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IntroductionCancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities.MethodsFollowing a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients' progress over time.ResultsThis work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients' care processes in the context of the LifeChamps project together with a graphical and navigable Process Indicator in the context of prostate cancer patients.DiscussionThe tool presented includes Process Mining techniques to infer actual processes and present understandable results visually and navigable, looking for different types of patients, trajectories, and behaviors.

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 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.

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.

Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients&rsquo, dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients&rsquo, unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.

[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).

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.

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 study presents some results of customer paths&rsquo, analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men&rsquo, s bathroom or women&rsquo, s bathroom. Since the study has a comprehensive scope, we focused on male and female customers&rsquo, behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.

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.

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.

BACKGROUND Research in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored. OBJECTIVE To study the gap between literature and available apps for type 1 diabetes self-management and patient empowerment and to discover the features that an ideal app should provide to people with diabetes. METHODS The methodology comprises systematic reviews in the scientific literature and app marketplaces. We included articles describing interventions that demonstrated an effect on diabetes management with particular clinical endpoints through the use of mobile technologies. The features of these apps were gathered in a taxonomy of what an ideal app should look like to then assess which of these features are available in the market. RESULTS The literature search resulted in 231 matches. Of these, 55 met the inclusion criteria. A taxonomy featuring 3 levels of characteristics was designed based on 5 papers which were selected for the synthesis. Level 1 includes 10 general features (Personalization, Family support, Agenda, Data record, Insulin bolus calculator, Data management, Interaction, Tips and support, Reminders, and Rewards) Level 2 and Level 3 included features providing a descriptive detail of Level 1 features. Eighty apps matching the inclusion criteria were analyzed. None of the assessed apps fulfilled the features of the taxonomy of an ideal app. Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Agenda (5/80, 6.3%) and Rewards (3/80, 3.8%) where the less predominant. The operating system was not associated with the number of features (P=.42, F=.81) nor the type of feature (P=.20, χ2=11.7). Apps were classified according to the number of level 1 features and sorted into quartiles. First quartile apps had a regular distribution of the ten features in the taxonomy whereas the other 3 quartiles had an irregular distribution. CONCLUSIONS There are significant gaps between research and the market in mobile health for type 1 diabetes management. While the literature focuses on aspects related to gamification, rewarding, and social communities, the available apps are focused on disease management aspects such as data record and appointments. Personalized and tailored empowerment features should be included in commercial apps for large-scale assessment of potential in the self-management of the disease.

Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 &plusmn, 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 &plusmn, 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices ( p &lt, &lt, 0 . 01 ) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.

BACKGROUND Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes. OBJECTIVE Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients. METHODS This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise. RESULTS The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner. CONCLUSIONS By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.

Digital Health Literacy (DHL) is a key element to promote patient empowerment. This position paper presents the lessons learnt from the daily activities of a General Practitioner interacting with patients. General Practitioners have a main role in each stage on individual digital health literacy process. They are the first meeting point between patients and the medical knowledge; in the search phase, they are who can prescribe and validate health information; in the comprehension phase, they lead to a full understanding; and in the adoption phase, they assist in the own personal application. Major conclusions are that General Practitioners need a set of tools, organizational resources and knowledge to acquire Digital Health Literacy skills to help patients on their way from the information to the empowerment. Some of these tools and knowledge are identified to draw the future roadmap to get people with Digital Health Literacy skills.

The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component ( Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.

Cardiovascular diseases are one of the main causes of mortality in the European Union. Cardiovascular diseases require a continuous follow-up in order to prevent complications that produce a decrease on the quality of life of patients and can even lead to their death. According to the Evidence Based Medicine thesis, the involvement of the patient in the care process will improve the Quality of Care provided and the efficiency of Care Plans. However, there is controversy in the literature about the fact that the Care Plans that Evidence Based Medicine provides are based on strict clinical cases to generate scientific evidence that is unclear whether they can be applied to personalized medicine. The use of an Interactive Pattern Recognition approach can create models controlled by human experts based on the biomedical data provided, in addition to medical decisions and the patient personality, providing a better way to find more tailored models. However, the use of Interactive Pattern Recognition techniques still requires the application of Human Understandable Pattern Recognition techniques. In this paper, an Interactive Pattern Recognition technique based on Process Mining technology is presented as a solution to deal with this problem.

Personalization is one of the drivers to deal with the sustainability of the health care systems in Eastern or Western countries. This position paper presents a set of challenges in personalized systems for Personal Health Care, especially in the field of cardiovascular diseases. Authors present the LINK initiative to push for this paradigm change, moving from personal to personalized health care, pointing different actions that need to be addressed. Therefore, a well-focused strategy on big data, behavioral aspects, translational research and patient empowerment can pave the way for a real Personalized Health Care in a very close future.

The social trend is progressively becoming the key feature of current Web understanding (Web 2.0). This trend appears irrepressible as millions of users, directly or indirectly connected through social networks, are able to share and exchange any kind of content, information, feeling or experience. Social interactions radically changed the user approach. Furthermore, the socialization of content around social objects provides new unexplored commercial marketplaces and business opportunities. On the other hand, the progressive evolution of the web towards the Semantic Web (or Web 3.0) provides a formal representation of knowledge based on the meaning of data. When the social meets semantics, the social intelligence can be formed in the context of a semantic environment in which user and community profiles as well as any kind of interaction is semantically represented (Semantic Social Web). This paper first provides a conceptual analysis of the second and third version of the Web model. That discussion is aimed at the definition of a middle concept (Web 2.5) resulting in the convergence and integration of key features from the current and next generation Web. The Semantic Social Web (Web 2.5) has a clear theoretical meaning, understood as the bridge between the overused Web 2.0 and the not yet mature Semantic Web (Web 3.0).

