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A Comparison of Smoking History in the Electronic Health Record With Self-Report.

ABSTRACT: INTRODUCTION:Knowing patients' smoking history helps guide who may benefit from preventive services such as lung cancer screening. The accuracy of smoking history electronic health records remains unclear. METHODS:This was a secondary analysis of data collected from a portal-based lung cancer screening decision aid. Participants of an academically affiliated health system, aged 55-76 years, completed an online survey that collected a detailed smoking history including years of smoking, years since quitting, and smoking intensity. Eligibility for lung cancer screening was defined using the Centers for Medicare and Medicaid Services criteria. Data analysis was performed May-December 2018, and data collection occurred between November 2016 and February 2017. RESULTS:A total of 336 participants completed the survey and were included in the analysis. Of 175 participants with self-reported smoking intensity, 72% had packs per day and 62% had pack-years recorded in the electronic health record. When present, smoking history in the electronic health records correlated well with self-reported years of smoking (r =0.78, p≤0.0001) and years since quitting (r =0.94, p≤0.0001). Self-reported smoking intensity, including pack-years (r =0.62, p<0.0001) and packs per day (r =0.65, p≤0.0001), was less correlated. Of those participants eligible for lung cancer screening by self-report, only 35% met criteria for screening by electronic health records data alone. Others were either incorrectly classified as ineligible (23%) or had incomplete data (41%). CONCLUSIONS:The electronic health records frequently misses critical elements of a smoking history, and when present, it often underestimates smoking intensity, which may impact who receives lung cancer screening.

SUBMITTER: Patel N

PROVIDER: S-EPMC7533103 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionSelf-reported measures of healthcare utilisation are often used in longitudinal cohort studies involving older community-dwelling people. The aim of this study is to compare healthcare utilisation rates using patient self-report and manual extraction from the general practice (GP) electronic medical record (EMR).MethodsStudy population: Two prospective cohort studies (n = 806 and n = 1,377, aged ≥70 years) conducted in the Republic of Ireland were compared. Study outcomes: GP, outpatient department (OPD) and emergency department (ED) visits over a one-year period. Statistical analysis: Descriptive statistics of the two cohorts are presented. A negative binomial regression was performed and results are presented as incidence rate ratios (IRR) with 95% confidence intervals (CI). For the outcome of any ED visit, linear regression was performed, yielding risk ratios (RR) with 95% CI.ResultsThe annual rates of GP, OPD and ED visits were 6.30 (SD 4.63), 2.11 (SD 2.46) and 0.26 (SD 0.62) respectively in GP EMR cohort, compared to 5.65 (SD 8.06), 2.09 (SD 5.83) and 0.32 (SD 0.84) in the self-report cohort. In univariate regression analysis comparing healthcare utilisation, the self-report cohort reported a lower frequency of GP visits (unadjusted IRR 0.90 (95% CI 0.84, 0.96), p = 0.02)), a greater frequency of ED visits (1.20 (0.98, 1.49), p = 0.083)), and no difference in OPD visits (unadjusted IRR 0.99 (95% CI 0.86, 1.13), p = 0.845)). In multivariate analysis, adjusted for relevant confounders, there was no difference in GP visits (adjusted IRR 0.99 (95% CI 0.92, 1.06), p = 0.684)) or OPD visits (adjusted IRR 1.09 (0.95, 1.25), p = 0.23)) between the two cohorts. However, the self-report cohort reported 37% more ED visits (adjusted IRR 1.37 (1.10, 1.71), p = 0.005)) and were more likely to report any ED visit (adjusted RR 1.23 (95% CI 1.02, 1.48), p = 0.028)).ConclusionsThis study demonstrates that reported rates of GP and OPD visits were similar but there were differences in reported ED visits, with significantly higher self-reported visits. This may be due to ED visits not being notified to the GP and contextual issues such as transfer of healthcare utilisation data between sectors may vary in different healthcare systems.

Project description:ObjectiveTo assess the effects of an electronic health record (EHR) intervention that prompts the clinician to prescribe nicotine replacement therapy (NRT) at hospital admission and discharge in a large integrated health system.DesignRetrospective cohort study using interrupted time series (ITS) analysis leveraging EHR data generated before and after implementation of the 2015 EHR-based intervention.SettingKaiser Permanente Northern California, a large integrated health system with 4.2 million members.ParticipantsCurrent smokers aged ≥18 hospitalised for any reason.ExposureEHR-based clinical decision supports that prompted the clinician to order NRT on hospital admission (implemented February 2015) and discharge (implemented September 2015).Main outcomes and measuresPrimary outcomes included the monthly percentage of admitted smokers with NRT orders during admission and at discharge. A secondary outcome assessed patient quit rates within 30 days of hospital discharge as reported during discharge follow-up outpatient visits.ResultsThe percentage of admissions with NRT orders increased from 29.9% in the year preceding the intervention to 78.1% in the year following (41.8% change, 95% CI 38.6% to 44.9%) after implementation of the admission hard-stop intervention compared with the baseline trend (ITS estimate). The percentage of discharges with NRT orders increased acutely at the time of both interventions (admission intervention ITS estimate 15.5%, 95% CI 11% to 20%; discharge intervention ITS estimate 13.4%, 95% CI 9.1% to 17.7%). Following the implementation of the discharge intervention, there was a small increase in patient-reported quit rates (ITS estimate 5.0%, 95% CI 2.2% to 7.8%).ConclusionsAn EHR-based clinical decision-making support embedded into admission and discharge documentation was associated with an increase in NRT prescriptions and improvement in quit rates. Similar systemic EHR interventions can help improve smoking cessation efforts after hospitalisation.

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A Comparison of Smoking History in the Electronic Health Record With Self-Report.

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