The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study.
Syst Rev
; 12(1): 7, 2023 01 17.
Article
en En
| MEDLINE
| ID: mdl-36650579
ABSTRACT
BACKGROUND:
Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and the need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews.METHODS:
This protocol describes how we will answer two research questions using a retrospective studydesign:
Is there a difference in resources used to produce reviews using recommended ML versus not using ML, and is there a difference in time-to-completion? We will also compare recommended ML use to non-recommended ML use that merely adds ML use to existing procedures. We will retrospectively include all reviews conducted at our institute from 1 August 2020, corresponding to the commission of the first review in our institute that used ML.CONCLUSION:
The results of this study will allow us to quantitatively estimate the effect of ML adoption on resource use and time-to-completion, providing our organization and others with better information to make high-level organizational decisions about ML.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
Tipo de estudio:
Observational_studies
/
Policy_brief
Límite:
Humans
Idioma:
En
Revista:
Syst Rev
Año:
2023
Tipo del documento:
Article
País de afiliación:
Noruega