ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Investigating Longitudinal Tobacco Use Information from Social History and Clinical Notes in the Electronic Health Record.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records.JCO clinical cancer informatics · 2025Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Leveraging natural language processing to augment structured social determinants of health data in the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Automated Detection of Substance-Use Status and Related Information from Clinical Text.Sensors (Basel, Switzerland) · 2022Article
- Improving smoking history documentation in the electronic health record for lung cancer risk assessment and screening in primary care: A case study.Healthcare (Amsterdam, Netherlands) · 2021Article
- Tobacco-Related Counseling and Documentation in Adolescent Primary Care Practice: Challenges and Opportunities.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2020Article
- Article
- Building a tobacco user registry by extracting multiple smoking behaviors from clinical notes.BMC medical informatics and decision making · 2019Article
- Assessing data availability and quality within an electronic health record system through external validation against an external clinical data source.BMC medical informatics and decision making · 2019Article
- Using the emergency department to investigate smoking in young adults.Annals of epidemiology · 2019Article
- Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Identifying Supplement Use Within Clinical Notes: An Applicationof Natural Language Processing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Representation of Social History Factors Across Age Groups: A Topic Analysis of Free-Text Social Documentation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Challenges with Collecting Smoking Status in Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Natural Language Processing for EHR-Based Computational Phenotyping.IEEE/ACM transactions on computational biology and bioinformaticsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
Indexed as
Identifiers
28269918PMC5333299What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.