Process Mining of Disease Trajectories: A Feasibility Study

Kusuma, Guntur Prabawa; Sykes, Samantha; McInerney, Ciarán; Johnson, Owen

doi:10.5220/0009166607050712

Cited by 14 publications

(11 citation statements)

References 29 publications

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“…As a heuristic method, process mining examines data sets as a whole by analysing an event log containing patient diagnoses and dates as cases. Using this data mining approach, it is feasible to map common temporal disease trajectories end to end and examine the direction of multimorbidities (Kusuma et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Multimorbid disease trajectories for people with periodontitis

Larvin

Kang

Aggarwal

et al. 2021

J Clinic Periodontology

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Aim: Periodontitis is a multifactorial condition linked to increased risk of systemic diseases. This study aimed to identify disease trajectories of people with periodontitis using the process mining technique as a heuristic approach.Materials and methods: A total of 188,863 participants from the UK Biobank cohort were included. Self-reported oral health indicators (bleeding gums, painful gums, loose teeth) were surrogates for periodontitis at baseline. Systemic disease diagnoses and dates formed the process mining event log. Relative risk (RR) of systemic diseases, disease trajectories, and Cox proportional hazard ratio models for mortality were compared to age-and sex-matched controls who did not report a history of periodontitis.Results: Participants with loose teeth had shorter median time to most systemic diseases, and crude RR was increased for several diseases including cardiovascular dis-

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Section: Introductionmentioning

confidence: 99%

Multimorbid disease trajectories for people with periodontitis

Larvin

Kang

Aggarwal

et al. 2021

J Clinic Periodontology

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“…This evaluation is not dissimilar to the approach taken by Jensen et al 21 to identify disease (diagnostic) trajectories from a population-wide registry data set in Denmark. A feasibility assessment of applying PM algorithms to evaluating the disease trajectories outlined by Jensen et al has more recently been undertaken by Kusuma et al, 6 and our application of a PM discovery algorithm to identifying diagnostic trajectories for sepsis using an existing electronic health dataset provides further identification of the potential of this approach. The timeframe selected was 2 years prior and 1 year following a sepsis diagnosis.…”

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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“…Recent examples of such studies follow. In [30] Kusuma et al presented a novel application of PM focused on a feasibility study of disease trajectories. Gatta et al presented a framework for event log generation and knowledge representation for PM in healthcare [31].…”

Section: Related Workmentioning

confidence: 99%

Comparative Assessment of Data-driven Process Models in Health Information Technology

Klasky

Özmen

Omitaomu

et al. 2021

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Background and PurposeProcess mining for conformance analysis consists of comparing a reference process model against a datadriven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. MethodsWe present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. ResultsThe comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman's correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. ConclusionsThe foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.

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Process Mining of Disease Trajectories: A Feasibility Study

Cited by 14 publications

References 29 publications

Multimorbid disease trajectories for people with periodontitis

Multimorbid disease trajectories for people with periodontitis

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

Comparative Assessment of Data-driven Process Models in Health Information Technology

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