Name
Title | ||
|---|---|---|
Investigating Longitudinal Tobacco Use Information from Social History and Clinical Notes in the Electronic Health Record. |
Abstract | ||
|---|---|---|
The electronic health record (EHR) provides an opportunity for improved use of clinical documentation including leveraging tobacco use information by clinicians and researchers. In this study, we investigated the content, consistency, and completeness of tobacco use data from structured and unstructured sources in the EHR. A natural language process (NLP) pipeline was utilized to extract details about tobacco use from clinical notes and free-text tobacco use comments within the social history module of an EHR system. We analyzed the consistency of tobacco use information within clinical notes, comments, and available structured fields for tobacco use. Our results indicate that structured fields for tobacco use alone may not be able to provide complete tobacco use information. While there was better consistency for some elements (e.g., status and type), inconsistencies were found particularly for temporal information. Further work is needed to improve tobacco use information integration from different parts of the EHR. |
Year | Venue | Field |
|---|---|---|
2016 | AMIA | Medical education,Social history,Genealogy,Medical record,Medicine |
DocType | Volume | Citations |
Conference | 2016 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Wang | 1 | 25 | 10.08 |
| Elizabeth S. Chen | 2 | 8 | 6.63 |
| Serguei V S Pakhomov | 3 | 471 | 40.62 |
| Elizabeth Lindemann | 4 | 0 | 0.34 |
| G B Melton | 5 | 264 | 45.72 |