A30 ADVANCES IN LUNG CANCER SCREENING AND PULMONARY NODULE ASSESSMENT: Utilization Of The Electronic Medical Record To Determine Smoking History: A Challenge For Population-Based Lung Cancer Screening

Publication Title

American Journal of Respiratory and Critical Care Medicine

Document Type

Conference Proceeding

Publication Date

2016

Keywords

washington; swedish; swedish thoracic surgery

Abstract

Rationale: Meaningful use of the electronic medical record (EMR) can potentially aid in population health management, including identifying patients who qualify for lung cancer screening. The core eligibility criteria for lung cancer screening [age 55-77 years, smoking history of 30 ‘pack-years’ (PY), current smoker or quit within last 15 years] is housed within the EMR. Data is entered into the EMR by multiple individuals from differing departments, often without a standardized method. Inaccuracies in this data may have significant implications for both general risk stratification and identification of patients eligible for lung cancer screening. Our hypothesis is that PY documented in the EMR is often discordant with PY obtained during the lung cancer screening shared decision making (SDM) conversation, potentially excluding eligible populations from lung cancer screening. Methods: A retrospective analysis of patients referred, or self-referred, to our institution’s Lung Cancer Screening Program from February-October 2015 was performed. PY calculated during the SDM conversation was compared to the documented PY in the EMR extracted at the time of SDM. All SDM conversations were conducted via telephone, by a licensed independent practitioner. Patients’ PY was calculated at SDM utilizing the following formula: (Number of packs of cigarettes smoked per day X (Number of years smoked – Number of quit years)). Results: A total of 186 patients referred for lung cancer screening were included in the analysis. Discordance between SDM and EMR was present in 96% (179/186) of the referred population. In patients with discordant PY documentation, (N=179) the EMR under-reported PY in 85% (152/179) with a median difference of 30 PYs [IQR=14 – 42] and over-reported PY in 15% (27/179) with a median difference of 9 PYs [IQR=3-17]. The PY calculated at SDM identified 95% (177/186) of the patients eligible for screening. Of these, 54% (95/177) would have failed to meet the 30 PY threshold for lung cancer screening based on their EMR data (Figure 1). Conclusions: Inaccuracies in the EMR can potentially exclude patients from lung cancer screening when utilized as the primary filter for identification of eligible populations. Incorrect reporting of PY can potentially impact the appropriate risk stratification of patients.

Clinical Institute

Cancer

Specialty

Health Information Technology

Specialty

Oncology

Specialty

Population Health

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