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.

Unfortunately, the co-author name was incorrectly published as ‘ ‘Lamona’’ instead of ‘ ‘Lamorna’’ in the original article

Background: Lung cancer is one of the most common and aggressive forms of cancer, resulting in a low survival and high mortality rate. To improve cancer related outcomes, high-risk subpopulations should be identified to reduce overdiagnosis of lung cancer and aid in the implementation of interventions. Electronic Health Records (EHRs) have been effective in identifying cohorts and phenotyping diseases. To identify whether EHR data can be used in risk modelling for lung cancer, this review will seek to identify data features that are contained in EHRs and related to lung cancer. Methods: A search strategy was developed and then applied to MEDLINE via Ovid, Web of Science, Scopus and the Cochrane library. The titles and abstracts of studies will be identified and screened independently by reviewers. Reviewers will read the full texts of studies that appear to meet the eligibility criteria after initial screening. Articles that meet the criteria at this stage, will have their bibliographies examined for relevant studies. Data extraction will then be performed independently by reviewers and a narrative synthesis will be carried out.Discussion: While risk factors for lung cancer have been extensively researched, there has to date been no effort to identify whether information that relates to these factors are available in EHRs and can be modelled with. As such, the results of the review will seek to broaden knowledge around the use of EHRs in lung cancer risk modelling and inform researchers of the variables that are available in EHRs.Registration: PROSPERO CRD42021246781, Registered on 26/04/21.