RESUMO
This research develops a novel dynamic mobile health (mHealth) application (app), called the Clinical Event Annotator (CEA). The CEA comprises of a native Android tablet app and an administrative web app. The native app is used at the patient bedside to manually annotate clinical events in real-time. Event types include patient monitor alarms, routine care, clinical interventions, and patient movements. The app can be dynamically updated with user-defined customized events. The web app generates reports of the annotation sessions. The CEA app is developed to support a clinical study that explores the use of pressure-sensitive mats (PSM) in the neonatal intensive care unit (NICU) to detect the respiratory rate (RR), heart rate (HR), and movement of critically ill neonatal patients. High-fidelity CEA app annotations are synced with a backend database that enables integration and synchronization with independently acquired patient monitoring data, such as RR, HR, and contact pressure data from the PSM. The gold standard CEA annotations serve the purpose of retrospectively training machine learning algorithms for clinical event detection. Preliminary test results from use of the app in the clinical study are presented. Development of the CEA app is a unique and novel contribution that addresses the well-known problem of manually annotating physiologic data streams to support clinical data mining applications.