Diabetes is one of the metabolic disorders with more growth expectations in next decades. The literature points to a correct self-management, to an appropriate treatment and to an adequate healthy lifestyle as a way to dramatically improve the quality of life of patients with diabetes. The implementation of a holistic diabetes care system, using rising information technologies for deploying cares based on the thesis of the Evidence-Based Medicine can be a effective solution to provide an adequate and continuous care to patients. However, the design and deployment of computer readable careflows is not a easy task. In this paper, we propose the use of Interactive Pattern Recognition techniques for the iterative design of those protocols and we analyze the problems of using Process Mining to infer careflows and how to how to cope with the resulting Spaghetti Effect.

[EN] The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015., The authors want to acknowledge the work MySphera Company and Hospital General for their invaluable support. This work was supported in part by several projects; FASyS-Absolutely Safe and Healthy Factory (Spanish Ministry of Industry. CEN-20091034), MOSAIC-Models and simulation techniques for discovering diabetes influence factors (ICT-FP7-600914) and HEARTWAYS-Advanced Solutions for Supporting Cardiac Patients in Rehabilitation (ICT-SME-315659) EU Projects; and organizations like Tecnologias para la Salud y el Bienestar (TSB S.A.) and the Universitat Politecnica de Valencia.

The creation of tools supporting the automatization of the standardization and continuous control of healthcare processes can become a significant helping tool for clinical experts and healthcare systems willing to reduce variability in clinical practice. The reduction in the complexity of design and deployment of standard Clinical Pathways can enhance the possibilities for effective usage of computer assisted guidance systems for professionals and assure the quality of the provided care. Several technologies have been used in the past for trying to support these activities but they have not been able to generate the disruptive change required to foster the general adoption of standardization in this domain due to the high volume of work, resources, and knowledge required to adequately create practical protocols that can be used in practice. This chapter proposes the use of the PALIA algorithm, based in Activity-Based process mining techniques, as a new technology to infer the actual processes from the real execution logs to be used in the design and quality control of healthcare processes.

m-Health services are increasing its presence in our lives due to the high penetration of new smartphone devices. This new scenario proposes new challenges in terms of information accessibility that require new paradigms which enable the new applications to access the data in a continuous and ubiquitous way, ensuring the privacy required depending on the kind of data accessed. This paper proposes an architecture based on cloud computing paradigms in order to empower new m-Health applications to enrich their results by providing secure access to user data.

m-Health services are increasing its presence in our lives due to the high penetration of new smartphone devices. This new scenario proposes new challenges in terms of information accessibility that require new paradigms which enable the new applications to access the data in a continuous and ubiquitous way, ensuring the privacy required depending on the kind of data accessed. This paper proposes an architecture based on cloud computing paradigms in order to empower new m-Health applications to enrich their results by providing secure access to user data.

The creation of tools supporting the automatization of the standardization and continuous control of healthcare processes can become a significant helping tool for clinical experts and healthcare systems willing to reduce variability in clinical practice. The reduction in the complexity of design and deployment of standard Clinical Pathways can enhance the possibilities for effective usage of computer assisted guidance systems for professionals and assure the quality of the provided care. Several technologies have been used in the past for trying to support these activities but they have not been able to generate the disruptive change required to foster the general adoption of standardization in this domain due to the high volume of work, resources, and knowledge required to adequately create practical protocols that can be used in practice. This chapter proposes the use of the PALIA algorithm, based in Activity-Based process mining techniques, as a new technology to infer the actual processes from the real execution logs to be used in the design and quality control of healthcare processes.

The evolution of the Internet has been spectacular in recent decades. However, the Internet is still a linear scenario, focused on showing contents and dissociated from the physical world. On the other hand, there are many social groups that don't know how to use the opportunities that ICT can offer them, such as children. In this scenario, Project Enjoy.IT! designs, develops, and validates an entertainment platform with advanced contents that will set up a practical realization of the new products and services from the Future Internet. Project Enjoy.IT! integrates the physical world as an extension of the virtual world and vice versa. Thus, the project creates an AmI system that is able to act depending on the children's knowledge and necessities. The platform is based on a Services Choreography that allows an easy, simple integration of the necessary elements to give support to interactive entertainment activities. Copyright © 2011, IGI Global.

The design of clinical protocols for improving the quality of care in an efficient way is one of the challenges for the deployment of Evidence Based Medicine. The design of those protocols is a difficult task that require the consensus of care process experts. The use of Pattern Recognition approaches, like Process Mining, allows the automatic inference of processes that can help experts for formalizing these clinical protocols based on the actually deployed care process. However, the step rules among the different stages of the care protocols are based on high level descriptions of numerical clinical data gathered from the patient that can not be processed directly by Process Mining approaches. In this paper, a combination of Interactive Pattern Recognition with Temporal Abstraction technologies that allows processing of clinical data to allow the enrichment of Activity Based Process Mining corpus is presented.