Principles for Designing and Developing a Workflow Monitoring Tool to Enable and Enhance Clinical Workflow Automation

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.

Optimization of Electronic Health Record Usability Through a Department-Led Quality Improvement Process

“…This disconnect, dominant in the notes work group (88%), aligns with the current literature that better education during implementation allows for better interface functioning. 3,14,18,20,24 As this is a well-established problem but continually occurs, we ask, "How do we fix the disconnect that continues to allow EHR implementation to be suboptimal for clinician needs?" Sadly, the second largest proportion of optimizations (35%) were accommodations by the department adjusting workflow issues outside of the EHR.…”

“…20 This results in an poorly aligned interface that fails to meet the clinicians' needs as the developers lack an understanding of the clinicians' workflow and the clinicians fail to appreciate all of the nuanced digital amenities. 3,18,20,24 Overall efficient usability (speed and accuracy of task completion) can be compromised with this approach. Compromised usability directly relates to problems in productivity, error rate, and satisfaction.…”

“…20 This is better handled in an optimization process, as developers rarely conduct a deep dive into the granular aspects of daily patient care before, and users rarely become deeply involved in, implementation. 3,14,18,20,24 Therefore, it was essential for the project's aim of improving our EHR's summative usability that sufficient time and resources were afforded to fully understand each component of all aspects of all daily workflows, maximize their efficiency, identify those responsible for them, and create standardized best practices for each workflow.…”

“…When implementation fails to yield an EHR with high usability, optimization is necessary. Optimization is the process of continually improving a standing EHR 1,3,24 using data analysis through real-time feedback 16 and a focus on efficiency of patient outcomes, workflow, and sustainability. 1,17 The quality improvement process is identified as a method for conducting the optimization process.…”

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

“…Most of the existing clinical decision support systems are developed based on knowledge extracted from the available evidential dataset. 28 Though the accuracy of such systems is impressive, the knowledge extracted is sensitive to the quality of data. Moreover, decision-makers (clinicians) need systems that consider experts' preferences for decision-making.…”