Opportunities for machine learning to improve surgical ward safety.

ABSTRACT:

Background

Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward safety.

Data sources

Fifty-eight articles from Cochrane Library, EMBASE, and PubMed databases were included.

Conclusions

Only 15-20% of patients suffering ward arrest survive. In most cases, subtle signs of instability often occur prior to critical illness and arrest, and underlying pathology is reversible. Coarse risk assessments lead to under-triage of high-risk patients to wards, where surveillance for complications depends on time-consuming manual review of health records, infrequent patient assessments, prediction mod

SUBMITTER: Loftus TJ 

PROVIDER: S-EPMC7673643 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature