Preliminary Processing and Analysis of an Adverse Event Dataset for Detecting Sepsis-Related Events

Adverse event (AE) reports contain notes detailing procedural and guideline deviations, and unwanted incidents that can bring harm to patients. Available datasets mainly focus on vigilance or post-market surveillance of adverse drug reactions or medical device failures. The lack of clinical-related AE datasets makes it challenging to study healthcare-related AEs. AEs affect 10% of hospitalized patients, and almost half are preventable. Having an AE dataset can assist in identifying possible patient safety interventions and performing quality surveillance to lower AE rates. The free-text notes can provide insight into the cause of incidents and lead to better patient care. The objective of this study is to introduce a Norwegian AE dataset and present preliminary processing and analysis for sepsis-related events, specifically peripheral intravenous catheter-related bloodstream infections. Therefore, the methods focus on performing a domain analysis to prepare and better understand the data through screening, generating synthetic free-text notes, and annotating notes.