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An intervention based on the Electronic Medical Record to improve smoking cessation guidance in an urban tertiary care center emergency department.

ABSTRACT: INTRODUCTION:Smoking remains a major public health issue and a leading cause of death and disability in the United States. The objective of this study was to determine the effect of a simple intervention on smoking guidance, based on the electronic medical record (EMR), including providing discharge instructions and/or cessation counseling to emergency department (ED) patients who smoke. METHODS:This was an interventional before-and-after study in an ED with 70000 visits per year. A pre-intervention and post-intervention chart review was performed on a random sample of ED visits occurring in 2014 and 2016, identifying smokers and the frequency with which smokers received discharge instructions and/or cessation counseling. In the fall of 2015, our EMR was programmed to deploy smoking cessation discharge instructions automatically. RESULTS:In all, 28.7% (172/600; 95% CI: 25.2-32.4%) reported current smoking in the pre-intervention ED population and 27.6% (166/600; 95% CI: 24.2-31.4%) reported smoking in the post-intervention population. Smoking cessation guidance was provided to a total of 3.5% of self-reported smokers in the pre-intervention group (6/172; 95% CI: 1.4-7.6%); 1.2% (2/172; 95% CI: 0.3-4.1%) were informed of smoking cessation resources as part of their printed ED discharge instructions and 2.3% (4/172; 95% CI: 0.9-5.8%) received smoking cessation counseling by the ED provider. There was a statistically significant increase in the proportion of patients receiving any smoking cessation guidance after the intervention. All patients (166/166; 95% CI: 97-100% in this period received ED discharge instructions and a list of smoking cessation resources and 3.6% of smokers (6/166; 95% CI: 1.7-7.7%) received smoking cessation counseling by the ED provider. CONCLUSIONS:Automated deployment of smoking cessation discharge instructions in the EMR improves smoking cessation discharge instructions, and also has a positive impact on improving rates of in-person counseling by ED providers.

SUBMITTER: Phelan MP

PROVIDER: S-EPMC7205161 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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An intervention based on the Electronic Medical Record to improve smoking cessation guidance in an urban tertiary care center emergency department.

Phelan Michael P MP Nithianandam Balaji B Eikoff Nathan N Good Daniel D Hustey Fredric M FM Meldon Stephen S

Tobacco prevention & cessation 20190506

<h4>Introduction</h4>Smoking remains a major public health issue and a leading cause of death and disability in the United States. The objective of this study was to determine the effect of a simple intervention on smoking guidance, based on the electronic medical record (EMR), including providing discharge instructions and/or cessation counseling to emergency department (ED) patients who smoke.<h4>Methods</h4>This was an interventional before-and-after study in an ED with 70000 visits per year. ...[more]

PMID: 32411880

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Project description:The focus on acute care, time pressure, and lack of resources hamper the delivery of smoking cessation interventions in the emergency department (ED). The aim of this study was to 1) determine the effect of an emergency nurse-initiated intervention on delivery of smoking cessation counseling based on the 5As framework (ask-advise-assess-assist-arrange) and 2) assess ED nurses' and physicians' perceptions of smoking cessation counseling.The authors conducted a pre-post trial in 789 adult smokers (five or more cigarettes/day) who presented to two EDs. The intervention focused on improving delivery of the 5As by ED nurses and physicians and included face-to-face training and an online tutorial, use of a charting/reminder tool, fax referral of motivated smokers to the state tobacco quitline for proactive telephone counseling, and group feedback to ED staff. To assess ED performance of cessation counseling, a telephone interview of subjects was conducted shortly after the ED visit. Nurses' and physicians' self-efficacy, role satisfaction, and attitudes toward smoking cessation counseling were assessed by survey. Multivariable logistic regression was used to assess the effect of the intervention on performance of the 5As, while adjusting for key covariates.Of 650 smokers who completed the post-ED interview, a greater proportion had been asked about smoking by an ED nurse (68% vs. 53%, adjusted odds ratio [OR] = 2.0, 95% confidence interval [CI] = 1.3 to 2.9), assessed for willingness to quit (31% vs. 9%, adjusted OR= 4.9, 95% CI = 2.9 to 7.9), and assisted in quitting (23% vs. 6%, adjusted OR = 5.1, 95% CI = 2.7 to 9.5) and had arrangements for follow-up cessation counseling (7% vs. 1%, adjusted OR = 7.1, 95% CI = 2.3 to 21) during the intervention compared to the baseline period. A similar increase was observed for emergency physicians (EPs). ED nurses' self-efficacy and role satisfaction in cessation counseling significantly improved following the intervention; however, there was no change in "pros" and "cons" attitudes toward smoking cessation in either ED nurses or physicians.Emergency department nurses and physicians can effectively deliver smoking cessation counseling to smokers in a time-efficient manner. This trial also provides empirical support for expert recommendations that call for nursing staff to play a larger role in delivering public health interventions in the ED.

Project description:Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a "sepsis alert" for those with ?2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ?1 major organ dysfunction (SBP ? 90 mm Hg, lactic acid ?2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2-48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%. Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.

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.

Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Dataset Information

An intervention based on the Electronic Medical Record to improve smoking cessation guidance in an urban tertiary care center emergency department.

Publications

An intervention based on the Electronic Medical Record to improve smoking cessation guidance in an urban tertiary care center emergency department.

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