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Nurses have been described as particularly adept at identifying and utilizing workarounds to overcome poor system designs that disrupt workflows. Workflows and workarounds are not limited to directly observable patient care activities; they also occur within documentation activities and can be modeled using metadata (data about data) from clinical information systems. Health systems engineering is an approach to effectively implement a learning health system that drives more efficient and safer care by adapting and aligning individual structures (e.g., applications) and processes (e.g., workflows) to optimize outcomes within a “system of systems”. This chapter will outline three broad approaches that can be triangulated within a systems engineering framework to reengineer nursing and patient care workflows to overcome information silos by actively learning from health system safety information gaps and workarounds. Systems engineering can be applied to a range of healthcare processes leveraging a five-phase model of problem analysis, design, development, implementation, and evaluation. The three aforementioned approaches can be applied during the various phases to model workflow, data and information flow, and to support the development, integration, and optimization of health IT applications. In this chapter we present use cases of pragmatic applications grounded in theoretical and methodological approaches and situated within a systems engineering framework that demonstrate the iterative nature of health IT evaluation. We also highlight the complexity of nursing and patient care workflows and substantiate that even well-designed systems that adequately address sociotechnical dimensions as part of the development process, require continued attention to workflow during and after implementation. To achieve a learning health care system that optimizes workflow, it requires nursing and data science and systems engineering domain expertise and working collaboratively throughout the system life cycle to contextualize clinical analyses and to successfully convert data into knowledge.

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Reengineering Approaches for Learning Health Systems: Learning from Safety Information Gaps and Workarounds to Develop Effective and Usable Health IT Systems

  • 章节

  • 发表日期：

    2025年

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  • Jennifer Thate,
  • Sarah Rossetti,
  • Po-Yin Yen,
  • Patricia C. Dykes,
  • Kumiko Schnock,
  • Kenrick Cato

摘要

Nurses have been described as particularly adept at identifying and utilizing workarounds to overcome poor system designs that disrupt workflows. Workflows and workarounds are not limited to directly observable patient care activities; they also occur within documentation activities and can be modeled using metadata (data about data) from clinical information systems. Health systems engineering is an approach to effectively implement a learning health system that drives more efficient and safer care by adapting and aligning individual structures (e.g., applications) and processes (e.g., workflows) to optimize outcomes within a “system of systems”. This chapter will outline three broad approaches that can be triangulated within a systems engineering framework to reengineer nursing and patient care workflows to overcome information silos by actively learning from health system safety information gaps and workarounds. Systems engineering can be applied to a range of healthcare processes leveraging a five-phase model of problem analysis, design, development, implementation, and evaluation. The three aforementioned approaches can be applied during the various phases to model workflow, data and information flow, and to support the development, integration, and optimization of health IT applications. In this chapter we present use cases of pragmatic applications grounded in theoretical and methodological approaches and situated within a systems engineering framework that demonstrate the iterative nature of health IT evaluation. We also highlight the complexity of nursing and patient care workflows and substantiate that even well-designed systems that adequately address sociotechnical dimensions as part of the development process, require continued attention to workflow during and after implementation. To achieve a learning health care system that optimizes workflow, it requires nursing and data science and systems engineering domain expertise and working collaboratively throughout the system life cycle to contextualize clinical analyses and to successfully convert data into knowledge.