An Electronic Health Record−Based Strategy to Address Child Tobacco Smoke Exposure

Mahabee‐Gittens, E. Melinda; Dexheimer, Judith W.; Tabangin, Meredith E.; Merianos, Ashley L.; Stone, Lara; Meyers, Gabe T.; Gordon, Judith S.

doi:10.1016/j.amepre.2017.08.011

Cited by 19 publications

(21 citation statements)

References 42 publications

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“…Other qualitative feedback responses were obtained by focus groups or workshops (n=5) [43,57,61,67,68]. The alert firing rate (n=12) [43,55,62,[69][70][71][72][73][74][75][76][77] and alert acceptance rate (n=15) [27,29,43,58,61,62,69,74,[78][79][80][81][82][83][84] were also common metrics used to understand alarm fatigue and acceptability. Although metrics of relevance such as sensitivity, specificity, and positive predictive value (PPV) were referenced several times [77,[84][85][86][87][88], only one study evaluated these metrics as pre-specified outcomes [49].…”

Section: How and How Much -Methods And Metricsmentioning

confidence: 99%

“…Prior studies have shown that CDS needs to be minimally-interruptive to workflow [100], and literature in this survey emphasizes that this goal is better accomplished by integrating it into the existing EHR. Needing to reference an external database is a deterrent to CDS use [27]. The inability of CDS to understand records from outside the hospital system, such as scanned lab results, can lead to misfiring of CDS alerts [96].…”

Section: Make It Easy -Workflow Efficiency and Integrationmentioning

confidence: 99%

“…Finally, multiple publications discussed the importance of end-user feedback throughout the design, implementation, and modification process. Several publications noted that nurses and nurse practitioners are also stakeholders who should be involved in CDS design [17,18,27]. Feedback before iteration was emphasized in multiple studies [42,47,68,74,93] and "the importance of listening to user experience" was explicitly highlighted [36].…”

Section: Fix It -Improving Cdsmentioning

confidence: 99%

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Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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Objectives: This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout. Methods: We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria. Results: Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS. Conclusions: CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

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Section: How and How Much -Methods And Metricsmentioning

confidence: 99%

Section: Make It Easy -Workflow Efficiency and Integrationmentioning

confidence: 99%

Section: Fix It -Improving Cdsmentioning

confidence: 99%

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Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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“…29 Previous TSE reduction initiatives reveal that health care providers and parents who smoke and bring their children to ED and/or UC settings are highly receptive to these feasible efforts. 30,31 In addition, delivering interventions in these settings may increase caregiver quit attempts and successful quits and ultimately decrease child TSE. [32][33][34] This includes the implementation of electronic health record-embedded clinical decision support systems that can be easily incorporated into busy ED and/or UC providers' workloads.…”

Section: Discussionmentioning

confidence: 99%

Adolescent Tobacco Smoke Exposure, Respiratory Symptoms, and Emergency Department Use

2018

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“…Details on the development, design, and implementation of the CDSS into the EHR are described elsewhere. 6,10,11 . In brief, changes were made to incorporate a modified version of the "5As" of the Clinical Practice Guidelines for Tobacco Use -Ask about tobacco use, Advise to quit, Assess willingness to quit, and Assist/Arrange those who wish to quit with follow-up and resources (e.g.…”

Section: Introductionmentioning

confidence: 99%

Utilization of a Clinical Decision Support Tool to Reduce Child Tobacco Smoke Exposure in the Urgent Care Setting

Mahabee‐Gittens¹,

Merianos²,

Dexheimer³

et al. 2018

Pediatr Emer Care

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Background Clinical decision support systems (CDSS) may facilitate caregiver tobacco screening and counseling by pediatric urgent care (UC) nurses. Objective This study aimed to assess the feasibility of a CDSS to address caregivers' tobacco use and child tobacco smoke exposure (TSE). Methods We conducted a 3-month prospective study on caregivers screened using a CDSS. Nurses used the CDSS to advise, assess, and assist caregivers to quit. We assessed caregiver sociodemographics, smoking habits, and child TSE. Results We screened 185 caregivers whose children were exposed to TSE for study inclusion; 155 (84%) met the eligibility criteria, and 149 (80.5%) were included in the study. Study nurses advised 35.2% of the caregivers to quit, assessed 35.9% for readiness to quit, and assisted 32.4%. Of the 149 participants, 83.1% were female; 47.0% were white and 45.6% African American; 84.6% had public insurance or were self-pay; 71.1% were highly nicotine dependent; 50.0% and 50.7% allowed smoking in the home and car, respectively; and 81.3% of children were biochemically confirmed to be exposed to tobacco smoke. At follow-up (86.6% retention), 58.9% reported quit attempts at 3 months. There was a significant decrease in nicotine dependence and a significant increase in motivation to quit. Self-reported quit rate was 7.8% at 3 months. Conclusions An electronic health record–embedded CDSS was feasible to incorporate into busy UC nurses' workloads and was associated with encouraging changes in the smoking behavior of caregivers. More research on the use of CDSS to screen and counsel caregivers who smoke in the UC and other acute care settings is warranted.

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An Electronic Health Record−Based Strategy to Address Child Tobacco Smoke Exposure

Abstract: This study is registered at www.clinicaltrials.gov NCT02489708.

Cited by 19 publications

References 42 publications

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Adolescent Tobacco Smoke Exposure, Respiratory Symptoms, and Emergency Department Use

Utilization of a Clinical Decision Support Tool to Reduce Child Tobacco Smoke Exposure in the Urgent Care Setting

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