Leveraging Electronic Health Record Data For Whole-Person Knowledge Discovery
2018-04
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Leveraging Electronic Health Record Data For Whole-Person Knowledge Discovery
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2018-04
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This dissertation presents strengths data capture in electronic health record (EHR) documentation, risk assessment and management using a strengths-based data capture model, and a strengths-based research study within a wellbeing context using de-identified EHR data and a data-driven model. It starts with the description of the current state of documentation of problems and strengths in the EHRs. There exists a gap of strengths data in EHRs that predominantly follow problem-based infrastructure in the healthcare information system, and there are also emerging new data sources that include strengths data in EHRs. Following this trend, this dissertation examines the potential of leveraging the use of a proposed Strengths-based Data Capture Model in health risk assessment and management. This model adds a whole-person perspective including the purposeful use of strengths data as health assets in data capture, aggregation, and person-driven application throughout the process of risk assessment and management. It concludes by a strengths-based research in older adults using a data-driven model to aid data-mining discovery of associations among older adults’ strengths, problems, planned nursing interventions, and baseline Knowledge, Behavior, and Status scores using EHR data captured by the Omaha System. By integrating strengths-based documentation, data capture model, and research, this dissertation introduces a cutting-edge data capture model, and creates a platform for continued research and application of a strength-based ontology in clinical practice and electronic system of documentation.
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University of Minnesota Ph.D. dissertation. April 2018. Major: Nursing. Advisors: Karen Monsen, Ruth Lindquist. 1 computer file (PDF); vii, 114 pages.
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Gao, Grace. (2018). Leveraging Electronic Health Record Data For Whole-Person Knowledge Discovery. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215164.
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