Performance Analysis of Emergency Room Episodes Through Process Mining

Mining context-aware resource profiles in the presence of multitasking

Healthcare organisations are becoming increasingly aware of the need to improve their care processes and to manage their scarce resources efficiently to secure high-quality care standards. As these processes are knowledge-intensive and heavily depend on human resources, a comprehensive understanding of the complex relationship between processes and resources is indispensable for efficient resource management. Organisational mining, a subfield of Process Mining, reveals insights into how (human) resources organise their work based on analysing process execution data recorded in Health Information Systems (HIS). This can be used to, e.g., discover resource profiles which are groups of resources performing similar activity instances, providing an extensive overview of resource behaviour within healthcare organisations. Healthcare managers can employ these insights to allocate their resources efficiently, e.g., by improving the scheduling and staffing of nurses. Existing resource profiling algorithms are limited in their ability to apprehend the complex relationship between processes and resources because they do not take into account the context in which activities were executed, particularly in the context of multitasking. Therefore, this paper introduces ResProMin-MT to discover context-aware resource profiles in the presence of multitasking. In contrast to the state-of-the-art, ResProMin-MT is capable of taking into account more complex contextual activity dimensions, such as activity durations and the degree of multitasking by resources. We demonstrate the feasibility of our method within a real-life healthcare context, validated by medical domain experts.

Process mining based on patient waiting time: an application in health processes

Purpose

Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method.

Design/methodology/approach

In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor.

Findings

The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors.

Research limitations/implications

The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution.

Originality/value

This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms.

Clinical and operational insights from data-driven care pathway mapping: a systematic review

Background

Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The “as is” or “de facto” care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained.

Methods

A systematic review was conducted of literature abstracting sequential patient care activities (“de facto” care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned.

Results

254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes.

Discussion

Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues.

Conclusion

This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01756-2.

Process mining framework with time perspective for understanding acute care: a case study of AIS in hospitals

Background

Acute care for critical illness requires very strict treatment timeliness. However, healthcare providers usually cannot accurately figure out the causes of low efficiency in acute care process due to the lack of effective tools. Besides, it is difficult to compare or conformance processes from different patient groups.

Methods

To solve these problems, we proposed a novel process mining framework with time perspective, which integrates four steps: standard activity construction, data extraction and filtering, iterative model discovery, and performance analysis.

Results

It can visualize the execution of actual clinical activities hierarchically, evaluate the timeliness and identify bottlenecks in the treatment process. We take the acute ischemic stroke as a case study, and retrospectively reviewed 420 patients’ data from a large hospital. Then we discovered process models with timelines, and identified the main reasons for in-hospital delay.

Conclusions

Experiment results demonstrate that the framework proposed could be a new way of drawing insights about hospitals’ clinical process, to help clinical institutions increase work efficiency and improve medical service.

Data-driven performance management of business units using process mining and DEA: case study of an Iranian chain store

Purpose

The underlying purpose of this paper is to propose a comprehensive framework evaluating the performance of business units of an organization with a process perspective, identifying the most influential performance indicators, enabling managers to make more informed decisions based on data recording every day in their operational information systems.

Design/methodology/approach

For proposing the conceptual framework of performance evaluation a synchronized analysis of selected process' data, obtained from an integrated information system of an Iranian chain store, was performed.

Findings

The superiority of the proposed framework results is demonstrated in comparison to applying the process mining solely; principal component analysis was identified as an efficient link between process mining and data envelopment analysis. Also, based on the final data analytics, the units' throughput times and the variety of brands and suppliers had the most impact on their performances.

Research limitations/implications

The data of abundant business units and performance indicators, which would have allowed adding data prediction and other data analytics techniques for more insight, was not able to be accessed.

Practical implications

Organizations' managers can use the framework to evaluate their business units' current status and then prioritize their resources based on the most influential performance indicators for overall improvement.

Originality/value

The study contributes to the research on performance management and process mining by presenting a comprehensive framework with two levels of data analytics. It stresses discovering what is happening in business units, and how to prioritize their improvement opportunities learning the significant correlations between performance indicators and units' performance.

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

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.

Process Mining-Supported Emergency Room Process Performance Indicators

Emergency room processes are often exposed to the risk of unexpected factors, and process management based on performance measurements is required due to its connectivity to the quality of care. Regarding this, there have been several attempts to propose a method to analyze the emergency room processes. This paper proposes a framework for process performance indicators utilized in emergency rooms. Based on the devil’s quadrangle, i.e., time, cost, quality, and flexibility, the paper suggests multiple process performance indicators that can be analyzed using clinical event logs and verify them with a thorough discussion with clinical experts in the emergency department. A case study is conducted with the real-life clinical data collected from a tertiary hospital in Korea to validate the proposed method. The case study demonstrated that the proposed indicators are well applied using the clinical data, and the framework is capable of understanding emergency room processes’ performance.

A Review of Various Methods of Management of Risk in the Field of Emergency Medicine

BACKGROUND:

The main concept of risk management in the emergency department (ED) contains a broader meaning, so that; it’s known as a sudden event or situation which would happen at an uncertain future that has some negative or positive impacts which could be called threat or opportunity respectively. However, the knowledge of risk management could cover the overall procedures involved with administering the planning of risk management, identification, investigation, monitoring and also step by step clinical examination. One of the main tools for preventing adversities is evaluating and management of possible risks.

AIM:

One of the main objectives of the present study is recognising the most frequent types of the risk happening in the EDs. Moreover, the present study is trying to evaluate the possible risks which could happen among various ED sections.

METHODS:

Six databases of EMBASE, HubMed, Cochrane Library, MEDLINE, PubMed, CHBD and Goggle scholar were chosen for discovering much-related articles from the year 2005 to 2019. A total number of 68 were chosen finally to be reviewed more precisely based on the main objective of the present study.

RESULTS:

Precise planning, preparing sufficiently and conducting the process of continuous monitoring are needed for ensuring the fact that any possible risks could be managed through these planned strategies. On the other hand, by modifying the patients’ beliefs, anticipations and the available social culture about the importance of risk management issue, the overall objective of the present study could be achieved at higher rates.

CONCLUSION:

Moreover, because the potential of occurrence of risk in EDs is high and approximately more than half of them are fatal, more precise adequate systematic plans for management of them should result.