Elizabeth S. Chen
Brown University
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Publication
Featured researches published by Elizabeth S. Chen.
Archives of Suicide Research | 2018
Sarah A. Arias; Edwin D. Boudreaux; Elizabeth S. Chen; Ivan W. Miller; Carlos A. Camargo; Richard N. Jones; Lisa A. Uebelacker
In an emergency department (ED) sample, we investigated the concordance between identification of suicide-related visits through standardized comprehensive chart review versus a subset of 3 specific chart elements: ICD-9-CM codes, free-text presenting complaints, and free-text physician discharge diagnoses. The method for this study was review of medical records for adults (≥18u2009years) at 8 EDs across the United States. A total of 3,776 charts were reviewed. A combination of the 3 chart elements (ICD-9-CM, presenting complaints, and discharge diagnoses) provided the most robust data with 85% sensitivity, 96% specificity, 92% PPV, and 92% NPV. These findings highlight the use of key discrete fields in the medical record that can be extracted to facilitate identification of whether an ED visit was suicide-related.
Journal of the American Medical Informatics Association | 2018
Sripriya Rajamani; Elizabeth S. Chen; Elizabeth Lindemann; Ranyah Aldekhyyel; Yan Wang; Genevieve B. Melton
Reports by the National Academy of Medicine and leading public health organizations advocate including occupational information as part of an individuals social context. Given recent National Academy of Medicine recommendations on occupation-related data in the electronic health record, there is a critical need for improved representation. The National Institute for Occupational Safety and Health has developed an Occupational Data for Health (ODH) model, currently in draft format. This study aimed to validate the ODH model by mapping occupation-related elements from resources representing recommendations, standards, public health reports and surveys, and research measures, along with preliminary evaluation of associated value sets. All 247 occupation-related items across 20 resources mapped to the ODH model. Recommended value sets had high variability across the evaluated resources. This study demonstrates the ODH models value, the multifaceted nature of occupation information, and the critical need for occupation value sets to support clinical care, population health, and research.
16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 | 2017
Elizabeth Lindemann; Elizabeth S. Chen; Sripriya Rajamani; Nivedha Manohar; Yan Wang; Genevieve B. Melton
There has been increasing recognition of the key role of social determinants like occupation on health. Given the relatively poor understanding of occupation information in electronic health records (EHRs), we sought to characterize occupation information within free-text clinical document sources. From six distinct clinical sources, 868 total occupation-related sentences were identified for the study corpus. Building off approaches from previous studies, refined annotation guidelines were created using the National Institute for Occupational Safety and Health Occupational Data for Health data model with elements added to increase granularity. Our corpus generated 2,005 total annotations representing 39 of 41 entity types from the enhanced data model. Highest frequency entities were: Occupation Description (17.7%); Employment Status - Not Specified (12.5%); Employer Name (11.0%); Subject (9.8%); Industry Description (6.2%). Our findings support the value of standardizing entry of EHR occupation information to improve data quality for improved patient care and secondary uses of this information.
CRI | 2017
Elizabeth Lindemann; Elizabeth S. Chen; Sripriya Rajamani; Nivedha Manohar; Yan Wang; Genevieve B. Melton
american medical informatics association annual symposium | 2016
Tamara J. Winden; Elizabeth S. Chen; Genevieve B. Melton
american medical informatics association annual symposium | 2016
Ranyah Aldekhyyel; Elizabeth S. Chen; Sripriya Rajamani; Yan Wang; Genevieve B. Melton
AMIA | 2017
Ashley S. Lee; Indra Neil Sarkar; Genevieve B. Melton; Yuanqing Liu; Vivekanand Sharma; Elizabeth S. Chen
AMIA | 2017
Margaret M. Thorsen; Indra Neil Sarkar; Elizabeth S. Chen; Elaine Fontaine; Gregory Walker; Megan L. Ranney
american medical informatics association annual symposium | 2016
Yan Wang; Elizabeth S. Chen; Serguei V. S. Pakhomov; Elizabeth Lindemann; Genevieve B. Melton
AMIA | 2016
Sripriya Rajamani; Elizabeth S. Chen; Ranyah Aldekhyyel; Yan Wang; Genevieve B. Melton