Development and validation of a semi-automated surveillance system-lowering the fruit for non-ventilator-associated hospital-acquired pneumonia (nvHAP) prevention.

OBJECTIVES: Conducting manual surveillance of non-ventilator-associated hospital-acquired pneumonia (nvHAP) using ECDC (European Centre for Disease Prevention and Control) surveillance criteria is very resource intensive. We developed and validated a semi-automated surveillance system for nvHAP, and describe nvHAP incidence and aetiology at our hospital. METHODS: We applied an automated classification algorithm mirroring ECDC definition criteria to distinguish patients 'not at risk' from patients 'at risk' for suffering from nvHAP. 'At risk'-patients were manually screened for nvHAP. For validation, we applied the reference standard of full manual evaluation to three validation samples comprising 2091 patients. RESULTS: Among the 39 519 University Hospital Zurich inpatient discharges in 2017, the algorithm identified 2454 'at-risk' patients, reducing the number of medical records to be manually screened by 93.8%. From this subset, nvHAP was identified in 251 patients (0.64%, 95%CI: 0.57-0.73). Sensitivity, negative predictive value, and accuracy of semi-automated surveillance versus full manual surveillance were lowest in the validation sample consisting of patients with HAP according to the International Classification of Diseases (ICD-10) discharge diagnostic codes, with 97.5% (CI: 93.7-99.3%), 99.2% (CI: 97.9-99.8%), and 99.4% (CI: 98.4-99.8%), respectively. The overall incidence rate of nvHAP was 0.83/1000 patient days (95%CI: 0.73-0.94), with highest rates in haematology/oncology, cardiac and thoracic surgery, and internal medicine including subspecialties. CONCLUSIONS: The semi-automated surveillance demonstrated a very high sensitivity, negative predictive value, and accuracy. This approach significantly reduces manual surveillance workload, thus making continuous nvHAP surveillance feasible as a pivotal element for successful prevention efforts.

Mental models: a basic concept for human factors design in infection prevention.

Much of the effort devoted to promoting better hand hygiene is based on the belief that poor hand hygiene reflects poor motivation. We argue, however, that automatic unconscious behaviour driven by 'mental models' is an important contributor to what actually happens. Mental models are concepts of reality--imaginary, often blurred, and sometimes unstable. Human beings use them to reduce mental load and free up capacity in the conscious mind to focus on deliberate activities. They are pragmatic solutions to the complexity of life. Knowledge of such mental processes helps healthcare designers and clinicians overcome barriers to behavioural change. This article reviews the concept of mental models and considers how it can be used to improve hand hygiene and patient safety.