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Open Access Dissertation
Doctor of Philosophy (PhD)
Year Degree Awarded
Health Information Technology | Industrial Engineering | Software Engineering
Human-intensive processes (HIPs), such as medical processes involving coordination among doctors, nurses, and other medical staff, often play a critical role in society. Despite considerable work and progress in error reduction, human errors are still a major concern for many HIPs.
To address this problem of human errors in HIPs, this thesis investigates two approaches for online process guidance, i.e., for guiding process performers while a process is being executed. Both approaches rely on monitoring a process execution and base the guidance they provide on a detailed formal process model that captures the recommended ways to perform the corresponding HIP. The first approach, which we call deviation detection and explanation, automatically detects when an executing HIP deviates from a set of recommended executions of that HIP, as specified by the process model. Such deviations could represent errors and, thus, detecting and reporting deviations as they occur could help catch errors before something bad happens. The approach also provides information to help explain a detected deviation to assist process performers with identifying potential errors and with planning recovery from these errors. The second approach, which we call process state visualization, proactively guides process performers by showing them information relevant to the current process execution, such as the activities that need to be performed at each point of that process execution. The goal of the process state visualization approach is to reduce the number of human errors.
The major contributions of this work can be summarized as follows:
-- Compared the relative strengths and weaknesses of several techniques for process elicitation and process model validation to help create correct and sufficiently complete process models needed for the proposed online process guidance approaches.
-- Developed an approach for deviation detection and explanation and evaluated it with realistic process models and synthetic process executions with seeded errors.
* Recognized delayed deviation detection as a potential obstacle for the approach and investigated its frequency and consequences.
-- Developed an initial approach for visualization of process execution state and demonstrated it on a medical case study.
Christov, Stefan, "Model-Based Guidance for Human-Intensive Processes" (2015). Doctoral Dissertations. 295.