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Modeling Flowsheet Data to Support Secondary Use

Westra, Bonnie L. PhD, RN, FAAN, FACMI; Christie, Beverly DNP, RN; Johnson, Steven G. PhD, MS; Pruinelli, Lisiane PhD, MSN, RN; LaFlamme, Anne DNP, RN; Sherman, Suzan G. PhD, RN; Park, Jung In PhD, BS, RN; Delaney, Connie W. PhD, RN, FAAN, FACMI; Gao, Grace DNP, RN; Speedie, Stuart PhD, FACMI

CIN: Computers, Informatics, Nursing: September 2017 - Volume 35 - Issue 9 - p 452–458
doi: 10.1097/CIN.0000000000000350

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same “thing,” but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

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Author Affiliations: School of Nursing (Drs Westra, Johnson, Pruinelli, Park, Delaney, and Gao) and Institute for Health Informatics, University of Minnesota (Drs Westra, Delaney, and Speedie); Fairview Health Services & University of Minnesota Health (Drs Christie, LaFlamme, and Sherman), Minneapolis, MN.

This study was supported by the National Center for Research Resources of the National Institutes of Health (NIH) through grant 1UL1RR033183 to the University of Minnesota Clinical and Translational Science Institute (CTSI).

The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the CTSI or the NIH. The University of Minnesota CTSI is part of a national Clinical and Translational Science Award consortium created to accelerate laboratory discoveries into treatments for patients.

The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

Corresponding author: Bonnie L. Westra, PhD, RN, FAAN, FACMI, University of Minnesota, School of Nursing, 308 Harvard St SE, WDH 5-140, Minneapolis, MN 55434 (

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