Subscribe to RSS
Validation and Refinement of a Pain Information Model from EHR Flowsheet Data
14 September 2017
15 January 2018
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.
Keywordspain management - nursing informatics - electronic health records and systems - knowledge modeling and representation - secondary use - efficiency improvement
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.
- 1 Goossen W, Goossen-Baremans A, van der Zel M. Detailed clinical models: a review. Healthc Inform Res 2010; 16 (04) 201-214
- 2 Johnson SG, Byrne MD, Christie B. , et al. Modeling flowsheet data for clinical research. AMIA Jt Summits Transl Sci Proc 2015; 2015: 77-81
- 3 Rutherford M a. Standardized nursing language: what does it mean for nursing practice?. Online J Issues Nurs 2008; 13 (01) 1-7
- 4 Westra BL, Delaney CW, Konicek D, Keenan G. Nursing standards to support the electronic health record. Nurs Outlook 2008; 56 (05) 258-266.e1
- 5 Westra BL, Christie B, Johnson SG. , et al. Modeling flowsheet data to support secondary use. Comput Informatics Nurs 2017; 35 (09) 452-458
- 6 Chow M, Beene M, O'Brien A. , et al. A nursing information model process for interoperability. J Am Med Inform Assoc 2015; 22 (03) 608-614
- 7 Harris MR, Langford LH, Miller H, Hook M, Dykes PC, Matney SA. Harmonizing and extending standards from a domain-specific and bottom-up approach: an example from development through use in clinical applications. J Am Med Inform Assoc 2015; 22 (03) 545-552
- 8 American Nurses Association & the American Society for Pain Management. Pain Management Nursing: Scope and Standards of Practice. 2nd ed. Silver Spring, MD: American Nurses Association; 2016
- 9 Nahin RL. Estimates of pain prevalence and severity in adults: United States, 2012. J Pain 2015; 16 (08) 769-780
- 10 Gaskin DJ, Richard P. The economic costs of pain in the United States. J Pain 2012; 13 (08) 715-724
- 11 The Joint Commission. Joint Commission Enhances Pain Assessment and Management Requirements for Accredited Hospitals. The Joint Commission Perspectives 2017;37(7):1-3. Available at: https://www.jointcommission.org/assets/1/18/Joint_Commission_Enhances_Pain_Assessment_and_Management_Requirements_for_Accredited_Hospitals1.PDF
- 12 Delaney CW, Pruinelli L, Alexander S, Westra BL. 2016 Nursing Knowledge Big Data Science Initiative. Comput Inform Nurs 2016; 34 (09) 384-386
- 13 Jarow JP, LaVange L, Woodcock J. Multidimensional evidence generation and FDA regulatory decision making: defining and using “real-world” data. JAMA 2017; 318 (08) 703-704
- 14 Matney SA, Settergren TT, Carrington JM, Richesson RL, Sheide A, Westra BL. Standardizing physiologic assessment data to enable big data analytics. West J Nurs Res 2016; 39 (01) 63-77
- 15 Carter-Templeton H, Effken J, Weaver C, Cochran K, Androwich I, O'Brien A. Toward a central repository for sharing nursing informatics' best practices. Comput Inform Nurs 2016; 34 (06) 245-246