Appl Clin Inform 2018; 09(01): 185-198
DOI: 10.1055/s-0038-1636508
Research Article
Schattauer GmbH Stuttgart

Validation and Refinement of a Pain Information Model from EHR Flowsheet Data

Bonnie L. Westra
Steven G. Johnson
Samira Ali
Karen M. Bavuso
Christopher A. Cruz
Sarah Collins
Meg Furukawa
Mary L. Hook
Anne LaFlamme
Kay Lytle
Lisiane Pruinelli
Tari Rajchel
Theresa Tess Settergren
Kathryn F. Westman
Luann Whittenburg
Further Information

Publication History

14 September 2017

15 January 2018

Publication Date:
14 March 2018 (online)


Background Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management.

Objective The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes.

Methods A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations.

Results The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability.

Conclusion The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.

Protection of Human and Animal Subjects

The data were considered “metadata” and represented descriptions of how the organization's EHR was designed and aggregated counts for frequency of use; no patient-identifiable data were included. Each participant consulted with their organization to determine whether Institutional Board Approval was needed. If Institutional Review Board (IRB) approval was required, it was obtained prior to data extraction and transmission to a secure database at the University of Minnesota.

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