Danny T Y Wu1,2, Lindsey Barrick3,2, Mustafa Ozkaynak4, Katherine Blondon5,6, Kai Zheng7. 1. Department of Biomedical Informatics, University of Cincinnati College of Medicine, Ohio, United States. 2. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States. 3. Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States. 4. College of Nursing, University of Colorado-Anschutz Medical Campus, Aurora, Colorado, United States. 5. Medical and Quality Directorate, University Hospitals of Geneva, Geneva, Switzerland. 6. Faculty of Medicine, University of Geneva, Geneva, Switzerland. 7. Department of Informatics, University of California, Irvine, Irvine, California, United States.
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. Thieme. All rights reserved.
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. Thieme. All rights reserved.
Authors: Meryl Bloomrosen; Justin Starren; Nancy M Lorenzi; Joan S Ash; Vimla L Patel; Edward H Shortliffe Journal: J Am Med Inform Assoc Date: 2011 Jan-Feb Impact factor: 4.497
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Authors: Kevin M Overmann; Danny T Y Wu; Catherine T Xu; Shwetha S Bindhu; Lindsey Barrick Journal: J Am Med Inform Assoc Date: 2021-06-12 Impact factor: 4.497