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 techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Several studies have discussed the benefits of multidisciplinary collaboration in primary care. However, what remains unclear is how collaboration is undertaken in a multidisciplinary manner in concrete terms. To identify how multidisciplinary teams in primary care collaborate, in regards to the professionals involved in the teams and the collaborative activities that take place, and determine whether these characteristics and practices are present across disciplines and whether collaboration affects clinical outcomes. A systematic literature review of past research, using the MEDLINE, ScienceDirect and Web of Science databases. Four types of team composition were identified: specialized teams, highly multidisciplinary teams, doctor–nurse–pharmacist triad and physician–nurse centred teams. Four types of collaboration within teams were identified: co-located collaboration, non-hierarchical collaboration, collaboration through shared consultations and collaboration via referral and counter-referral. Two combinations were commonly repeated: non-hierarchical collaboration in highly multidisciplinary teams and co-located collaboration in specialist teams. Fifty-two per cent of articles reported positive results when comparing collaboration against the non-collaborative alternative, whereas 16% showed no difference and 32% did not present a comparison. 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.

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.