Key result
A socio-technical study of clinical teams identified opportunities to align predictive machine learning models with collaborative clinical practices for proactive perioperative hypotension management.
Why the study?
While machine learning models can predict perioperative hypotension, little effort has been made to ground their formulation and development in the socio-technical context of clinical care work.
How can predictive machine learning models for hypotension be designed to align with the socio-technical context of perioperative care?
How can predictive machine learning models for hypotension be designed to align with the socio-technical context of perioperative care?
Grounding machine learning development in the socio-technical context of perioperative care can facilitate more proactive management of hypotension.
Answers are grounded in this paper and cite their sources.
Socio-technical alignment may guide ML design for perioperative hypotension; leaves open prospective validation of clinical impact.
Hypotension during perioperative care, if undetected or uncontrolled, can lead to serious clinical complications. Predictive machine learning models, based on routinely collected EHR data, offer potential for early warning of hypotension to enable proactive clinical intervention. However, while research has demonstrated the feasibility of such machine learning models, little effort is made to ground their formulation and development in socio-technical context of perioperative care work. To address this, we present a study of collaborative work practices of clinical teams during and after surgery with specific emphasis on the organisation of hypotension management. The findings highlight where predictive insights could be usefully deployed to reconfigure care and facilitate more proactive management of hypotension. We further explore how the socio-technical insights help define key parameters of machine learning prediction tasks to align with the demands of collaborative clinical practice. We discuss more general implications for the design of predictive machine learning in hospital care.
No takes yet. Share an insight, caveat, or question.
Ghosh et al. (2023) studied Hypotension during perioperative care. Predictive machine learning models was evaluated. A socio-technical study of clinical teams identified opportunities to align predictive machine learning models with collaborative clinical practices for proactive perioperative hypotension management.
Synapse has enriched 10 related papers. Consider them for comparative context: