Informatics Infrastructure for Syndrome Surveillance, Decision Support, Reporting, and Modeling of Critical Illness

OBJECTIVE To develop and validate an informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. METHODS Using open-schema data feeds Imported from electronic medical records (EMRs), we developed a near-real-time relational database (Multidisciplinary Epidemiology and Translational Research in Intensive Care Data Mart). Imported data domains Included physiologic monitoring, medication orders, laboratory and radiologic investigations, and physician and nursing notes. Open database connectivity supported the use of Boolean combinations of data that allowed authorized users to develop syndrome surveillance, decision support, and reporting (data sniffers) routines. Random samples of database entries in each category were validated against corresponding Independent manual reviews. RESULTS The Multidisciplinary Epidemiology and Translational Research in Intensive Care Data Mart accommodates, on average, 15,000 admissions to the Intensive care unit (ICU) per year and 200,000 vital records per day. Agreement between database entries and manual EMR audits was high for sex, mortality, and use of mechanical ventilation (kappa,1.0 for all) and for age and laboratory and monitored data (Bland-Altman mean difference +/- SD, 1(0) for all). Agreement was lower for Interpreted or calculated variables, such as specific syndrome diagnoses (kappa, 0.5 for acute lung Injury), duration of ICU stay (mean difference +/- SD, 0.43+/-0.2), or duration of mechanical ventilation (mean difference +/- SD, 0.2+/-0.9). CONCLUSION Extraction of essential ICU data from a hospital EMIR into an open, integrative database facilitates process control, reporting, syndrome surveillance, decision support, and outcome research in the ICU.