Flexible Support of Healthcare Processes

Traditionally, healthcare information systems have focused on the support of predictable and repetitive clinical processes. Even though the latter can be often prespecified in formal process models, process flexibility in terms of dynamic adaptability is indispensable to cope with exceptions and unforeseen situations. Flexibility is further required to accommodate the need for evolving healthcare processes and to properly support healthcare process variability. In addition, process-aware information systems are increasingly used to support less structured healthcare processes (i.e., patient treatment processes), which can be characterized as knowledge-intensive. Healthcare processes of this category are neither fully predictable nor repetitive and, therefore, they cannot be fully prespecified at design time. The partial unpredictability of these processes, in turn, demands a certain amount of looseness. This chapter deals with the characteristic flexibility needs of both prespecified and loosely specified healthcare processes. In addition, it presents fundamental flexibility features required to address these flexibility needs as well as to accommodate them in healthcare practice

Context-Based Prevention and Handling of Exceptions for Human-Centric Mobile Services

Using smart mobile devices to support human-centric services is a frequent demand in business scenarios. As a particular challenge, tasks performed in a paper-driven way shall be digitally transformed with the use of mobile devices. With the goal to enable business applications supporting human-centric mobile services in mind, we developed a frame-work that extends existing process management technology with mobile activities running on smart mobile devices. Note that when considering the frequently changing conditions of mobile environments, the prevention and the proper handling of exceptions (e.g., lost connections) become crucial. The developed framework, therefore, aims to prevent exceptions and to provide a sophisticated exception handling service not supported by existing process management technology so far

Context-Based Handling of Mobile Process Activities

Process technology constitutes a crucial component of information systems. In this context, high flexibility is required as business functions must be quickly adaptable to cope with dynamic business changes. As recent developments allow for the use of mobile devices in knowledge-intensive areas, it is often demanded to enhance process-aware information systems with mobile activity support. In general, the technical integration of this activity type with existing process management technology is challenging. For example, protocols governing the communication between mobile devices and process management systems must be adapted. If a mobile context shall be additionally considered, the integration gets even more complex. However, the use of a mobile context offers advantages. For example, the mobile activity execution time may be decreased if mobile activities are only assigned to those users whose location is beneficial. This chapter proposes an approach to enable the robust handling of single process activities on mobile devices based on a mobile process model

Robuste und kontextbezogene Ausführung mobiler Aktivitäten in Prozessumgebungen

IT-Trendanalysten sehen das Thema "Mobilität" als eine wichtige Säule nachhaltiger IT-Lösungen. Der Trend in Richtung mobiler IT-Anwendungen wird maßgeblich durch Millenials getrieben, d.h. Menschen die mit dem digitalen Zeitalter aufgewachsen sind. Diese erwarten insbesondere auch eine Integration von Smart-Mobilgeräten in bestehende IT-Lösungen. In Bezug auf Prozess-Management-Technologie bedeutet dieser Trend, dass Smart-Mobilgeräte in IT-gestützte Arbeits- bzw. Prozessabläufe nahtlos integriert werden können müssen. Insbesondere sollten sowohl einzelne Aktivitäten (d.h. Prozessschritte) als auch ganze Prozessfragmente (d.h. Ausschnitte eines Prozesses) auf Smart-Mobilgeräten ausführbar sein. Die vorliegende Arbeit adressiert eine solche Integration von Prozess-Management-Technologie und Smart-Mobilgeräten. Konkret wird untersucht, wie ausgewählte Aktivitäten eines Prozesses robust und kontextbezogen auf Smart-Mobilgeräten ausgeführt werden können und welche weitergehenden Anforderungen sich für mobil ausgeführte Aktivitäten im Vergleich zur Ausführung von Aktivitäten auf stationären Systemen ergeben. Da Smart-Mobilgeräte beschränkte Ressourcen besitzen und das Risiko eines Ausfalls höher als bei stationären Systemen ist, erfordern diese Aspekte tiefergehende Untersuchungen. Darüber hinaus erfordert die Unterstützung mobiler Aktivitäten eine technische Umgebung, in der Prozesse ausgeführt werden (sog. Prozessumgebung). Die Arbeit zeigt, dass die nahtlose Integration von Smart-Mobilgeräten in eine Prozessumgebung einen mobilen Kontext (d.h. Attribute wie z.B. Ausführungsort, Geräteeigenschaften und Netzverbindung) erfordert. Auf dessen Basis wird ein umfassendes Rahmenwerk eingeführt, mit dem sich mobile Aktivitäten robust und kontextbezogen in einer Prozessumgebung ausführen lassen. Das Rahmenwerk fußt auf fünf technischen Säulen, deren Konzepte die robuste und kontextbezogene Ausführung bewerkstelligen. Darüber hinaus wird gezeigt, wie sich die vorgestellte Lösung in existierende Prozess-Management-Technologie integrieren lässt. Insgesamt eröffnet eine robuste und kontextbezogene Ausführung mobiler Aktivitäten in einer Prozessumgebung neue Perspektiven für die Einbindung von Endanwendern in ihre Prozesse

