Investigation of treatment delay in a complex healthcare process using physician insurance claims data: an application to symptomatic carotid artery stenosis

Abstract

Background Delays in diagnostic and therapeutic processes are a potentially preventable cause of morbidity and mortality. Process improvement depends on accurate knowledge about as-is processes, historically collected from front-line workers and summarized in flowcharts. Such flowcharts can now be generated by process discovery algorithms supplied with chronological records from real-world cases. However, these algorithms may generate incomprehensible flowcharts when applied to complex unstructured processes, which are common in healthcare. The aim of this study is to evaluate methods for analysing data from real-world cases to determine causes of delay in complex healthcare processes. Methods Physician insurance claims and hospital discharge data were obtained for patients undergoing carotid endarterectomy at a single tertiary hospital between 2008 and 2014. All patients were recently symptomatic with vision loss. A chronological record of physician visits and diagnostic tests (activities) was generated for each patient using claims data. Algorithmic process discovery was attempted using the Heuristic Miner. The effect of activity selection on treatment delay was investigated from two perspectives: activity-specific effects were measured using linear regression, and patterns of activity co-occurrence were identified using K means clustering. Results Ninety patients were included, with a median symptom-to-surgery treatment time of 34 days. Every patient had a unique sequence of activities. The flowchart generated by the Heuristic Miner algorithm was uninterpretable. Linear regression models of waiting time revealed beneficial effects of emergency and neurology visits, and detrimental effects of carotid ultrasound and post-imaging follow-up visits to family physicians and ophthalmologists. K-means clustering identified two co-occurrence patterns: emergency visits, neurology visits and CT angiography were more common in a cluster of rapidly treated patients (median symptom to surgery time of 18 days), whereas family physician visits, carotid ultrasound imaging and post-imaging follow-up visits to eye specialists were more common in a cluster of patients with treatment delay (median time of 57 days). Conclusions Routinely collected data provided a comprehensive account of events in the symptom-to-surgery process for carotid endarterectomy. Linear regression and K-means clustering can be used to analyze real-world data to understand causes of delay in complex healthcare processes.