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 (κ, 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 (κ, 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 EMR into an open, integrative database facilitates process control, reporting, syndrome surveillance, decision support, and outcome research in the ICU.EMR = electronic medical record; ICU = intensive care unit; IRB = Institutional Review Board; METRIC = Multidisciplinary Epidemiology and Translational Research in Intensive Care; SQL = structured query languageThe relevance of care in the intensive care unit (ICU) to public health in the United States is reflected in annual figures of 4.4 million ICU admissions, 500,000 deaths, 13.3% of hospital costs, 4.2% of national health expenditures, and 0.56% of the gross domestic product.^1,2 This demand is expected to increase as the US population ages; patients older than 65 years currently account for more than 55% of all ICU days.^3,4 Unmeasured burdens include the high degree of disability and loss of productivity for both ICU survivors and their caregivers.^5-7The complexity of the ICU environment, characterized by a vast amount of information and the critical importance of timing of interventions, presents a major barrier to safe and efficient care delivery.^8,9 Recent advances in medical informatics and the anticipated widespread implementation of electronic medical records (EMRs) combine to provide an opportunity to facilitate processes for delivery of safe, high-quality care in the ICU.This article describes the development and implementation of the Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC) Data Mart, an informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness at Mayo Clinic.