Automated Detection of Patient Harm: Implementation and Prospective Evaluation of a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited Resources

Abstract

Objectives This study aimed to prospectively validate an application that automates the detection of broad categories of hospital adverse events (AEs) extracted from a basic hospital information system, and to efficiently mobilize resources to reduce the level of acquired patient harm. Methods Data were collected from an internally designed software, extracting results from 14 triggers indicative of patient harm, querying clinical and administrative databases including all inpatient admissions (n = 8760) from October 2019 to June 2020. Representative samples of the triggered cases were clinically validated using chart review by a consensus expert panel. The positive predictive value (PPV) of each trigger was evaluated, and the detection sensitivity of the surveillance system was estimated relative to incidence ranges in the literature. Results The system identified 394 AEs among 946 triggered cases, associated with 291 patients, yielding an overall PPV of 42%. Variability was observed among the trigger PPVs and among the estimated detection sensitivities across the harm categories, the highest being for the healthcare-associated infections. The median length of stay of patients with an AE showed to be significantly higher than the median for the overall patient population. Conclusions This application was able to identify AEs across a broad spectrum of harm categories, in a real-time manner, while reducing the use of resources required by other harm detection methods. Such a system could serve as a promising patient safety tool for AE surveillance, allowing for timely, targeted, and resource-efficient interventions, even for hospitals with limited resources.