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.
The problem of determining the optimal process model of an event log of traces of events with temporal information is presented. A formal description of the event log and relevant complexity measures are detailed. Then the process model and its replayability score that measures model fitness with respect to the event log are defined. Two process models are formulated, taking into account temporal information. The first, called grid process model, is reminiscent of Petri net unfolding and is a graph with multiple layers of labeled nodes and arcs connecting lower to upper layer nodes. Our second model is an extension of the first. Denoted the time grid process model, it associates a time interval to each arc. Subsequently, a Tabu search algorithm is constructed to determine the optimal process model that maximizes the replayability score subject to the constraints of the maximal number of nodes and arcs. Numerical experiments are conducted to assess the performance of the proposed Tabu search algorithm. Lastly, a healthcare case study was conducted to demonstrate the applicability of our approach for clinical pathway modeling. Special attention was paid on readability, so that final users could beneficially use the process mining results.
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