Dataset Information

Implementing point of care "e-referrals" in 137 clinics to increase access to a quit smoking internet system: the Quit-Primo and National Dental PBRN HI-QUIT Studies.

ABSTRACT: Integrating electronic referral systems into clinical practices may increase use of web-accessible tobacco interventions. We report on our feasibility evaluation of using theory-driven implementation science techniques to translate an e-referral system (ReferASmoker.org) into the workflow of 137 community-based medical and dental practices, including system use, patient registration, implementation costs, and lessons learned. After 6 months, 2,376 smokers were e-referred (medical, 1,625; dental, 751). Eighty-six percent of the medical practices [75/87, mean referral = 18.7 (SD?=?17.9), range 0-105] and dental practices [43/50, mean referral = 15.0 (SD?=?10.5), range 0-38] had e-referred. Of those smokers e-referred, 25.3 registered [mean smoker registration rate-medical 4.9 (SD?=?7.6, range 0-59), dental 3.6 (SD?=?3.0, range 0-10)]. Estimated mean implementation costs are medical practices, US$429.00 (SD?=?85.3); and dental practices, US$238.75 (SD?=?13.6). High performing practices reported specific strategies to integrate ReferASmoker.org; low performers reported lack of smokers and patient disinterest in the study. Thus, a majority of practices e-referred and 25.3 % of referred smokers registered demonstrating e-referral feasibility. However, further examination of the identified implementation barriers is important as of the estimated 90,000 to 140,000 smokers seen in the 87 medical practices in 6 months, only 1,625 were e-referred.

SUBMITTER: Sadasivam RS

PROVIDER: S-EPMC3830021 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Implementing point of care "e-referrals" in 137 clinics to increase access to a quit smoking internet system: the Quit-Primo and National Dental PBRN HI-QUIT Studies.

Sadasivam Rajani S RS Hogan Timothy P TP Volkman Julie E JE Smith Bridget M BM Coley Heather L HL Williams Jessica H JH Delaughter Kathryn K Ray Midge N MN Gilbert Gregg H GH Ford Daniel E DE Allison Jeroan J JJ Houston Thomas K TK

Translational behavioral medicine 20131201 4

Integrating electronic referral systems into clinical practices may increase use of web-accessible tobacco interventions. We report on our feasibility evaluation of using theory-driven implementation science techniques to translate an e-referral system (ReferASmoker.org) into the workflow of 137 community-based medical and dental practices, including system use, patient registration, implementation costs, and lessons learned. After 6 months, 2,376 smokers were e-referred (medical, 1,625; dental, ...[more]

PMID: 24294325

Similar Datasets

Project description:Smoking is the most preventable cause of death. Although effective, Web-assisted tobacco interventions are underutilized and recruitment is challenging. Understanding who participates in Web-assisted tobacco interventions may help in improving recruitment.To understand characteristics of smokers participating in a Web-assisted tobacco intervention (Decide2Quit.org).In addition to the typical Google advertisements, we expanded Decide2Quit.org recruitment to include referrals from medical and dental providers. We assessed how the expanded recruitment of smokers changed the users' characteristics, including comparison with a population-based sample of smokers from the national Behavioral Risk Factors Surveillance Survey (BRFSS). Using a negative binomial regression, we compared demographic and smoking characteristics by recruitment source, in particular readiness to quit and association with subsequent Decide2Quit.org use.The Decide2Quit.org cohort included 605 smokers; the 2010 BRFSS dataset included 69,992. Compared to BRFSS smokers, a higher proportion of Decide2Quit.org smokers were female (65.2% vs 45.7%, P=.001), over age 35 (80.8% vs 67.0%, P=.001), and had some college or were college graduates (65.7% vs 45.9%, P=.001). Demographic and smoking characteristics varied by recruitment; for example, a lower proportion of medical- (22.1%) and dental-referred (18.9%) smokers had set a quit date or had already quit than Google smokers (40.1%, P<.001). Medical- and dental-referred smokers were less likely to use Decide2Quit.org functions; in adjusted analysis, Google smokers (predicted count 17.04, 95% CI 14.97-19.11) had higher predicted counts of Web page visits than medical-referred (predicted count 12.73, 95% CI 11.42-14.04) and dental-referred (predicted count 11.97, 95% CI 10.13-13.82) smokers, and were more likely to contact tobacco treatment specialists.Recruitment from clinical practices complimented Google recruitment attracting smokers less motivated to quit and less experienced with Web-assisted tobacco interventions.

