionals to better perceive of these processes. Professionals will monitor the patient’s progress iteratively and will interact with the system to fine-tune interventions and treatments. The developed strategy can support both the characterisation of general process-based PI and the analysis of individual and personalised aspects of the processes going from general to individual. This method was applied to real data extracted from a tertiary hospital in Spain.

Indoor customers may have different purposes to visit a shopping mall. Understanding the visiting aims results in better customer relationship management. One of the ways to explain the customer purpose is to discover customer paths. Customers mainly visit stores related to their purposes. The main problem is to discover customer paths from paths. Since customers have changeable mood and there are many stores in a shopping mall, customer paths are generally too complex to evaluate. To overcome that problem, we use process mining technique. Process mining is a technique that has some algorithms to discover business processes from event logs in the databases. In this study, we consider the visited stores as an activity in a process. PALIA Suite, a process mining tool that includes several clustering methods for processes, is used to discover and cluster indoor customer paths to evaluate differences among the visits.

Malnutrition is one of the major geriatric syndromes and frailty factor, this joint with the fact of elderly population growing, will situate malnutrition as a front end problem in the upcoming years. Therefore, it is important that health professionals can assess and follow up nutritional status in a proper way, using all available data related to patients. Process mining can be used to extract knowledge from information in order to understand health care processes. A classic approach to assess malnutrition usually comprises anthropometric measures as static variables, with no information about patients evolution and pathways. The aim of this work was to examine anthropometric measures from a dynamic perspective thanks to process mining tools, in order to obtain dynamic behaviour models. This paper proposes a method based on the use of process mining to discover and identify weight changes behaviour. Clustering is used as part of the pre-processing of data to manage variability, and then process mining is used to identify patterns of patients' behaviour. The method is applied through different experiments to data from 96 patients. Results grouped almost all individuals in different models based on common behaviours. Main finding shows different behaviour groups seem to have different results regarding malnutrition status for same interventions. By discovering patterns of dynamic weight change and their relation with malnutrition, nursing homes and health care professional can promote more successful intervention among patients based on their behaviour, moreover they can compare interventions' results analysing changes in behaviour between before and after the intervention.

Obesity is a growing problem that has reached a pandemic dimension. Diagnosis of obesity is based on the body mass index, regardless other important indicators related to metabolic impairments. One of the main problems is the difficult identification of obese subjects or in risk of developing obesity. New health information systems supporting massive amounts of physiological, treatments and lifestyle data have been proposed elsewhere, however these have also introduced a significant data and decision overload problem. In this paper we present a comparative study on data base management engines for supporting big data analytics for the identification of obese subjects based on Electronic Health Records. We compared relational and non-relational approaches to address scalability and performance in a tertiary hospital. The experiments have evaluated data from five different hospital services on a data-mart containing 20,706,947 records from the University Hospital La Fe of Valencia (Spain). Experiments where based on data load and query with different configurations and restrictions. NoSQL approach yielded better results when compared to relational engines for all the proposed experiments.

The CrowdHEALTH project aims at delivering an integrated platform that provides decision support to public health authorities for policy creation through the exploitation of collective knowledge that emerges from multiple information sources. The latter will be realized through proposed Social Holistic Health Records - SHHRs. CrowdHEALTH will provide policy makers with the means of processing large amount of healthcare information (including diseases, root causes, risk factors and patient data across different geographical areas and timescales) from a single-entry point.

An adequate nutritional status is a major determinant of health in the elderly population. Malnutrition or risk of malnutrition are situations linked to an increase of complications, which often affects people over 60 years old. The Mini Nutritional Assessment questionnaire allows to obtain in a fast, easy and non-invasive way information related to the nutritional status. However, the solely act of assessment does not lead to an improvement in the nutritional status. In this paper we describe the implementation and evaluation of an Android based app containing the Mini Nutritional Assessment questionnaire and a set of recommended nutritional interventions based on the questionnaire score. The app was evaluated in a single-cohort study involving 154 subjects during a a period of 6 months in a nursing home. Our results confirm the effectiveness of the screening and intervention program based on the use of the app, as per an improvement in the nutritional status of subjects who where primarily malnourished or at risk of malnutrition. The usability, perceived usefulness and perceived savings of the app was evaluated by means of an adapted System Usability Score questionnaire on ten professionals participating in the study, showing high levels of acceptance and potential savings.

