Supporting medical ward rounds through mobile task and process management

Pryss, Rüdiger; Mundbrod, Nicolas; Langer, David; Reichert, Manfred

doi:10.1007/s10257-014-0244-5

Cited by 31 publications

(20 citation statements)

References 21 publications

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“…Smart mobile devices (eg, mobile phones or tablets), in turn, could act as an enabler for scenarios in which Web questionnaires are not sufficient. Mobile devices have already proven their applicability in the context of various business scenarios [9], ranging from simple task management apps to sophisticated business analytics platforms to even apps supporting ward rounds in hospitals [10]. …”

Section: Introductionmentioning

confidence: 99%

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Schobel¹,

Pryss²,

Probst³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundMany research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios.ObjectiveIn order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants.MethodsAcross 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework.ResultsStatistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P<.001), and time (P<.001) from the first to the last use of the configurator. Experts showed significant learning effects for operations (P=.001) and time (P<.001), but not for errors as the experts’ errors were already very low at the first modeling of the data collection instrument. Moreover, regarding the time and operations needed, novices got significantly better at the third modeling task than experts were at the first one (t tests; P<.001 for time and P=.002 for operations). Regarding errors, novices did not get significantly better at working with any of the 10 data collection instruments than experts were at the first modeling task, but novices’ error rates for all 5 data collection instruments at Session 2 were not significantly different anymore from those of experts at the first modeling task. After 7 days of not using the configurator (from Session 1 to Session 2), the experts’ learning effect at the end of Session 1 remained stable at the beginning of Session 2, but the novices’ learning effect at the end of Session 1 showed a significant decay at the beginning of Session 2 regarding time and operations (t tests; P<.001 for time and P=.03 for operations).ConclusionsIn conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own.

show abstract

Section: Introductionmentioning

confidence: 99%

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Schobel¹,

Pryss²,

Probst³

et al. 2018

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

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“…Both are directly considered in Section VI in context. Finally, Belotti et al [10] and Pryss et al [11] discuss the usage of digital task management in the context of KiBPs. Drawing upon, the definition of the challenges and requirements are also influenced by the results of these related works.…”

Section: Emergentmentioning

confidence: 99%

“…In turn, case study CS2 presents insights from patient treatment processes in a medical unit of a university hospital. Again, insights are based on profound experiences we have in this domain [18], [19], [11]. In both cases, we categorized the processes as knowledge-intensive since they clearly expose the characteristics presented in Section II-A.…”

Section: Case Studiesmentioning

confidence: 99%

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Process-Aware Task Management Support for Knowledge-Intensive Business Processes: Findings, Challenges, Requirements

Mundbrod

Reichert

2014

2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations

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Abstract-The operational support of knowledge-intensive business processes constitutes a big challenge. In particular, this process type is characterized as non-predictable, emergent, goaloriented, and knowledge-creating. Today, knowledge-intensive business processes are solely driven by professionals utilizing their skills and expertise whereas no support is provided by processaware information systems. In particular, the management of knowledge-intensive tasks (i.e., defining, updating, distributing, enacting, monitoring and assessing of tasks) is still accomplished manually by the knowledge workers based on pen and paper with the well-known drawbacks. This work presents two case studies of knowledge-intensive business processes. Based on the insights gained from these studies, we derive the core challenges as well as the requirements for an process-aware information system supporting knowledge workers in the management of the tasks emerging in the context of knowledge-intensive business processes.

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“…Smart mobile devices (e.g., smartphones or tablets), in turn, could act as an enabler for scenarios in which web questionnaires are not sufficient anymore. Mobile devices have already proven their applicability in the context of various business scenarios [9], ranging from simple task management applications to sophisticated business analytics platforms to even applications supporting ward-rounds in hospitals [10].…”

Section: Introductionmentioning

confidence: 99%

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study (Preprint)

Schobel¹,

Pryss²,

Probst³

et al. 2018

Preprint

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Background: Many research domains still heavily rely on paper-based data collection procedures despite numerous drawbacks. However, the QuestionSys framework is intended to empower researchers as well as clinicians with no programming skills to develop their own smart mobile applications in order to collect data in their specific scenarios. Objectives: In order to validate the feasibility of this model-driven end-user programming approach, a study with N=80 participants was conducted. Methods: Across 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component. Thereby, performance measures like time and operations needed and the resulting errors were evaluated. Participants were separated in two groups (i.e., novices vs. experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys approach. Results: T-tests revealed that novices showed significant learning effects in errors, operations, and time during the use of the configurator (all p < .05) and experts showed learning effects in operations and time (both p < .05) but not in errors as the experts' errors were already very low at the first modeling of a data collection instrument. Moreover, regarding time and operations needed, novices got significantly better after 3rd modeling task than experts were after the 1st one (tTests; both p < .05). With regard to errors, novices did not get significantly better at any of the 10 data collection instruments than experts were at the 1st modeling task, but novices' error rates in all 5 data collection instruments at Session 2 were not significantly different anymore from the ones of experts at the 1st modeling task. During the 7 days without using the configurator (from Session 1 to Session 2), the experts' learning effect of Session 1 remained stable, but the novices' learning effect of Session 1 lessened significantly regarding time and operations (t-Tests; both p < . 05). Conclusions: In conclusion, novices are able to use the configurator properly and show fast (but still instable) learning effects so that they become as good as experts already after little experience with the configurator. Researchers and clinicians can use the QuestionSys configurator to develop data collection applications for smart mobile devices on their own.

show abstract

Supporting medical ward rounds through mobile task and process management

Cited by 31 publications

References 21 publications

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Process-Aware Task Management Support for Knowledge-Intensive Business Processes: Findings, Challenges, Requirements

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study (Preprint)

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