Recommendations for enhancing the usability and understandability of process mining in healthcare

https://doi.org/10.1016/j.artmed.2020.101962Get rights and content

Highlights

  • Process mining enables evidence-based process improvement in healthcare.

  • Limited uptake of process mining techniques in healthcare organizations.

  • Paper presents recommendations to enhance usability and understandability.

  • Ten recommendations for process mining researchers and the community.

  • Three recommendations for healthcare organizations and system vendors.

Abstract

Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.

Introduction

The healthcare sector is confronted with severe challenges, most importantly the contention between tightening budgets and increased care needs due to the aging population [1], [2]. To face these challenges while achieving high quality of care standards, healthcare organizations such as hospitals are becoming increasingly aware of the need to improve their processes (i.e. interrelated sets of activities, decisions and events with a particular goal [3]). Processes play a central role in a healthcare organization's daily operations [1], [4]. They can be subdivided in two categories: clinical processes (e.g. the emergency care process or the treatment process of a particular condition), and administrative/organizational processes (e.g. the inventory management process of materials or the billing process) [5], [6].

Healthcare processes in general, and clinical processes in particular, possess some distinct characteristics compared to common business processes such as the order-to-cash process [6], [7]. Healthcare processes can be characterized as loosely framed and knowledge-intensive [8], [9]. A loosely framed process can be performed in a large, but finite and predefined, number of distinct ways [9]. This relates to the observation that healthcare processes typically exhibit high levels of variation [6]. The knowledge-intensive character implies that the execution of healthcare processes heavily depends on knowledge workers, such as physicians, and the knowledge-intensive decisions they make [8]. These complex decisions are made based on a wide range of criteria, including medical knowledge, patient-related characteristics and the experience of healthcare professionals [5], [6], [8], [10], [11]. Healthcare processes are typically also closely intertwined with each other and are multi-disciplinary, requiring cooperation between clinicians with different expertises, which adds to their complexity [5], [6], [8]. Besides their knowledge-intensive, loosely framed and multi-disciplinary character, healthcare processes are also highly dynamic as they typically continuously change over time due to advances in medical knowledge, technology or administrative procedures [5], [6], [10].

To identify opportunities for process improvement in such healthcare processes, a healthcare organization first needs to gain a profound understanding of the process under consideration. To gather insights in how the process is executed, staff members who are familiar with the process can be brought together for a discussion. This discussion can target the development of process models capturing process insights (such as the order of activities in a clinical process), which forms a basis for a process analysis. However, this is very time- and effort-intensive, and the created process model tends to present an idealized view on the process which might have little connection to reality [12].

To uncover the real behavior of an executed process, a solution can originate from data already collected by health information systems such as a hospital information system. Using the data embedded in the databases of these health information systems, an event log can be generated which contains detailed process execution data for a healthcare process of interest. In this way, the event log contains real-life data about which activities were performed, when they were performed, who performed them and for whom (e.g. for which patient) [12], [13]. Process mining is the research field concerned with the development of techniques to retrieve non-trivial information from such an event log [7], [12]. Over the past decade, the process mining community has developed an extensive set of techniques which convey profound process insights based on real-life data [4], [12]. These techniques relate to the discovery of process models from data, the detection of deviations between an existing model and reality, or the enhancement of an existing process model with, e.g., process performance information [12]. Process mining outcomes can be leveraged to instigate evidence-based process improvement initiatives in healthcare.

The systematic use of process mining in healthcare would be consistent with the Learning Healthcare System concept [14] introduced by the Institute of Medicine. One of the basic pillars of a Learning Healthcare System is the implementation of data reuse mechanisms, which allow for learning from data generated during the execution of processes [15], [16]. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for a two-day international brainstorm seminar.1 The seminar brought together 18 experts from 11 different countries, both researchers and healthcare practitioners, to reflect upon how to enhance the usability and understandability of process mining in healthcare. This position paper synthesizes the conclusions of the brainstorm seminar. It specifies ten key recommendations that process mining researchers and the community are encouraged to carefully consider when developing a new research agenda for process mining in healthcare. While the focus of our work is predominantly on recommendations for researchers and the research community, three additional recommendations are formulated which explicitly target healthcare organizations and health information systems vendors.

The remainder of this paper is structured as follows. Section 2 provides a a primer to process mining in healthcare, encompassing both an introduction to the topic and an overview of some applications in a healthcare context. Section 3 outlines the key recommendations for process mining researchers and the research community. Section 4 presents the key recommendations for healthcare organizations and health information systems vendors. Section 5 presents a brief conclusion.

Section snippets

A process mining in healthcare primer

To provide the required background to appreciate the formulated recommendation, this section provides a primer to process mining in healthcare. Section 2.1 introduces the basic concepts of process mining in healthcare. Building on these concepts, Section 2.2 outlines some applications of process mining in healthcare.

Recommendations for process mining researchers and the research community

Despite process mining's great potential to help healthcare organizations understand how their processes are actually executed, its use in healthcare outside a research context is limited. Starting from this observation, a two-day international brainstorm seminar took place to reflect upon how to enhance the usability and understandability of process mining in healthcare. This seminar brought together 18 experts from 11 different countries, both process mining researchers and healthcare

Recommendations for healthcare organizations and health information systems vendors

The predominant focus of this paper is on providing recommendations to process mining researchers and the research community to enhance the usability and understandability of process mining in healthcare. However, healthcare organizations and health information systems vendors also play a key role in a more widespread use of process mining as they should provide an environment which enables a continuous use of process mining. To this end, three key recommendations are provided to shape such an

Conclusions

Despite the unique potential of process mining to retrieve data-driven insights in healthcare processes, its uptake in healthcare organizations is rather limited. Given this observation, this paper synthesized the outcomes of a two-day international brainstorm seminar on how to enhance the usability and understandability of process mining in healthcare. Based on the discussions of 18 experts, both researchers and healthcare practitioners, a set of key recommendations is specified. Ten

Conflict of interest

There are no conflicts of interest to report.

Acknowledgment

This work has been partially supported by EIT Health Activity Nr. 20238.

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