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Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals_ what is known and what remains uncertain_CQU.pdf (1.25 MB)

Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: What is known and what remains uncertain

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journal contribution
posted on 2024-04-15, 02:40 authored by Anton H van der Vegt, Victoria Campbell, Imogen Mitchell, James Malycha, Joanna Simpson, Tracy FlenadyTracy Flenady, Arthas Flabouris, Paul J Lane, Naitik Mehta, Vikrant R Kalke, Jovie A Decoyna, Nicholas Es’haghi, Chun-Huei Liu, Ian A Scott
Objective: To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework. Materials and methods: A systematic review of studies of implemented or trialed real-time clinical deterioration prediction MLAs was undertaken, which identified: how MLA implementation was measured; impact of MLAs on clinical processes and patient outcomes; and barriers, enablers and uncertainties within the implementation process. Review findings were then mapped to the SALIENT end-to-end implementation framework to identify the implementation stages at which these factors applied. Results: Thirty-seven articles relating to 14 groups of MLAs were identified, each trialing or implementing a bespoke algorithm. One hundred and seven distinct implementation evaluation metrics were identified. Four groups reported decreased hospital mortality, 1 significantly. We identified 24 barriers, 40 enablers, and 14 uncertainties and mapped these to the 5 stages of the SALIENT implementation framework. Discussion: Algorithm performance across implementation stages decreased between in silico and trial stages. Silent plus pilot trial inclusion was associated with decreased mortality, as was the use of logistic regression algorithms that used less than 39 variables. Mitigation of alert fatigue via alert suppression and threshold configuration was commonly employed across groups. Conclusions: There is evidence that real-world implementation of clinical deterioration prediction MLAs may improve clinical outcomes. Various factors identified as influencing success or failure of implementation can be mapped to different stages of implementation, thereby providing useful and practical guidance for implementers.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

31

Issue

2

Start Page

509

End Page

524

Number of Pages

16

eISSN

1527-974X

ISSN

1067-5027

Publisher

Oxford University Press (OUP)

Publisher License

CC BY

Additional Rights

CC BY 4.0 DEED

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2023-10-31

Era Eligible

  • Yes

Medium

Print

Journal

Journal of the American Medical Informatics Association