Using a Sociotechnical Model to Understand Challenges with Sepsis Recognition among Critically Ill Infants

Abstract

Objective The aim of the study is to apply a sociotechnical model to the requirements phase of implementing a machine learning algorithm-based system to support sepsis recognition in the neonatal intensive care unit.

Methods We incorporated components from the sociotechnical model, Safety in Engineering for Patient Safety 2.0, in three requirements phase activities: (1) semi-structured interviews, (2) user profiles, and (3) system use cases.

Results Thirty-one neonatal intensive care unit clinicians participated in semi-structured interviews (11 nurses, 10 front line ordering clinician, five fellows, and five attending physician). Interview transcripts were coded and then compiled into themes deductively based on components from the sociotechnical model (persons, environment, organization, tasks, tools and technology, collaboration, and outcomes). The interview analysis was used to create four user profiles defining responsibilities in sepsis recognition, team collaboration, and attributes relevant to sepsis recognition. Two user profiles (nurse, front line ordering clinician) included variants based on experience relevant to sepsis recognition. The interview analysis was used to develop three system use cases representing clinical sepsis scenarios. Each use case defines the precondition, actors, and high-level sequence of actions, and includes variants based on sociotechnical works system factors that can complicate sepsis recognition. The interview analysis, user profiles, and use cases serve as the foundation for supporting sociotechnical design to all subsequent human-centered design methods including subject recruitment, formative design, summative user testing, and simulation testing.

Conclusion Integration of the sociotechnical model-guided requirements gathering activities, analysis, and deliverables by framing a range of sociotechnical components and the interconnectedness of these components in the broader work system. Applying the sociotechnical model resulted in discovering work system, process, and outcome requirements that would otherwise be difficult to capture, or missed entirely, using traditional requirements gathering methods or approaches to clinical decision support design.

Keywords

clinical decision support - environment - clinical care - neonatology - pediatrics - domain - technology

Protection of Human and Animal Subjects

The study was determined to be exempt from human studies by the Children's Hospital of Philadelphia, Institutional Review Board. This research was funded by the Institute for Biomedical Informatics at the University of Pennsylvania School of Medicine and the Department of Biomedical and Health Informatics at the Children's Hospital of Philadelphia. The funding sources had no role in the conception, study design, data collection, analysis, or decision to publish this manuscript.

Author Contributions

D.J.K. contributions include the conception of the work, acquisition, analysis, and interpretation of data, drafting, and revising the manuscript. L.S., M.C.H., and R.W.G. contributions include the conception of the work, analysis, and interpretation of data, and revising the manuscript. G.P.S. contributions include the conception of the work, acquisition of data, and revising the manuscript. N.M. contributions include the conception of the work, acquisition, analysis, and interpretation of data, and revising the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.

Publikationsverlauf

Eingereicht: 15. September 2021

Angenommen: 14. April 2022

Artikel online veröffentlicht:
29. Juli 2022

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