Development of an interactive, Web-delivered system to increase provider-patient engagement in smoking cessation.
ABSTRACT Patient self-management interventions for smoking cessation are effective but underused. Health care providers do not routinely refer smokers to these interventions.
The objective of our study was to uncover barriers and facilitators to the use of an e-referral system that will be evaluated in a community-based randomized trial. The e-referral system will allow providers to refer smokers to an online smoking intervention during routine clinical care.
We devised a four-step development and pilot testing process: (1) system conceptualization using Delphi to identify key functionalities that would overcome barriers in provider referrals for smoking cessation, (2) Web system programming using agile software development and best programming practices with usability refinement using think-aloud testing, (3) implementation planning using the nominal group technique for the effective integration of the system into the workflow of practices, and (4) pilot testing to identify practice recruitment and system-use barriers in real-world settings.
Our Delphi process (step 1) conceptualized three key e-referral functions: (1) Refer Your Smokers, allowing providers to e-refer patients at the point of care by entering their emails directly into the system, (2) practice reports, providing feedback regarding referrals and impact of smoking-cessation counseling, and (3) secure messaging, facilitating provider-patient communication. Usability testing (step 2) suggested the system was easy to use, but implementation planning (step 3) suggested several important approaches to encourage use (eg, proactive email cues to encourage practices to participate). Pilot testing (step 4) in 5 practices had limited success, with only 2 patients referred; we uncovered important recruitment and system-use barriers (eg, lack of study champion, training, and motivation, registration difficulties, and forgetting to refer).
Implementing a system to be used in a clinical setting is complex, as several issues can affect system use. In our ongoing large randomized trial, preliminary analysis with the first 50 practices using the system for 3 months demonstrated that our rigorous preimplementation evaluation helped us successfully identify and overcome these barriers before the main trial. TRIAL: Clinicaltrials.gov NCT00797628; http://clinicaltrials.gov/ct2/show/NCT00797628 (Archived by WebCite at http://www.webcitation.org/61feCfjCy).
- SourceAvailable from: Wendy Hopmans[Show abstract] [Hide abstract]
ABSTRACT: Background Online cancer information can support patients in making treatment decisions. However, such information may not be adequately tailored to the patient¿s perspective, particularly if healthcare professionals do not sufficiently engage patient groups when developing online information. We applied qualitative user testing during the development of a patient information website on stereotactic ablative radiotherapy (SABR), a new guideline-recommended curative treatment for early-stage lung cancer.Methods We recruited 27 participants who included patients referred for SABR and their relatives. A qualitative user test of the website was performed in 18 subjects, followed by an additional evaluation by users after website redesign (N¿=¿9). We primarily used the `thinking aloud¿ approach and semi-structured interviewing. Qualitative data analysis was performed to assess the main findings reported by the participants.ResultsStudy participants preferred receiving different information that had been provided initially. Problems identified with the online information related to comprehending medical terminology, understanding the scientific evidence regarding SABR, and appreciating the side-effects associated with SABR. Following redesign of the website, participants reported fewer problems with understanding content, and some additional recommendations for better online information were identified.Conclusions Our findings indicate that input from patients and their relatives allows for a more comprehensive and usable website for providing treatment information. Such a website can facilitate improved patient participation in treatment decision-making for cancer.BMC Medical Informatics and Decision Making 12/2014; 14(1):116. · 1.50 Impact Factor
- [Show abstract] [Hide abstract]
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.Translational behavioral medicine. 12/2013; 3(4):370-378.
- [Show abstract] [Hide abstract]
ABSTRACT: 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. Clinicaltrials.gov NCT00797628; http://clinicaltrials.gov/ct2/show/NCT00797628 (Archived by WebCite at http://www.webcitation.org/6F3tqz0b3).Journal of Medical Internet Research 01/2013; 15(5):e77. · 4.67 Impact Factor