[EN] Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days., This research was funded by ITACA SABIEN and partially supported by CONICYT REDI 170136.

[EN] The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018., This project received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 812386.

Clinical Guidelines, medical protocols, and other healthcare indications, cover a significant slice of physicians daily routine, as they are used to support clinical choices also with relevant legal implications. On the one hand, informatics have proved to be a valuable mean for providing formalisms, methods, and approaches to extend clinical guidelines for better supporting the work performed in the healthcare domain. On the other hand, due to the different perspectives that can be considered for addressing similar problems, it lead to an undeniable fragmentation of the field. It may be argued that such fragmentation did not help to propose a practical, accepted, and extensively adopted solutions to assist physicians. As in Process Mining as a general field, Process Mining for Healthcare inherits the requirement of Conformance Checking. Conformance Checking aims to measure the adherence of a particular (discovered or known) process with a given set of data, or vice-versa. Due to the intuitive similarities in terms of challenges and problems to be faced between conformance checking and clinical guidelines, one may be tempted to expect that the fragmentation issue will naturally arise also in the conformance checking field. This position paper is a first step on the direction to embrace experience, lessons learnt, paradigms, and formalisms globally derived from the clinical guidelines challenge. We argue that such new focus, joint with the even growing notoriety and interest in PM4HC, might allow more physicians to make the big jump from user to protagonist becoming more motivated and proactive in building a strong multidisciplinary community.

Medical emergencies are one of the most critical processes that occurs in a hospital. The creation of adequate and timely triage protocols, can make the difference between the life and death of the patient. One of the most critical emergency care protocols is the stroke case. This disease demands an accurate and quick diagnosis for ensuring an immediate treatment in order to limit or even, avoid, the undesired cognitive decline. The aim of this paper is perform an analysis of how Process Mining techniques can support health professionals in the interactive analysis of emergency processes considering critical timing of Stroke, using a Question Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we have used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January of 2010 to June of 2017. Our results demonstrate how Process Mining technology can highlight the differences of the stroke patient flow in emergency, supporting professionals in the better understanding and improvement of quality of care.

The wide-spread adoption of real-time location system is boosting the development of software applications to track persons and assets on real-time and perform analytics. Among the vast amount of data analysis techniques, process mining allows to conform work-flows with heterogeneous multivariate data, enhancing the model understandability and usefulness in clinical environments. However, such applications still find entrance barriers in the clinical context. In this paper we have identified the preferred features of a process mining based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. Work-flows are inferred and enhanced using process discovery on location data of patients undergoing an intervention, drawing nodes (states in the process) and transitions across the entire process. Analytic Hierarchy Process has been applied to quantify the prioritization of the features contained in the process mining dashboard (filtering data, enhancement, node selection, statistics, etc..), distinguishing on the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (N=10) was classified into three groups: Technical, Clinical and Managerial staff according to their responsibilities. Results show different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms.

[EN] The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care., This research was funded by Hospital General de Valencia thanks to the LOPEZ TRIGO 2017 AWARD and by the CONICYT grant REDI 170136 Project. The APC was funded by the APE/2019/007 (D.O.G.V. 8355/06.08.2018 Annex XIII).

Hospitals are becoming increasingly aware of the need to improve their processes and data-driven approaches, such as process mining, are gaining attention. When applying process mining techniques in reality , it is widely recognized that real-life data tends to suffer from data quality problems. Consequently, thorough data quality assessment and data cleaning is required. This paper proposes an interactive data cleaning approach for process mining. It encompasses both data-based and discovery-based data quality assessment, showing that both are complementary. To illustrate some key elements of the proposed approach, a case study of an outpatient clinic's appointment system is considered.