scholarly journals P2.11-22 Use of Electronic Medical Records for Tobacco Use and Lung Cancer Screening Documentation in a Large Urban Academic Medicine Practice

2018 ◽  
Vol 13 (10) ◽  
pp. S787
Author(s):  
G. Suero-Abreu ◽  
K. Sampson ◽  
J. Wang ◽  
A. Douglas ◽  
A. Karatasakis ◽  
...  
Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5449
Author(s):  
Lamorna Brown ◽  
Utkarsh Agrawal ◽  
Frank Sullivan

Lung cancer screening trials using low-dose computed tomography (LDCT) show reduced late-stage diagnosis and mortality rates. These trials have identified high-risk groups that would benefit from screening. However, these sub-populations can be difficult to access and retain in trials. Implementation of national screening programmes further suggests that there is poor uptake in eligible populations. A new approach to participant selection may be more effective. Electronic medical records (EMRs) are a viable alternative to population-based or health registries, as they contain detailed clinical and demographic information. Trials have identified that e-screening using EMRs has improved trial retention and eligible subject identification. As such, this paper argues for greater use of EMRs in trial recruitment and screening programmes. Moreover, this opinion paper explores the current issues in and approaches to lung cancer screening, whether records can be used to identify eligible subjects for screening and the challenges that researchers face when using EMR data.


Author(s):  
Graham W. Warren ◽  
Jamie S. Ostroff ◽  
John R. Goffin

Tobacco use is the largest preventable risk factor for the development of several cancers, and continued tobacco use by patients with cancer and survivors of cancer causes adverse outcomes. Worldwide tobacco control efforts have reduced tobacco use and improved health outcomes in many countries, but several countries continue to suffer from increased tobacco use and associated adverse health effects. Continued tobacco use by patients undergoing cancer screening or treatment results in continued risk for cancer-related and noncancer-related health conditions. Although integrating tobacco assessment and cessation support into lung cancer screening and cancer care is well justified and feasible, most patients with cancer unfortunately do not receive evidence-based tobacco cessation support. Combining evidence-based methods of treating tobacco addiction, such as behavioral counseling and pharmacotherapy, with practical clinical considerations in the setting of lung cancer screening and cancer treatment should result in substantial improvements in access to evidence-based care and resultant improvements in health risks and cancer treatment outcomes.


CHEST Journal ◽  
2016 ◽  
Vol 150 (4) ◽  
pp. 1301A
Author(s):  
Niloofar Taghizadeh ◽  
Kathryn Taylor ◽  
Remon Tadros ◽  
Paul MacEachern ◽  
Rommy Koetzler ◽  
...  

2019 ◽  
Vol 16 ◽  
pp. 101023 ◽  
Author(s):  
Steven S. Fu ◽  
Anne C. Melzer ◽  
Angela E. Fabbrini ◽  
Kathryn L. Rice ◽  
Barbara Clothier ◽  
...  

Author(s):  
Jamie S. Ostroff ◽  
Donna Shelley

Lung cancer screening using low-dose helical computed tomography is now recommended for early detection of lung cancer. This case study provides an overview of a study that is testing the effectiveness of tobacco treatment interventions for high-risk smokers seeking lung cancer screening and examining factors that may influence implementation process and sustainability for delivering effective models of smoking cessation treatment in lung cancer screening settings. The focus of the case study is a description of how and why two implementation frameworks were applied to evaluate the implementation outcomes and additional multilevel factors (i.e., organization and intervention characteristics) that may influence effective implementation of evidence-based tobacco use treatment interventions in the context of lung cancer screening. Ultimately, implementation of high-quality tobacco treatment in lung cancer screening settings is likely to further reduce tobacco-related cancer morbidity and mortality.


Lung Cancer ◽  
2017 ◽  
Vol 111 ◽  
pp. 101-107 ◽  
Author(s):  
Niloofar Taghizadeh ◽  
Kathryn L. Taylor ◽  
Paul MacEachern ◽  
Rommy Koetzler ◽  
James A. Dickinson ◽  
...  

2020 ◽  
Author(s):  
Marvin Chia-Han Yeh ◽  
Yu-Chuan(Jack) Li ◽  
Yu-Hsiang Wang ◽  
Hsuan-Chia Yang ◽  
Kuan-Jen Bai ◽  
...  

BACKGROUND Artificial intelligence can integrate complex features and may be used to predict the risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. OBJECTIVE Using electronic medical records to pre-screening patient’s risk for developing lung cancer. METHODS Two million participants were randomly selected from the Taiwan National Health Insurance Research Database from 1999 to 2013; We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data and tested prospectively on post-2012 data. An age- and gender-matched subgroup that is 10 times larger than the original lung cancer group was used to assess the predictive power of EMR. Discrimination (area under the curve [AUC]) and calibration analyses were performed. RESULTS The analysis included 11,617 cases of lung cancer and 1,423,154 controls. The model achieved an AUC of 0.90 for the overall population and 0.87 in patients >55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among those >55-years-old with a preexisting history of lung disease. CONCLUSIONS Our model achieved excellent performance at predicting lung cancer within one year and may be deployed for digital patient screening. Deep learning facilitates the effective use of EMRs to identify individuals at high risk for developing lung cancer.


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