Gaps in the Electronic Medical Record May Contribute to Low Participation in Lung Cancer Screening.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
3150 patients.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSIONS] Incomplete EMR data entry may contribute to the complexities of identifying LCS-eligible patients. This highlights the value of improving the completeness of EMR smoking history data and conducting targeted BPA audits to understand optimal activation parameters to improve clinician orders for LCS.
[INTRODUCTION] Best Practice Advisories (BPAs) are electronic medical record (EMR) tools that help increase uptake of recommended health care behaviors, such as cancer screenings, by identifying eligi
APA
Fay KA, Schifferdecker KE, et al. (2026). Gaps in the Electronic Medical Record May Contribute to Low Participation in Lung Cancer Screening.. The Journal of surgical research, 319, 40-46. https://doi.org/10.1016/j.jss.2025.11.074
MLA
Fay KA, et al.. "Gaps in the Electronic Medical Record May Contribute to Low Participation in Lung Cancer Screening.." The Journal of surgical research, vol. 319, 2026, pp. 40-46.
PMID
41643255 ↗
Abstract 한글 요약
[INTRODUCTION] Best Practice Advisories (BPAs) are electronic medical record (EMR) tools that help increase uptake of recommended health care behaviors, such as cancer screenings, by identifying eligible patients and alerting providers. However, incomplete/inaccurate documentation within the EMR can be a potential barrier to BPA utility. The purpose of this work was to investigate the effectiveness of a BPA tool to identify eligible patients for lung cancer screening (LCS) using available EMR smoking histories.
[MATERIALS AND METHODS] Retrospective observational review was conducted of a BPA programmed to identify LCS-eligible patients at a single quaternary, LCS-accredited, academic medical center. Programming targeted patients aged 50-77 y classified as "current" or "former smokers," excluding patients with recent lung computed tomography scans and/or lung cancer diagnoses. Data analyzed included frequency of BPA activation and the associated smoking history. Descriptive statistics were used to analyze outcomes.
[RESULTS] Between January 2017 and December 2021, there were 25,172 BPA activations, of which 11,701 were removed because they occurred outside a clinical/telehealth visit. This left 14,101 BPAs linked to 3150 patients. EMR information was not sufficient to calculate pack-year history for 48.9% (1541/3150), and the LCS order rate was 2.5% (78/3150). Although pulmonary disease specialists accounted for 13.7% (236/1721) of total LCS orders, the BPA did not activate for them.
[CONCLUSIONS] Incomplete EMR data entry may contribute to the complexities of identifying LCS-eligible patients. This highlights the value of improving the completeness of EMR smoking history data and conducting targeted BPA audits to understand optimal activation parameters to improve clinician orders for LCS.
[MATERIALS AND METHODS] Retrospective observational review was conducted of a BPA programmed to identify LCS-eligible patients at a single quaternary, LCS-accredited, academic medical center. Programming targeted patients aged 50-77 y classified as "current" or "former smokers," excluding patients with recent lung computed tomography scans and/or lung cancer diagnoses. Data analyzed included frequency of BPA activation and the associated smoking history. Descriptive statistics were used to analyze outcomes.
[RESULTS] Between January 2017 and December 2021, there were 25,172 BPA activations, of which 11,701 were removed because they occurred outside a clinical/telehealth visit. This left 14,101 BPAs linked to 3150 patients. EMR information was not sufficient to calculate pack-year history for 48.9% (1541/3150), and the LCS order rate was 2.5% (78/3150). Although pulmonary disease specialists accounted for 13.7% (236/1721) of total LCS orders, the BPA did not activate for them.
[CONCLUSIONS] Incomplete EMR data entry may contribute to the complexities of identifying LCS-eligible patients. This highlights the value of improving the completeness of EMR smoking history data and conducting targeted BPA audits to understand optimal activation parameters to improve clinician orders for LCS.
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