PREVENTION OF FALSE POSITIVES IN INFECTION OUTBREAK DETECTION: AN ADVANCED SURVEILLANCE SYSTEM

Background : False-positive laboratory results are a persistent challenge in Infection Prevention and Control (IPC), often leading to pseudo-outbreaks, unnecessary antimicrobial therapy, misallocation of resources, and inflated healthcare-associated infection (HAI) indicators. Traditional surveillance systems frequently rely on isolated culture results without adequate clinical or radiological correlation, heightening the risk of misclassification. The attached manuscript emphasises the need to integrate clinical signs, laboratory markers, and microbiological confirmation to distinguish true infections from contamination events.

Aim: To evaluate causes, patterns, and consequences of false-positive infection detection and to develop an advanced surveillance system incorporating the Triangular Confirmation Model to enhance diagnostic accuracy and prevent pseudo-outbreaks.

Methods : A mixed-methods research design was adopted. Quantitative data were collected from retrospective microbiological records, culture contamination logs, radiology reports, and electronic health records. Qualitative data were obtained through semi-structured interviews with IPC practitioners, clinical microbiologists, and nursing personnel. Quantitative data were analysed using descriptive and inferential statistics to determine contamination prevalence and predictors, while qualitative interviews underwent thematic analysis. Triangulation was used to synthesise findings across data sources.

Results : False-positive events were strongly associated with inadequate specimen collection practices, environmental contamination, inappropriate culture ordering, and single-set culture interpretation. Clinical assessment was frequently underutilised during early outbreak evaluation. Integration of clinical signs, radiological findings, and culture confirmation significantly reduced misclassification compared with culture-based surveillance alone. The proposed Triangular Confirmation Model —comprising (1) clinical and epidemiological assessment, (2) laboratory and radiological markers, and (3) microbiological culture validation—demonstrated improved reliability in identifying true infections. When embedded within an advanced surveillance framework augmented by automated alerts, trend analysis, and contamination probability scoring, the model further decreased false alarms and unnecessary IPC interventions.

Conclusion : False-positive laboratory results pose substantial clinical, operational, and economic burdens. A multidimensional surveillance approach using the Triangular Confirmation Model enhances diagnostic precision, supports antimicrobial stewardship, and minimises pseudo-outbreak responses. Adoption of advanced, integrated surveillance systems can strengthen IPC programmes and improve patient safety outcomes across diverse healthcare settings.