Process Mining of Disease Trajectories: A Literature Review

Kusuma, Guntur Prabawa; Kurniati, Angelina Prima; Rojas, Eric; McInerney, Ciarán; Gale, Chris P; Johnson, Owen

doi:10.3233/shti210200

Cited by 13 publications

(13 citation statements)

References 12 publications

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“…Furthermore, the Process Mining for Healthcare (PM4H) community recently described the process mining key challenges and especially the distinguishing characteristics that add to the complexity of using process mining within a healthcare context ( 28 ), including the need for tailored methodologies and frameworks, and the necessity to take into account concepts drifts given the changes of processes over time. These challenges have been partially tackled in the proposed framework, which mines the distinct change in pattern between the first and second wave of the pandemic, and identifies distinct disease trajectories, which has been recognized as an emerging area of study in process mining ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data

Dagliati

Gatta

Malovini

et al. 2022

Front. Public Health

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The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital “Istituti Clinici Salvatore Maugeri” in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

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Section: Discussionmentioning

confidence: 99%

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data

Dagliati

Gatta

Malovini

et al. 2022

Front. Public Health

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“…Process mining is commonly used for mining the sequence of activities but the time between activities can also be analysed. Process mining has been used in cancer (Kurniati et al, 2016), cardiovascular disease (Kusuma et al, 2017), dentistry (Fox et al, 2018;R S Mans et al, 2012), frailty (Farid et al, 2019), sepsis (Mannhardt & Blinde, 2017), and in primary care (Williams et al, 2018). Process mining is suitable for answering frequently posed questions by extracting information from an EHR (Mans et al, 2013).…”

Section: Process Mining In Healthcarementioning

confidence: 99%

Process Mining of Disease Trajectories: A Feasibility Study

Kusuma,

Sykes,

McInerney

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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Modelling patient disease trajectories from evidence in electronic health records could help clinicians and medical researchers develop a better understanding of the progression of diseases within target populations. Process mining provides a set of well-established tools and techniques that have been used to mine electronic health record data to understand healthcare care pathways. In this paper we explore the feasibility for using a process mining methodology and toolset to automate the identification of disease trajectory models. We created synthetic electronic health record data based on a published disease trajectory model and developed a series of event log transformations to reproduce the disease trajectory model using standard process mining tools. Our approach will make it easier to produce disease trajectory models from routine health data.

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“…Previous studies of process mining in healthcare have been done in specific domains, including primary care [9], cardiology [10], oncology [11], [12], and disease trajectory [13]. Our previous studies using the same dataset found that the dataset is potentially useful to progress research in process mining [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…For example, process mining in oncology can be used to analyse conformity to cancer treatment procedures, such as two-week-wait procedure. Whilst, process mining can also be used for disease trajectory analysis, to describe progression of diseases experienced by patients [13]. This study is limited in number, but potentially be useful to discover population-level patterns of disesases or comparison between countries.…”

Section: Introductionmentioning

confidence: 99%

Process Mining for Disease Trajectory Analysis on the Indonesia Health Insurance Data

Kurniati

Kusuma²,

Wisudiawan³

2022

Jur. Ris. Kom.

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Process mining has been implemented in many domains, including healthcare. In healthcare, process mining projects aimed to inform sequential patterns of processes based on the actual process executions as they are recorded in the event log. Event log as the main input of process mining tasks can be extracted from the automatically recorded data of patient treatments or diagnoses. By understanding common patterns of patient diagnoses, we can analyse disease trajectories of a cohort of patients. Disease trajectory analysis has been used to describe the course or progression of diseases, especially chronic diseases, as experienced over time. We applied process mining as the main methodology for disease trajectory analysis, following the process mining project methodology, to analyse patient records on the Indonesia Health Insurance (BPJS Kesehatan) Data Samples. We extracted the data samples, transform them into an event log, discover the disease trajectories based on process discovery algorithm, analyse it to inform their conformance to the event log. Contributions of our research are to promote process mining for disease trajectory analysis and to open wider opportunities to analyse Indonesia Health Insurance data representing Indonesia health conditions. As a case study, we explored disease trajectory of cancer patients

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Process Mining of Disease Trajectories: A Literature Review

Cited by 13 publications

References 12 publications

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data

Process Mining of Disease Trajectories: A Feasibility Study

Process Mining for Disease Trajectory Analysis on the Indonesia Health Insurance Data

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