Adoption of novel biomarker test parameters with machine learning-based algorithms for the early detection of sepsis in hospital practice.
artificial intelligence
biomarker
machine learning
nursing and hospital practice
sepsis
Journal
Journal of nursing management
ISSN: 1365-2834
Titre abrégé: J Nurs Manag
Pays: England
ID NLM: 9306050
Informations de publication
Date de publication:
Nov 2022
Nov 2022
Historique:
revised:
29
07
2022
received:
03
05
2022
accepted:
14
09
2022
pubmed:
21
9
2022
medline:
30
12
2022
entrez:
20
9
2022
Statut:
ppublish
Résumé
We aim (i) to redesign sepsis's clinical pathway and fit the organizational requirements of a novel machine-learning algorithm incorporating a novel biomarker test and (ii) to assess adoption drivers of the new combined technology. There is an urgent need to achieve sepsis' early detection and diagnostic excellence. A qualitative study based on semi-structured interviews conducted at the target site and across other Italian hospitals. A content analysis was undertaken, emergent themes were selected and categorized, and interviews were conducted until saturation was reached. Sixteen nurses (10 at the target site and six across other hospitals) and nine non-nursing professionals (seven at the target site and two across other hospitals) were interviewed. An organizational redesign was identified as the primary adoption driver. Even though nurses perceived workload increase related to the machine-learning component, technology acceptability was relatively high, as the standardization of tasks was perceived as crucial to improving professional satisfaction. A novel business-oriented solution based on machine learning requires interprofessional integration, new professional roles, infrastructure improvement, and data integration to be effectively implemented. Lessons learned from this study suggest the need to involve nurses in the early stages of the design of new machine-learning technologies and the importance of training nurses on sepsis management through the support of disruptive technological innovation.
Sections du résumé
AIMS
OBJECTIVE
We aim (i) to redesign sepsis's clinical pathway and fit the organizational requirements of a novel machine-learning algorithm incorporating a novel biomarker test and (ii) to assess adoption drivers of the new combined technology.
BACKGROUND
BACKGROUND
There is an urgent need to achieve sepsis' early detection and diagnostic excellence.
METHODS
METHODS
A qualitative study based on semi-structured interviews conducted at the target site and across other Italian hospitals. A content analysis was undertaken, emergent themes were selected and categorized, and interviews were conducted until saturation was reached.
RESULTS
RESULTS
Sixteen nurses (10 at the target site and six across other hospitals) and nine non-nursing professionals (seven at the target site and two across other hospitals) were interviewed. An organizational redesign was identified as the primary adoption driver. Even though nurses perceived workload increase related to the machine-learning component, technology acceptability was relatively high, as the standardization of tasks was perceived as crucial to improving professional satisfaction.
CONCLUSIONS
CONCLUSIONS
A novel business-oriented solution based on machine learning requires interprofessional integration, new professional roles, infrastructure improvement, and data integration to be effectively implemented.
IMPLICATIONS FOR NURSING MANAGEMENT
CONCLUSIONS
Lessons learned from this study suggest the need to involve nurses in the early stages of the design of new machine-learning technologies and the importance of training nurses on sepsis management through the support of disruptive technological innovation.
Identifiants
pubmed: 36125938
doi: 10.1111/jonm.13807
pmc: PMC10092467
doi:
Types de publication
Journal Article
Langues
eng
Pagination
3754-3764Subventions
Organisme : Beckman Coulter Foundation
Informations de copyright
© 2022 The Authors. Journal of Nursing Management published by John Wiley & Sons Ltd.
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