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Social and Behavioral Domains in Acute Care Electronic Health Records: Barriers, Facilitators, Relevance, and Value.
|Title:||Social and Behavioral Domains in Acute Care Electronic Health Records: Barriers, Facilitators, Relevance, and Value.|
|Authors:||LaWall, Emiline G.|
|Contributors:||Public Health (department)|
|Keywords:||Social and Behavioral Domains|
Acute Care Electronic Health Records
Length of Stay
Potentially Preventable Readmission
|Date Issued:||Aug 2018|
|Publisher:||University of Hawaiʻi at Mānoa|
|Abstract:||Social and Behavioral Determinants (SBD) are defined as environmental, financial, or psychosocial factors that impact the health of individuals and communities. There is increasing awareness that SBD affects health in a more profound way than access to (and quality of) medical services (Gold et al, 2017; Nguyen et al, 2015). Understanding and applying SBD data can mitigate health iniquities and reduce the burden of chronic disease (Roux et al, 2015). Despite this, SBD measures are infrequently and inconsistently documented by healthcare providers (Hripcsak, Forrest, Brennan and Stead, 2015). To address this concern, the Institutes of Medicine (IOM) established a set of 12 SBD data for standardization within Electronic Health Records (EHR; IOM, 2014). |
The objective of this dissertation was to provide new evidence about the barriers, facilitators, relevance and value of adding SBD to the EHR in acute care settings. The conceptual model of this dissertation integrates the IOM’s 12 recommended SBD in to the commonly-recognized Anderson Model of Healthcare Utilization, which relates the use of health care services to several population characteristics and environmental factors.
In the first study, focus groups were conducted to ascertain hospital employees’ perceptions of current and future use of SBD in the EHR. In the second and third studies, two years of de-identified EHR data was collected from two hospital sites on Oahu, Hawai‘i to assess whether the previously-deployed SBD measures of “Physical Activity” and “Social Connection and Social Isolation” could inform the common acute care process measures (length-of-stay and potentially preventable readmissions, respectively). For the study of “Physical Activity” in predicting length-of-stay, a piecewise regression with a breakpoint for time was used. Multiple-logistic regression was used to examine “Social Connection and Isolation” in predicting potentially preventable readmissions.
The results from this dissertation demonstrate that SBD can be incorporated in to acute care quality efforts and social service referrals, subsequently providing improvement to patients’ immediate course of care. However, we must continue to conduct feasibility of implementing these measures within the nuances of various acute care workflows. Identifying creative ways to capture SBD, such as patient self-report or natural language processing, may be more optimal mechanisms to obtain trended SBD data, with minimal disruptions to clinical workflows.
|Description:||Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018.|
|Rights:||All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.|
|Appears in Collections:||
Ph.D - Public Health|
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