Process mining in healthcare : opportunities beyond the ordinary

Nowadays, in a Hospital Information System (HIS) huge amounts of data are stored about the care processes as they unfold. This data can be used for process mining. This way we can analyse the operational processes within a hospital based on facts rather than fiction. In order to enhance the uptake of process mining within the healthcare domain we present a healthcare reference model which exhaustively lists the typical types of data that exists within a HIS and that can be used for process mining. Based on this reference model, we elaborate on the most interesting kinds of process mining analyses that can be performed in order to illustrate the potential of process mining. As such, a basis is provided for governing and improving the processes within a hospital. Keywords: healthcare, process mining, reference mode

Supporting process mining workflows with RapidProM

Process mining is gaining more and more attention both in industry and practice. As such, the number of process mining products is steadily increasing. However, none of these products allow for composing and executing analysis work flows consisting of multiple process mining algorithms. As a result, the analyst needs to perform repetitive process mining tasks manually and scientific process experiments are extremely labor intensive. To this end, we have RapidMiner 5, which allows for the definition and execution of analysis work flows, connected with the process mining framework ProM 6. As such any discovery, conformance, or extension algorithm of ProM can be used within a RapidMiner analysis process thus supporting process mining work flows

Supporting process mining workflows with RapidProM

Process mining is gaining more and more attention both in industry and practice. As such, the number of process mining products is steadily increasing. However, none of these products allow for composing and executing analysis work flows consisting of multiple process mining algorithms. As a result, the analyst needs to perform repetitive process mining tasks manually and scientific process experiments are extremely labor intensive. To this end, we have RapidMiner 5, which allows for the definition and execution of analysis work flows, connected with the process mining framework ProM 6. As such any discovery, conformance, or extension algorithm of ProM can be used within a RapidMiner analysis process thus supporting process mining work flows

Process mining in healthcare : opportunities beyond the ordinary

Nowadays, in a Hospital Information System (HIS) huge amounts of data are stored about the care processes as they unfold. This data can be used for process mining. This way we can analyse the operational processes within a hospital based on facts rather than fiction. In order to enhance the uptake of process mining within the healthcare domain we present a healthcare reference model which exhaustively lists the typical types of data that exists within a HIS and that can be used for process mining. Based on this reference model, we elaborate on the most interesting kinds of process mining analyses that can be performed in order to illustrate the potential of process mining. As such, a basis is provided for governing and improving the processes within a hospital. Keywords: healthcare, process mining, reference mode

Discovering simulation models

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a Coloured Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models

Mining declarative models using time intervals

A common problem in process mining is the interpretation of the time stamp of events, e.g., whether it represents the moment of recording, or its occurrence. Often, this interpretation is left implicit. In this paper, we make this interpretation explicit using time intervals: an event occurs somewhere during a time window. The time window may be fine, e.g., a single point in time, or coarse, like a day. As each event is related to an activity within some process, we obtain for each activity a set of intervals in which the activity occurred. Based on these sets of intervals, we define ordering and simultaneousness relations. These relations form the basis of the discovery of a declarative process model describing the behavior in the event log

Implementation of a healthcare process in four different workflow systems

Currently, many hospitals are investigating the use of a work-flow management system in order to provide support for care processes. However, today's workflow management systems fall short in supporting care processes as exibility is required for its execution. In this paper, we investigate the exibility requirements that need to be satisfied in orderto support healthcare processes having various characteristics. An evaluation shows that different systems need to be used in conjunction with each other in order to fully support the various types of care processes

Discovering simulation models

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a Coloured Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models

Towards a taxonomy of process flexibility (extended version)

Effective business processes must be able to accommodate changes to the environment in which they operate, e.g., new laws, changes in business strategy. The ability to encompass such changes is termed process flexibility. In this paper, we take a deeper look into the various ways in which flexibility can be achieved and propose a comprehensive taxonomy of these methods which identifies both the manner in which each of them is facilitated, and also the various configuration options and alternatives that exist in each case. This taxonomy is subsequently used to evaluate a selection of process-aware information systems and identify their potential to deploy flexible business processes