Optimal process mining of timed event logs

Oliveira, Hugo De; Augusto, Vincent; Jouaneton, B.; Lamarsalle, Ludovic; Prodel, Martin; Xie, Xiaolan

doi:10.1016/j.ins.2020.04.020

Cited by 14 publications

(20 citation statements)

References 12 publications

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“…From a medical point of view, it is extremely important to understand the relative timing of events, and which particular events and diagnoses place the patient at increased risk of sepsis. A similar process model structure—the Time Grid Process Model—has recently been introduced by De Oliveira et al, 22 where the time patterns are analyzed during model optimization, and characteristic time distributions are represented in the final discovered model. It may be beneficial to also apply this model to the sepsis pathway in order to address specific questions of interest.…”

Section: Discussionmentioning

confidence: 99%

“Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England

Oliveira

Prodel²,

Lamarsalle³

et al. 2020

JAMIA Open

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Objective The “Bow-tie” optimal pathway discovery analysis uses large clinical event datasets to map clinical pathways and to visualize risks (improvement opportunities) before, and outcomes after, a specific clinical event. This proof-of-concept study assesses the use of NHS Hospital Episode Statistics (HES) in England as a potential clinical event dataset for this pathway discovery analysis approach. Materials and Methods A metaheuristic optimization algorithm was used to perform the “bow-tie” analysis on HES event log data for sepsis (ICD-10 A40/A41) in 2016. Analysis of hospital episodes across inpatient and outpatient departments was performed for the period 730 days before and 365 days after the index sepsis hospitalization event. Results HES data captured a sepsis event for 76 523 individuals (>13 years), relating to 580 000 coded events (across 220 sepsis and non-sepsis event classes). The “bow-tie” analysis identified several diagnoses that most frequently preceded hospitalization for sepsis, in line with the expectation that sepsis most frequently occurs in vulnerable populations. A diagnosis of pneumonia (5 290 patients) and urinary tract infections (UTIs; 2 057 patients) most often preceded the sepsis event, with recurrent UTIs acting as a potential indicative risk factor for sepsis. Discussion This proof-of-concept study demonstrates that a “bow-tie” pathway discovery analysis of the HES database can be undertaken and provides clinical insights that, with further study, could help improve the identification and management of sepsis. The algorithm can now be more widely applied to HES data to undertake targeted clinical pathway analysis across multiple healthcare conditions.

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Section: Discussionmentioning

confidence: 99%

“Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England

Oliveira

Prodel²,

Lamarsalle³

et al. 2020

JAMIA Open

Self Cite

View full text Add to dashboard Cite

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“…in 2018 [6]. Applied to healthcare data event logs, the proposed method has been adapted to take into account temporal information during the optimization process [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, the labeling of events is a challenging step in data processing. A commune practice is the definition of labels by hand, based on expert knowledge [8]. The detection of hidden healthcare sub-processes has been proposed, using Hidden Markov Models (HMM) [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…imaging reports, or vital signs are available. Nevertheless, such a database is useful to map patient pathways [5]- [8], perform medical data clustering [9] and prediction tasks [10], [11]. Regarding healthcare processes, the complexity is multifold.…”

Section: Introductionmentioning

confidence: 99%

“…The number of clusters used was K = 15, which qualitatively appears as a wise trade-off between explainability of clusters and final process model readability. The process mining framework used was the one proposed in[8], designed for application to medical event logs. The maximum number of nodes, edges and positions for process model optimization was fixed to 15, 25 and 5, respectively.…”

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confidence: 99%

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Automatic and Explainable Labeling of Medical Event Logs With Autoencoding

Oliveira

Augusto

Jouaneton³

et al. 2020

IEEE J. Biomed. Health Inform.

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Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding, accurate labels are created by clustering similar events in latent space. Moreover, the explanation of created labels is provided by the decoding of its corresponding events. Tested on synthetic events, the method is able to find hidden clusters on sparse binary data, as well as accurately explain created labels. A case study on real healthcare data is performed. Results confirm the suitability of the method to extract knowledge from complex event logs representing patient pathways.

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Evaluation of patient transport service in hospitals using process mining methods: Patients' perspective

Kropp

Faeghi

Lennerts

2022

Health Planning & Management

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Designing healthcare facilities and their processes is a complex task which influences the quality and efficiency of healthcare services. The ongoing demand for healthcare services and cost burdens necessitate the application of analytical methods to enhance the overall service efficiency in hospitals. However, the variability in healthcare processes makes it highly complicated to accomplish this aim. This study addresses the complexity in the patient transport service process at a German hospital, and proposes a method based on process mining to obtain a holistic approach to recognise bottlenecks and main reasons for delays and resulting high costs associated with idle resources. To this aim, the event log data from the patient transport software system is collected and processed to discover the sequences and the timeline of the activities for the different cases of the transport process. The comparison between the actual and planned processes from the data set of the year 2020 shows that, for example, around 36% of the cases were 10 or more minutes delayed. To find delay issues in the process flow and their root causes the data traces of certain routes are intensively assessed.

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Optimal process mining of timed event logs

Cited by 14 publications

References 12 publications

“Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England

“Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England

Automatic and Explainable Labeling of Medical Event Logs With Autoencoding

Evaluation of patient transport service in hospitals using process mining methods: Patients' perspective

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