The Deployment of Business Process Management Systems: A Quantitative Analysis of End-Users’ Evaluations

Business process management technology is used at many sites as an instrument to improve the efficiency and agility of business operations. Currently, only a fragmented insight exists into the determinants for successful usage of this technology. The study in this paper is a first approach to more systematically investigate this issue, in particular by taking the end-user perspective into account. The study draws from quantitative data on two different implementations of business process management technology, as generated by the involvement of 342 end users. A major finding is that the proposed research model, inspired by the DeLone and McLean IS success model, has a very high power to explain the successfulness of the investigated implementations. It is found in particular that input and output quality and the quality of the IT support during operations are key factors that are more important than the characteristics of the actual system, which has important ramifications for IT praxis and research

Identifying Patient Groups based on Frequent Patterns of Patient Samples

Grouping patients meaningfully can give insights about the different types of patients, their needs, and the priorities. Finding groups that are meaningful is however very challenging as background knowledge is often required to determine what a useful grouping is. In this paper we propose an approach that is able to find groups of patients based on a small sample of positive examples given by a domain expert. Because of that, the approach relies on very limited efforts by the domain experts. The approach groups based on the activities and diagnostic/billing codes within health pathways of patients. To define such a grouping based on the sample of patients efficiently, frequent patterns of activities are discovered and used to measure the similarity between the care pathways of other patients to the patients in the sample group. This approach results in an insightful definition of the group. The proposed approach is evaluated using several datasets obtained from a large university medical center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury and around 0.6 for diabetes

Towards a Science of Checklists

Checklists are in use in many work domains, including aviation, manufacturing, quality control, and healthcare. Despite their adoption, the literature shows both breadth and persistence of problems with the organizational usage of checklists. In this paper, we conduct a structured literature survey to analyze checklists from the perspective of informational artifacts. Our contribution is a respective conceptualization of checklists and a rigorous analysis of their problems. As we will argue, these insights help to consider how the capabilities of IT systems can be leveraged to improve checklists and address their problematic aspects. We present our work as a basis for IT-oriented research into a relevant yet under-examined information practice in organizational work routines

DETECTING ROLE INCONSISTENCIES IN PROCESS MODELS

Business process models capture crucial information about business operations. To overcome the challenge of maintaining process definitions in large process repositories, researchers have suggested methods to discover errors in the functional and the behavioral perspectives of process models. However, there is a gap in the literature on the detection of problems on the organizational perspective of process models, which is critical to manage the resources and the responsibilities within organizations. In this paper, we introduce an approach to automatically detect inconsistencies between activities and roles in process models. Our approach implements natural language processing techniques and enterprise semantics to identify ambiguous, redundant, and missing roles in textual descriptions. We applied our approach on the process model repository of a major telecommunication company. A quantitative evaluation of our approach with 282 real-life activities displayed that this approach can accurately discover role inconsistencies. Practitioners can achieve significant quality improvements in their process model repositories by applying the approach on process models complemented with textual descriptions

An Integrative Framework of the Factors Affecting Process Model Understanding: A Learning Perspective

Process models are used by information professionals to convey semantics about the business operations in a real world domain intended to be supported by an information system. The understandability of these models is vital to them actually being used. After all, what is not understood cannot be acted upon. Yet until now, understandability has primarily been defined as an intrinsic quality of the models themselves. Moreover, those studies that looked at understandability from a user perspective have mainly conceptualized users through rather arbitrary sets of variables. In this paper we advance an integrative framework to understand the role of the user in the process of understanding process models. Building on cognitive psychology, goal-setting theory and multimedia learning theory, we identify three stages of learning required to realize model understanding, these being Presage, Process, and Product. We define eight relevant user characteristics in the Presage stage of learning, three knowledge construction variables in the Process stage and three potential learning outcomes in the Product stage. To illustrate the benefits of the framework, we review existing process modeling work to identify where our framework can complement and extend existing studies

An Evidence-Based Decision Support Framework for Clinician Medical Scheduling

In healthcare management, waiting time for consultation is an important measure that has strong associations with patient's satisfaction (i.e., the longer patients wait for consultation, the less satisfied they are). To this end, it is required to optimize medical scheduling for clinicians. A typical approach for deriving the optimized schedules is to perform experiments using discrete event simulation. The existing work has developed how to build a simulation model based on process mining techniques. However, applying this method for outpatient processes straightforwardly, in particular medical scheduling, is challenging: 1) the collected data from electronic health record system requires a series of processes to acquire simulation parameters from the raw data; and 2) even if the derived simulation model fully reflects the reality, there is no systematic approach to deriving effective improvements for simulation analysis, i.e., experimental scenarios. To overcome these challenges, this paper proposes a novel decision support framework for a clinician's schedule using simulation analysis. In the proposed framework, a data-driven simulation model is constructed based on process mining analysis, which includes process discovery, patient arrival rate analysis, and service time analysis. Also, a series of steps to derive the optimal improvement method from the simulation analysis is included in the framework. To demonstrate the usefulness of our approach, we present the case study results with real-world data in a hospital.11Ysciescopu