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Process Mining Manifesto

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 in healthcare

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.

Applying Clustering in Process Mining to Find Different Versions of a Business Process That Changes over Time

Most Process Mining techniques assume business processes remain steady through time, when in fact their underlying design could evolve over time. Discovery algorithms should be able to automatically find the different versions of a process, providing independent models to describe each of them. In this article, we present an approach that uses the starting time of each process instance as an additional feature to those considered in traditional clustering approaches. By combining control-flow and time features, the clusters formed share both a structural similarity and a temporal proximity. Hence, the process model generated for each cluster should represent a different version of the analyzed business process. A synthetic example set was used for testing, showing the new approach outperforms the basic approach. Although further testing with real data is required, these results motivate us to deepen on this research line.

A Framework for Recommending Resource Allocation Based on Process Mining

Dynamically allocating the most appropriate resource to execute the different activities of a business process is an important challenge in business process management. An ineffective allocation may lead to an inadequate resources usage, higher costs, or a poor process performance. Different approaches have been used to solve this challenge: data mining techniques, probabilistic allocation, or even manual allocation. However, there is a need for methods that support resource allocation based on multi-factor criteria. We propose a framework for recommending resource allocation based on Process Mining that does the recommendation at sub-process level, instead of activity-level. We introduce a resource process cube that provides a flexible, extensible and fine-grained mechanism to abstract historical information about past process executions. Then, several metrics are computed considering different criteria to obtain a final recommendation ranking based on the BPA algorithm. The approach is applied to a help desk scenario to demonstrate its usefulness.

Process Remaining Time Prediction Using Query Catalogs

A relevant topic in business process management is the ability to predict the outcome of a process in order to establish a priori recommendations about how to go forward from a certain point in the process. Recommendations are made based on different predictions, like the process remaining time or the process cost. Predicting remaining time is an issue that has been addressed by few authors, whose approaches have limitations inherent to their designs. This article presents a novel approach for predicting process remaining time based on query catalogs that store the information of process events in the form of partial trace tails, which are then used to estimate the remaining time of new executions of the process, ensuring greater accuracy, flexibility and dynamism that the best methods currently available. This was tested in both simulated and real process event logs. The methods defined in this article may be incorporated into recommendation systems to give a better estimation of process remaining time, allowing them to dynamically learn with each new trace passing through the system.

Temporal Specification of Business Processes through Project Planning Tools

Business Process Management has gained importance within organizations due to the need to streamline their operations. Nevertheless, despite the existence of process modeling standards such as BPMN, nowadays it is difficult to specify complex temporal constraints and relationships among tasks of a given process, which prevents the specification and subsequent automation of processes where these restrictions are relevant. To solve the exposed difficulty, we have resorted to the project planning and management field, developing a BPMN equivalency of all temporal constraints and relationships that can be specified in a standard project planning tool: Microsoft Project. This not only enables a simple interface for specifying complex temporal restrictions in business processes, but also defines an execution semantic for the models developed in the field of project planning, allowing their later automation through process execution engines.

Combining iterative heuristic optimization and uncertainty analysis methods for robust parameter design

A number of investigators have pointed out that products and processes lack quality because of performance inconsistency, which is often due to uncontrollable parameters in the manufacturing process or product usage. Robust design methods are aimed at finding product/process designs that are less sensitive to parameter variation. Robust design of computer simulations requires a large number of runs, which are very time consuming. A novel methodology for robust design is presented in this article. It integrates an iterative heuristic optimization method with uncertainty analysis to achieve effective variability reductions, exploring a large parameter domain with an accessible number of simulations. To demonstrate the effectiveness of this methodology, the robust design of a 0.15 μm CMOS device is shown.

Using global search heuristics for the capacity vehicle routing problem

In this work, a frame is posed which allows to define global search heuristics in an efficient and declarative way, which interacts with a specific computational implementation of a problem. An overview of different ways to solve problems by the use of global search is presented, followed by the specification of the language proposed. A real decision support system was developed through the use of the language. The problem faced was an extension of the capacity vehicle routing problem. The followed approach minimizes the development cost of a decision support system for logistic and productive environments, since the performance of different heuristics can be tested using the language in a straightforward way. Besides, when new requirements or additional knowledge about the problem appear, the solving engine can be easily modified through the heuristic language.

Clinical Processes and Its Data, What Can We Do with Them?

Global healthcare services have evolved over time, and nowadays they are expected to follow high-quality optimized standards. Analyzing healthcare processes has become a relevant field of study, and different techniques and tools have been developed to promote improvements in the efficiency and effectiveness of these processes. There is a research field called process mining that can be used to extract knowledge from the event data stored in the hospital information systems. With the help of this, it is possible to discover the real executed process, examine its performance and analyze the resource interaction during its execution. The goal of this article is to provide a bibliographic survey about the use of process mining algorithms, techniques, and tools in the analysis of healthcare processes, providing a general overview about the main approaches previously used and the information required to apply them in the medical field. We provide important insights about data, algorithms, techniques and methodologies that are required to help answer medical expert questions about their processes, motivating and inspiring a broader usage. So, if we have the information and it is possible to analyze and understand the healthcare processes, why are we not doing it?

Multidisciplinary collaboration in primary care: a systematic review

Background 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. Objective 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. Methods A systematic literature review of past research, using the MEDLINE, ScienceDirect and Web of Science databases. Results 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. Conclusion 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.