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2015 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Process Mining in Healthcare

Evaluating and Exploiting Operational Healthcare Processes

verfasst von: Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch

Verlag: Springer International Publishing

Buchreihe : SpringerBriefs in Business Process Management

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed.

They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model.

This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Healthcare costs have increased dramatically and the demand for high-quality care will only grow in our aging society. At the same time, more event data are being collected about care processes. Healthcare Information Systems (HIS) have hundreds of tables with patient-related event data. Therefore, it is quite natural to exploit these data to improve care processes while reducing costs. Data science techniques will play a crucial role in this endeavor. Process mining can be used to improve compliance and performance while reducing costs. The chapter sets the scene for process mining in healthcare, thus serving as an introduction to this SpringerBrief.
Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch
Chapter 2. Healthcare Processes
Abstract
Process mining can be used to improve compliance and performance in hospitals and other care organizations. Before analyzing event data, we first provide an overview of the different types of care processes. We distinguish three levels of care: primary, secondary, and tertiary. We characterize five types of healthcare processes and link these to four basic types of data science questions: (a) What happened?, (b) Why did it happen?, (c) What will happen?, and (d) What is the best that can happen? Such questions can be answered using process mining. Using the characteristics of care processes, different questions may be posed. For example, the level of variability may influence the selection of the most suitable process mining technique.
Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch
Chapter 3. Process Mining
Abstract
Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but is mature enough to be applied to care processes of any type and of any complexity. The process-mining spectrum is broad and includes techniques for process discovery, conformance checking, prediction, and bottleneck analysis. Traditional data-mining approaches are not process-centric. Input for data mining is typically a set of records and the output is a decision tree, a collection of clusters, or frequent patterns. Process mining starts from events and the output is related to an end-to-end process model. Data mining tools can be used to support particular decisions in a larger process. However, they cannot be used for process discovery, conformance checking, and other forms of process analysis. Therefore, process mining is needed to improve compliance and performance in hospitals in a systematic manner.
Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch
Chapter 4. Healthcare Reference Model
Abstract
Data science projects in hospitals often fail because of data-related problems. The data are somewhere in the Hospital Information System (HIS), but the analyst cannot find it or extraction is too costly. Therefore, a healthcare reference model was developed. The goal is to locate event data easily and to support data extraction. Moreover, the analyst can use the model to ask the right questions. The healthcare reference model was developed based on an analysis of the available data in several Dutch hospitals. The reference model is described in terms of a UML class diagram. The model consists of 122 classes that provide a good overview of the key data relevant for process mining. It was validated by HIS professionals of the AMC, Catharina, and Isala hospitals.
Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch
Chapter 5. Applications of Process Mining
Abstract
The healthcare reference model illustrates the wealth of event data available in today’s hospitals. In this chapter, we focus on the application of process mining using such data. We identify various process mining use cases. These illustrate the use of process mining techniques like process discovery and conformance checking based on the healthcare reference model. We use event data from the Maastricht University Medical Center (MUMC) and the Academic Medical Center (AMC) in Amsterdam to illustrate the process mining use cases. The examples demonstrate that tools like ProM can indeed be used to remove inefficiencies and improve quality.
Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch
Chapter 6. Data Quality Issues
Abstract
Healthcare data, like any data, may have all kinds of quality problems. In this chapter, we identify 27 data quality issues that may compromise the validity of process mining results. Examples are missing data, incorrect data, imprecise data, and irrelevant data. For example, an event may only have a date (e.g., 15-6-2015) and not a fine-grained timestamp. As a result, the ordering of events is unknown, thus complicating analysis. Practitioners were interviewed to estimate the frequency of the 27 types of data quality issues identified. This provides insights into typical problems that may arise in data-science projects in hospitals. The quality of the analysis results directly depends on the input data (i.e., Garbage-In Garbage-Out). Therefore, the chapter also discusses 12 guidelines for logging. These guidelines should be used when developing the next generation of hospital information systems. Improved event logs will enable more advanced forms of process mining related to prediction and recommendation.
Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch
Chapter 7. Epilogue
Abstract
To address challenges related to efficiency and costs in healthcare, we need to exploit the event data present in today’s hospital information systems. Recent developments in data science are an important enabler for providing better and cheaper healthcare solutions. The healthcare reference model and the guidelines for logging aim to improve the input side, i.e., the goal is to collect high-quality event data. However, it is not enough to collect torrents of data. Powerful analysis techniques are needed to analyze the behavioral aspects of care processes. In this SpringerBrief, we proposed process mining as a key technology for understanding and improving healthcare processes. There are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users toward better care processes. This chapter summarizes the main contributions of this SpringerBrief.
Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch
Metadaten
Titel
Process Mining in Healthcare
verfasst von
Ronny S. Mans
Wil M. P. van der Aalst
Rob J. B. Vanwersch
Copyright-Jahr
2015
Electronic ISBN
978-3-319-16071-9
Print ISBN
978-3-319-16070-2
DOI
https://doi.org/10.1007/978-3-319-16071-9