Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study.

ABSTRACT:

Objective

Many options are currently available for sepsis surveillance clinical decision support (CDS) from electronic medical record (EMR) vendors, third party, and homegrown models drawing on rule-based (RB) and machine learning (ML) algorithms. This study explores sepsis CDS implementation from the perspective of implementation leads by describing the motivations, tool choices, and implementation experiences of a diverse group of implementers.

Materials and methods

Semi-structured interviews were conducted with and a questionnaire was administered to 21 hospital leaders overseeing CDS implementation at 15 US medical centers. Participants were recruited via convenience sampling. Responses were coded by 2 coders with consensus approach and inductively analyzed for themes.

SUBMITTER: Joshi M 

PROVIDER: S-EPMC9030109 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature