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Background: Acute care for critical illness requires very strict treatment timeliness. However, healthcare providers usually cannot accurately figure out the causes of low efficiency in acute care process due to the lack of effective tools. Besides, it is difficult to compare or conformance processes from different patient groups. Methods: To solve these problems, we proposed a novel process mining framework with time perspective, which integrates four steps: standard activity construction, data extraction and filtering, iterative model discovery, and performance analysis. Results: It can visualize the execution of actual clinical activities hierarchically, evaluate the timeliness and identify bottlenecks in the treatment process. We take the acute ischemic stroke as a case study, and retrospectively reviewed 420 patients' data from a large hospital. Then we discovered process models with timelines, and identified the main reasons for in-hospital delay. Conclusions: Experiment results demonstrate that the framework proposed could be a new way of drawing insights about hospitals' clinical process, to help clinical institutions increase work efficiency and improve medical service. [ABSTRACT FROM AUTHOR]

Background: Acute care for critical illness requires very strict treatment timeliness. However, healthcare providers usually cannot accurately figure out the causes of low efficiency in acute care process due to the lack of effective tools. Besides, it is difficult to compare or conformance processes from different patient groups.Methods: To solve these problems, we proposed a novel process mining framework with time perspective, which integrates four steps: standard activity construction, data extraction and filtering, iterative model discovery, and performance analysis.Results: It can visualize the execution of actual clinical activities hierarchically, evaluate the timeliness and identify bottlenecks in the treatment process. We take the acute ischemic stroke as a case study, and retrospectively reviewed 420 patients' data from a large hospital. Then we discovered process models with timelines, and identified the main reasons for in-hospital delay.Conclusions: Experiment results demonstrate that the framework proposed could be a new way of drawing insights about hospitals' clinical process, to help clinical institutions increase work efficiency and improve medical service. [ABSTRACT FROM AUTHOR]