An Approach to Acquiring, Normalizing, and Managing EHR Data From a Clinical Data Repository for Studying Pressure Ulcer Outcomes. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Padula, William V.; Blackshaw, Leon; Brindle, C. Tod; Volchenboum, Samuel L.
• Is Part Of: Journal of WOCN. Volume 43:Issue 1(2016)
• Publication Date: 2016-01
• Subjects: Data mining -- Data warehouse -- Informatics -- Pressure ulcer; Abdomen -- Surgery -- Periodicals; Abdomen -- Wounds and injuries -- Nursing -- Periodicals; Bedsores -- Nursing -- Periodicals; Fecal incontinence -- Nursing -- Periodicals; Enterostomy -- Nursing -- Periodicals; Urinary incontinence -- Nursing -- Periodicals; 610.73
• DOI: 10.1097/WON.0000000000000185
• Languages: English
• ISSNs: 1071-5754

Abstract

Abstract : Changes in the methods that individual facilities follow to collect and store data related to hospital-acquired pressure ulcer (HAPU) occurrences are essential for improving patient outcomes and advancing our understanding the science behind this clinically relevant issue. Using an established electronic health record system at a large, urban, tertiary-care academic medical center, we investigated the process required for taking raw data of HAPU outcomes and submitting these data to a normalization process. We extracted data from 1.5 million patient shifts and filtered observations to those with a Braden score and linked tables in the electronic health record, including (1) Braden scale scores, (2) laboratory outcomes data, (3) surgical time, (4) provider orders, (5) medications, and (6) discharge diagnoses. Braden scores are important measures specific to HAPUs since these scores clarify the daily risk of a hospitalized patient for developing a pressure ulcer. The other more common measures that may be associated with HAPU outcomes are important to organize in a single data frame with Braden scores according to each patient. Primary keys were assigned to each table, and the data were processed through 3 normalization steps and 1 denormalization step. These processes created 8 tables that can be stored efficiently in a clinical database of HAPU outcomes. As hospitals focus on organizing data for review of HAPUs and other types of hospital-acquired conditions, the normalization process we describe in this article offers directions for collaboration between providers and informatics teams using a common language and structure. Abstract : Supplemental Digital Content is Available in the Text.