LAUSR

BACKGROUND  Automation of health care workflows has recently become a priority. This can be enabled and enhanced by a workflow monitoring tool (WMOT). OBJECTIVES  We shared our experience in clinical workflow analysis via three cases studies in health care and summarized principles to design and develop such a WMOT. METHODS  The case studies were conducted in different clinical settings with distinct goals. Each study used at least two types of workflow data to create a more comprehensive picture of work processes and identify bottlenecks, as well as quantify them. The case studies were synthesized using a data science process model with focuses on data input, analysis methods, and findings. RESULTS  Three case studies were presented and synthesized to generate a system structure of a WMOT. When developing a WMOT, one needs to consider the following four aspects: (1) goal orientation, (2) comprehensive and resilient data collection, (3) integrated and extensible analysis, and (4) domain experts. DISCUSSION  We encourage researchers to investigate the design and implementation of WMOTs and use the tools to create best practices to enable workflow automation and improve workflow efficiency and care quality.