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

https://doi.org/10.4065/mcp.2009.0479Get rights and content

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.

Section snippets

METHODS

The Institutional Critical Care Committee approved and supported the METRIC Data Mart project. Mayo Clinic Institutional Review Board (IRB) approval is required for specific research studies using METRIC Data Mart.

The Mayo Clinic campus in Rochester, MN, is an academic medical center with 1900 beds and 135,000 hospital admissions per year. The combined capacity of the ICUs is 204 beds and 14,800 admissions per year. Saint Marys Hospital has 183 ICU beds: 24 general medical, 16 medical

RESULTS

Data sources, metadata, and frequency and timeliness of updates for individual categories of data available within the METRIC Data Mart are described in Table 1. A “demo” table contains all demographic information and serves as the main index for ICU admissions. The combination of the patient's medical record number and the time of ICU admission serves as a unique identifier in the database.

Table 2 lists the results of random sample validation by manual record review for each specific category

DISCUSSION

With implementation of a comprehensive EMR at our institution, an opportunity arose to construct and validate the METRIC Data Mart, a custom, near real-time data warehouse.

After an Institute of Medicine Report19 highlighted an unacceptable rate of medical errors in hospitalized patients, the National Institutes of Health Roadmap called for the urgent support of patient safety initiatives, quality improvement, and translational outcome research. All these initiatives are facilitated by a

CONCLUSION

The METRIC Data Mart provides a novel informatics infrastructure for the extraction of ICU data from a hospital EMR into an open, integrative database. With the inevitable arrival of the EMR in hospital practice, the availability of an appropriately designed and implemented resource, such as that described in this study, will become an essential requirement for the conduct of activities such as process control, reporting, syndrome surveillance, decision support, and outcome research in the ICU.

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    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.

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