Systems engineering and human factors support of a system of novel EHR-integrated tools to prevent harm in the hospital

Abstract

We established a Patient Safety Learning Laboratory comprising 2 core and 3 individual project teams to introduce a suite of digital health tools integrated with our electronic health record to identify, assess, and mitigate threats to patient safety in real time. One of the core teams employed systems engineering (SE) and human factors (HF) methods to analyze problems, design and develop improvements to intervention components, support implementation, and evaluate the system of systems as an integrated whole. Of the 29 participants, 19 and 16 participated in surveys and focus groups, respectively, about their perception of SE and HF. We identified 7 themes regarding use of the 12 SE and HF methods over the 4-year project. Qualitative methods (interviews, focus, groups, observations, usability testing) were most frequently used, typically by individual project teams, and generated the most insight. Quantitative methods (failure mode and effects analysis, simulation modeling) typically were used by the SE and HF core team but generated variable insight. A decentralized project structure led to challenges using these SE and HF methods at the project and systems level. We offer recommendations and insights for using SE and HF to support digital health patient safety initiatives.

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

“…AI has potential to assist clinicians in making better diagnoses [ 16 - 18 ], and has contributed to the fields of drug development [ 19 - 21 ], personalized medicine, and patient care monitoring [ 14 , 22 - 24 ]. AI has also been embedded in electronic health record (EHR) systems to identify, assess, and mitigate threats to patient safety [ 25 ]. However, with the deployment of AI in health care, several risks and challenges can emerge at an individual level (eg, awareness, education, trust), macrolevel (eg, regulation and policies, risk of injuries due to AI errors), and technical level (eg, usability, performance, data privacy and security).…”

Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study

“…Therefore, a HF-based design that involves analysing the actual work of clinicians, participation from clinicians in the design process and implementation of HF design principles can enhance the usability of health IT such as CDS. As data continue to emerge about poor usability and workflow integration of health IT and adverse patient safety consequences,64 it is essential to integrate HF methods and principles in technology design processes 11 65–68. This is particularly important with the emergence of artificial intelligence, which can be used to design CDS technologies that complement and enhance clinical decision-making 69–71…”

Using machine learning to minimize delays caused by prior authorization: A brief report

“…The integration of ML into the healthcare system is changing the dynamics, such as the role of healthcare providers and creating new potentials to improve patient safety (Macrae, 2019), as well as the quality of care (Grossman et al, 2018). It has assisted clinicians in making better diagnoses (Bahl et al, 2018;Guan et al, 2019;Li et al, 2019), improved drug safety (H Chen et al, 2018;Costabal et al, 2019;Ekins et al, 2019), and enhanced patient-care monitoring (Banerjee et al, 2019;Ciervo et al, 2019;Dalal et al, 2019;Jiang et al, 2017;Ronquillo et al, 2018). Machine learning enables computers to utilize labeled (supervised learning) or unlabeled data (unsupervised learning) to identify latent information or make classification about the data without explicit programming (Hashimoto et al, 2018;Jiang et al, 2017).…”