Literature DB >> 26727681

An Approach to Acquiring, Normalizing, and Managing EHR Data From a Clinical Data Repository for Studying Pressure Ulcer Outcomes.

William V Padula1, Leon Blackshaw, C Tod Brindle, Samuel L Volchenboum.   

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.

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Year:  2016        PMID: 26727681     DOI: 10.1097/WON.0000000000000185

Source DB:  PubMed          Journal:  J Wound Ostomy Continence Nurs        ISSN: 1071-5754            Impact factor:   1.741


  2 in total

1.  Using clinical data to predict high-cost performance coding issues associated with pressure ulcers: a multilevel cohort model.

Authors:  William V Padula; Robert D Gibbons; Peter J Pronovost; Donald Hedeker; Manish K Mishra; Mary Beth F Makic; John Fp Bridges; Heidi L Wald; Robert J Valuck; Adam J Ginensky; Anthony Ursitti; Laura Ruth Venable; Ziv Epstein; David O Meltzer
Journal:  J Am Med Inform Assoc       Date:  2017-04-01       Impact factor: 4.497

2.  Surgical data science: The new knowledge domain.

Authors:  S Swaroop Vedula; Gregory D Hager
Journal:  Innov Surg Sci       Date:  2017-04-20
  2 in total

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