Outils et méthodes de détection automatiques des événements indésirables en soins aigus hospitaliers

Background

Healthcare-related adverse events (AEs) are a prevalent and significant concern, occurring in approximately 10% of hospital stays and accounting for 15% of hospital expenses. Traditional screening methods rely on retrospective manual record reviews, demanding substantial human and temporal resources. The integration of artificial intelligence (AI) offers a promising avenue for more efficient detection of AEs.

Objective

The objective is to systematically review both scientific and grey literature on automated tools for detecting healthcare-related adverse events.

Methods

We will conduct a comprehensive search using established Cochrane methodologies in Medline, Embase, CINAHL, Cochrane Library, Joanna Briggs Institute, Web of Science, and Google Scholar from 2009 to 2024. This search also included a review of references from selected articles.

Our inclusion criteria encompass all articles in French, English, and German related to healthcare-related adverse events and automated screening, without any exclusion based on study type or patient age.

Two independent reviewers will undertake study selection, data extraction, risk of bias assessment, and GRADE evaluation. 

Results will include: 

1. Methodological characteristics such as the automated screening method, its metrics, comparison with non-automated methods, data sources, and patient selection criteria
2. Characteristics of AEs like prevalence/incidence, types and classification, validation criteria, and severity
3. Evaluation for potential inclusion in the Swiss quality framework, international comparisons, cost-benefit analysis, and examination of facilitators and barriers to implementation. Results will be categorized by healthcare setting (acute or long-term hospital stay, outpatient).

Results

An initial search across various databases indicates a need to screen around 4,000 abstracts (prior to duplicate removal). Results will be presented at the conference.

Conclusions

The review aims to identify or develop cost-effective and reliable automated methods for screening and monitoring healthcare adverse events across various settings. This will inform Swiss healthcare policy in Quality and Safety.