scholarly journals Using Geospatial Analyses of Linked Electronic Health Records and Tobacco Outlet Data to Address the Social Determinants of Smoking

2019 ◽  
Vol 16 ◽  
Author(s):  
Scott D. Siegel ◽  
Madeline M. Brooks ◽  
Bayo M. Gbadebo ◽  
James T. Laughery
2016 ◽  
Vol 38 (10) ◽  
pp. 1399-1400 ◽  
Author(s):  
Karen A. Monsen ◽  
Nicole Kapinos ◽  
Joyce M. Rudenick ◽  
Kathryn Warmbold ◽  
Siobhan K. McMahon ◽  
...  

2017 ◽  
Vol 25 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Cosmin A Bejan ◽  
John Angiolillo ◽  
Douglas Conway ◽  
Robertson Nash ◽  
Jana K Shirey-Rice ◽  
...  

Abstract Objective Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository. Materials and Methods We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE. Results word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC = 0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC = 0.95) than ACE (AUPRC = 0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being “father” (21.8%) and “mother” (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%–47.6%). Conclusion We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.


2021 ◽  
Vol 26 (12) ◽  
pp. 604-610
Author(s):  
Ruth Lezard ◽  
Toity Deave

Electronic health records (EHRs) are integral to community nursing, and mobile access aids seamless, responsive care, prevents repetition and reduces hospital admissions. This saves time and money, enabling smoother workflows and increased productivity. Common practice among community nurses is to return to workbases to access EHRs. This research was conducted to explore what leads to inconsistency in EHR use. Focus groups were held with community nurses, and reflexive thematic analysis of the data was undertaken. Nurses who used EHRs during consultations described the practice as integrative and informed, promoting collaborative care. Those who did not described EHRs as time-consuming, template-laden and a barrier to nurse-patient communication. One barrier to mobile working is the threat to collegiate teamworking and the social and clinical supports it provides. This study suggests specific strategies could increase mobile EHR engagement: role-specific training for effective EHR use; clear organisational directives; innovative team communication; and peer-to-peer coaching.


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