Abstract
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.
Keywords
workflow (L01.906.893) - data collection (E05.318.308) - data analysis (H01.548.338)
- expert systems (L01.224.050.375.190)
