Process Mining-Supported Emergency Room Process Performance Indicators

Cho, Minsu; Song, Minseok; Park, Junhyun; Yeom, Seok-Ran; Wang, Il Jae; Choi, Byung-Kwan

doi:10.3390/ijerph17176290

Cited by 19 publications

(10 citation statements)

References 44 publications

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“…Also, Battineni et al , by using a PM case study and extracting data from a hospital information system, evaluated the performance of the system with time indicators available in any PM tool (Battineni et al , 2019). Recently, some articles analyzed the performance of emergency rooms' processes by PM (Antunes et al , 2019; Cho et al , 2020), and also, it has been shown how insightful PM can be in health and safety performance (Pika et al , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Maddah

Roghanian

2021

IJPPM

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PurposeThe 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/approachFor 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.FindingsThe 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/implicationsThe 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 implicationsOrganizations' 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/valueThe 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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Maddah

Roghanian

2021

IJPPM

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“…In these situations aggregation of the events into a single node better represents the action associated with this process. Detecting the set of nodes which need aggregation is based on a graph-theoretical concept of graph clustering 14 . Node aggregation is based on the time-lag between the events as well as the event labeling -to avoid unique but simultaneous events from being aggregated.…”

Section: Node Aggregationmentioning

confidence: 99%

“…al. proposed several common ED process measures which could be evaluated through this method, yet recommendations for which data to use and what events to measure remains unclear [14].…”

Section: Introductionmentioning

confidence: 99%

Signal from the Noise: A Mixed Methods Process Mining Approach to Evaluate Care Pathways

Rose

Noshad

Chen

2021

Preprint

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ObjectiveMapping real-world practice patterns vs. deviations from intended guidelines and protocols is necessary to identify and improve the quality of care for emergent medical conditions like stroke. We propose a process mining algorithm applied to Electronic Health Record (EHR) event log data as a unique opportunity to more easily identify and compare real-world care processes.MaterialsData was obtained from the event log of a major EHR vendor (Epic) for Stanford Health Care Hospital patients aged 18 years and older presenting to the ED from January 1, 2010 through December 31, 2018 and receiving tPA within 4.5 hours of presentation. Our algorithm was built using the Python programming language.MethodsAn unsupervised process-mining algorithm was developed and used to create a process map for our cohort. This map was then used to identify the most common path as well as individual and average conformity to this path across all encounters.ResultsOur automatically generated process mining graph, specifically its most common path, mimicked our institution’s recommended “code stroke” clinical pathway. The average conformity score for our cohort was 0.36 with a range from high of 0.64 and low of 0.20.DiscussionThis method allows for greater detail into common process measures to be more easily illustrated to evaluate the quality of care at a given institution. It may be extended to other, similarly well-defined processes or those which currently lack standardized clinical pathways.ConclusionOur mixed methods approach represents an essential data analysis step to improve complex care processes by automatically generating a qualitative and quantitative process measures from existing event log data which can then be used to target quality improvement initiatives.

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“…Existing studies examine collaboration patterns between different healthcare professionals (e.g., nurse, physician, medical assistant) using network analysis [ 44 ] as well as the interplay of departments using a role interaction model [ 45 ]. Since collaboration and process efficiency are imperative in ER, optimization of an ER layout [ 46 ] or a development of process performance indicators for ER [ 47 ] is also performed.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of OMOP common data model for process mining in healthcare

et al. 2023

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Background and objective Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment. Methods We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques. Results Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM’s usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses. Conclusion To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community.

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Process Mining-Supported Emergency Room Process Performance Indicators

Cited by 19 publications

References 44 publications

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

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

Signal from the Noise: A Mixed Methods Process Mining Approach to Evaluate Care Pathways

Exploring the potential of OMOP common data model for process mining in healthcare

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