A Formal Framework for Data-Aware Process Interaction Models

IT support for distributed and collaborative workflows as well as related interactions between business partners are becoming increasingly important. For modeling such partner interactions as flow of message exchanges, different topdown approaches, covered under the term interaction modeling, are provided. Like for workflow models, correctness constitutes a fundamental challenge for interaction models; e.g., to ensure the boundedness and absence of deadlocks and lifelocks. Due to their distributed execution, in addition, interaction models should be message-deterministic and realizable, i.e., the same conversation (i.e. sequence of messages) should always lead to the same result, and it should be ensured that partners always have enough information about the messages they must or may send in a given context. So far, most existing approaches have addressed correctness of interaction models without explicitly considering the data exchanged through messages and used for routing decisions. However, data support is crucial for collaborative workflows and interaction models respectively. This technical report enriches interaction models with the data perspective. In particular, it defines the behavior of data-aware interaction models based on Data- Aware Interaction Nets, which use elements of both Interaction Petri Nets and Workflow Nets with Data. Finally, formal correctness criteria for Data-Aware Interaction Nets are derived, guaranteeing the boundedness and absence of deadlocks and lifelocks, and ensuring message-determinism as well as realizability

Data-Aware Interaction in Distributed and Collaborative Workflows: Modeling, Semantics, Correctness

IT support for distributed and collaborative workflows and related interactions between business partners is becoming increasingly important. For modeling such partner interactions as flow of message exchanges, different top-down approaches, covered under the term interaction modeling, are provided. Like for workflow models, correctness constitutes a fundamental challenge for interaction models as well; e.g., to ensure the boundedness and absence of deadlocks and lifelocks. Due to their distributed execution, in addition, interaction models should be message-deterministic and realizable, i.e., the same conversation (i.e. sequence of messages) should always lead to the same result, and it should be ensured that partners always have enough information about the messages they must or may send in a given context. So far, most existing approaches have addressed correctness of interaction models without explicitly considering the data exchanged through messages and used for routing decisions. However, data support is crucial for collaborative workflows and interaction models respectively. This paper therefore enriches interaction models with the data perspective. In particular, it defines the behavior of data-aware interaction models based on Data-Aware Interaction Nets, which use elements of both Interaction Petri Nets and Workflow Nets with Data. Finally, formal correctness criteria for Data-Aware Interaction Nets are derived, guaranteeing the boundedness and absence of deadlocks and lifelocks, and ensuring message-determinism as well as realizability

Using Smart Mobile Devices for Collecting Structured Data in Clinical Trials: Results From a Large-Scale Case Study

In future, more and more clinical trials will rely on smart mobile devices for collecting structured data from subjects during trial execution. Although there have been many projects demonstrating the benefits of mobile digital questionnaires, the scenarios considered in literature have been rather limited so far. In particular, the number of subjects is rather low in respective studies and a well controllable infrastructure is usually presumed, which not always applies in practice. This paper gives insights into the lessons learned in a clinical psychology trial when using tablets for mobile data collection. In particular, more than 1.700 subjects have participated so far, providing us with valuable feedback on collecting trial data with smart mobile devices in the large scale. Furthermore, issues related to an insufficient infrastructure (e.g., unstable Internet connections) have been addressed as well. Overall, the paper provides valuable insights gained during trial execution. In future, electronic questionnaires executable on smart mobile devices will replace paper-based ones

Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable

Context-Aware Querying and Injection of Process Fragments in Process-Aware Information Systems

Cyber-physical systems (CPS) are often customized to meet customer needs and, hence, exhibit a large number of hard-/software configuration variants. Consequently, the processes deployed on a CPS need to be configured to the respective CPS variant. This includes both configuration at design time (i.e., before deploying the implemented processes on the CPS) and runtime configuration taking the current context of the CPS into account. Such runtime process configuration is by far not trivial, e.g., alternative process fragments may have to be selected at certain points during process execution of which one fragment is then dynamically applied to the process at hand. Contemporary approaches focus on the design time configuration of processes, while neglecting runtime configuration to cope with process variability. In this paper, a generic approach enabling context-aware process configuration at runtime is presented. With the Process Query Language process fragments can be flexibly selected from a process repository, and then be dynamically injected into running process instances depending on the respective contextual situations. The latter can be automatically derived from context factors, e.g., sensor data or configuration parameters of the given CPS. Altogether, the presented approach allows for a flexible configuration and late composition of process instances at runtime, as required in many application domains and scenarios