Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes

Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyze executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analyzed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes.

Overall, collaboration was found to be positive or neutral in every study that compared collaboration with a non-collaborative alternative. A collaboration typology based on objective measures was devised, in contrast to typologies that involve interviews, perception-based questionnaires and other subjective instruments.

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Purpose-Human resource allocation is considered a relevant problem in business process management (BPM). The successful allocation of available resources for the execution of process activities can impact on process performance, reduce costs and obtain a better productivity of the resources. In particular, process mining is an emerging discipline that allows improvement of the resource allocation based on the analysis of historical data. The purpose of this paper is to provide a broad review of primary studies published in the research area of human resource allocation in BPM and process mining. Design/methodology/approach-A systematic mapping study (SMS) was conducted in order to classify the proposed approaches to allocate human resources. A total of 2,370 studies published between January 2005 and July 2016 were identified. Through a selection protocol, a group of 95 studies were selected. Findings-Human resource allocation is an emerging research area that has been evolving over time, generating new proposals that are increasingly applied to real case studies. The majority of proposed approaches relate to the period 2011-2016. Journals and conference proceedings are the most common venues. Validation research and evaluation research are the most common research types. There are two main evaluation methods: simulation and case studies. Originality/value-This study aims to provide an initial assessment of the state of the art in the research area of human resource allocation in BPM and process mining. To the best of the authors' knowledge, this is the first research that has been conducted to date that generates a SMS in this research area.

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Abstract:In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations.

This study allows the discovery of role interaction models through the use of real-life clinical data and process mining techniques. Results show a useful way of providing relevant insights about how healthcare professionals collaborate, uncovering opportunities for process improvement.