RESEARCH ARTICLE


Validating Emergency Department Vital Signs Using a Data Quality Engine for Data Warehouse



N Genes*, 1, D Chandra 2, S Ellis3, K Baumlin1
1 Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
2 Information Technology, Mount Sinai Medical Center, New York, NY, USA
3 Research Information Technology, Mount Sinai Medical Center, New York, NY, USA


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© Genes et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, Box 1620, One Gustav L. Levy Place, 1190 Fifth Avenue, New York, NY 10029, USA; Tel: 212-824-8073; Fax: 212-426-1946; E-mail: nicholas.genes@mountsinai.org


Abstract

Background:

Vital signs in our emergency department information system were entered into free-text fields for heart rate, respiratory rate, blood pressure, temperature and oxygen saturation.

Objective:

We sought to convert these text entries into a more useful form, for research and QA purposes, upon entry into a data warehouse.

Methods:

We derived a series of rules and assigned quality scores to the transformed values, conforming to physiologic parameters for vital signs across the age range and spectrum of illness seen in the emergency department.

Results:

Validating these entries revealed that 98% of free-text data had perfect quality scores, conforming to established vital sign parameters. Average vital signs varied as expected by age. Degradations in quality scores were most commonly attributed logging temperature in Fahrenheit instead of Celsius; vital signs with this error could still be transformed for use. Errors occurred more frequently during periods of high triage, though error rates did not correlate with triage volume.

Conclusions:

In developing a method for importing free-text vital sign data from our emergency department information system, we now have a data warehouse with a broad array of quality-checked vital signs, permitting analysis and correlation with demographics and outcomes.

Keywords: Data warehouse, electronic health records, emergency medicine, hospital information systems, text mining, user computer interface, vital signs.