Project description:BACKGROUND:Within a web-assisted tobacco intervention, we provided a function for smokers to asynchronously communicate with a trained tobacco treatment specialist (TTS). Previous studies have not attempted to isolate the effect of asynchronous counseling on smoking cessation. OBJECTIVE:This study aimed to conduct a semiquantitative analysis of TTS-smoker communication and evaluate its association with smoking cessation. METHODS:We conducted a secondary analysis of data on secure asynchronous communication between trained TTSs and a cohort of smokers during a 6-month period. Smokers were able to select their preferred TTS and message them using a secure web-based form. To evaluate whether the TTS used evidence-based practices, we coded messages using the Motivational Interviewing Self-Evaluation Checklist and Smoking Cessation Counseling (SCC) Scale. We assessed the content of messages initiated by the smokers by creating topical content codes. At 6 months, we assessed the association between smoking cessation and the amount of TTS use and created a multivariable model adjusting for demographic characteristics and smoking characteristics at baseline. RESULTS:Of the 725 smokers offered asynchronous counseling support, 33.8% (245/725) messaged the TTS at least once. A total of 1082 messages (TTSs: 565; smokers 517) were exchanged between the smokers and TTSs. The majority of motivational interviewing codes were those that supported client strengths (280/517, 54.1%) and promoted engagement (280/517, 54.1%). SCC code analysis showed that the TTS provided assistance to smokers if they were willing to quit (247/517, 47.8%) and helped smokers prepare to quit (206/517, 39.8%) and anticipate barriers (197/517, 38.1%). The majority of smokers' messages discussed motivations to quit (234/565, 41.4%) and current and past treatments (talking about their previous use of nicotine replacement therapy and medications; 201/565, 35.6%). The majority of TTS messages used behavioral strategies (233/517, 45.1%), offered advice on treatments (189/517, 36.5%), and highlighted motivations to quit (171/517, 33.1%). There was no association between the amount of TTS use and cessation. In the multivariable model, after adjusting for gender, age, race, education, readiness at baseline, number of cigarettes smoked per day at baseline, and the selected TTS, smokers messaging the TTS one or two times had a smoking cessation odds ratio (OR) of 0.8 (95% CI 0.4-1.4), and those that messaged the TTS more than two times had a smoking cessation OR of 1.0 (95% CI 0.4-2.3). CONCLUSIONS:Our study demonstrated the feasibility of using asynchronous counseling to deliver evidence-based counseling. Low participant engagement or a lack of power could be potential explanations for the nonassociation with smoking cessation. Future trials should explore approaches to increase participant engagement and test asynchronous counseling in combination with other approaches for improving the rates of smoking cessation.

Project description:Although screening for tobacco use is increasing with electronic health records and standard protocols, other tobacco-control activities, such as referral of patients to cessation resources, is quite low. In the QUIT-PRIMO study, an online referral portal will allow providers to enter smokers' email addresses into the system. Upon returning home, the smokers will receive automated emails providing education about tobacco cessation and encouragement to use the patient smoking cessation website (with interactive tools, educational resources, motivational email messages, secure messaging with a tobacco treatment specialist, and online support group).The informatics system will be evaluated in a comparative effectiveness trial of 160 community-based primary care practices, cluster-randomized at the practice level. In the QUIT-PRIMO intervention, patients will be provided a paper information-prescription referral and then "e-referred" to the system. In the comparison group, patients will receive only the paper-based information-prescription referral with the website address. Once patients go to the website, they are subsequently randomized within practices to either a standard patient smoking cessation website or an augmented version with access to a tobacco treatment specialist online, motivational emails, and an online support group. We will compare intervention and control practice participation (referral rates) and patient participation (proportion referred who go to the website). We will then compare the effectiveness of the standard and augmented patient websites.Our goal is to evaluate an integrated informatics solution to increase access to web-delivered smoking cessation support. We will analyze the impact of this integrated system in terms of process (provider e-referral and patient login) and patient outcomes (six-month smoking cessation).Web-delivered Provider Intervention for Tobacco Control (QUIT-PRIMO) - a randomized controlled trial: NCT00797628.

Dataset Information

Implementing point of care "e-referrals" in 137 clinics to increase access to a quit smoking internet system: the Quit-Primo and National Dental PBRN HI-QUIT Studies.

Publications

Implementing point of care "e-referrals" in 137 clinics to increase access to a quit smoking internet system: the Quit-Primo and National Dental PBRN HI-QUIT Studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets