TY - JOUR
T1 - Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness
AU - Herasevich, Vitaly
AU - Pickering, Brian W.
AU - Dong, Yue
AU - Peters, Steve G.
AU - Gajic, Ognjen
N1 - Funding Information:
This publication was made possible by grant 1 KL2 RR024151 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), the NIH Roadmap for Medical Research, and Mayo Foundation. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. Information on NCRR is available at http://www.www.ncrr.nih.gov/ . Information on Reengineering the Clinical Research Enterprise can be obtained from http://nihroamap.nih.gov/clinicalresearch/overviewtranslational.asp . This study was supported in part by National Heart, Lung and Blood Institute grant K23 HL78743-01A1 and NIH grant KL2 RR024151.
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
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U2 - 10.4065/mcp.2009.0479
DO - 10.4065/mcp.2009.0479
M3 - Article
C2 - 20194152
AN - SCOPUS:77649216610
SN - 0025-6196
VL - 85
SP - 247
EP - 254
JO - Mayo Clinic proceedings
JF - Mayo Clinic proceedings
IS - 3
ER -