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).
[Show abstract][Hide abstract] ABSTRACT: Clinical research laboratories, bioinformatics core facilities, and health science organizations often rely on heavy planning based software development models to propose, build, and distribute software as a consumable product. Projects in non-agile software life cycles tend to have rigid “plan-design-build” milestones, increasing the amount of time needed for software development completion. Though the classic software development approach is needed for large-scale and organizational projects, clinical research laboratories can expedite software development while maintaining quality by using lean prototyping as a condition of project advancement to a committed adaptive software development cycle. Software projects benefit from an agile methodology due to the active and changing requirements often guided by experimental data driven models. We describe a lean to adaptive method used in parallel with laboratory bench work to develop quality software quickly that meets the requirements of a fast-paced research environment and reducing time to production, providing immediate value to the end user, and limiting unnecessary development practices in favor of results.
[Show abstract][Hide abstract] ABSTRACT: INTRODUCTION: 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.
OBJECTIVES: To understand characteristics of smokers participating in a Web-assisted tobacco intervention (Decide2Quit.org).
METHODS: 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.
RESULTS: 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
CONCLUSIONS: Recruitment from clinical practices complimented Google recruitment attracting smokers less motivated to quit and less experienced with Web-assisted tobacco interventions.
TRIAL REGISTRATION: Clinicaltrials.gov NCT00797628; http://clinicaltrials.gov/ct2/show/NCT00797628 (Archived by WebCite at http://www.webcitation.org/6F3tqz0b3).
Journal of Medical Internet Research 05/2013; 15(5):e77. DOI:10.2196/jmir.2385 · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Introduction:
Spending on hospital inpatients comprises a major proportion of healthcare costs. This study assessed the impact of systematic feedback to gastroenterologists on the cost of care provided to inpatients on a gastrointestinal/hepatology (GIH) hospital service.
Patients with a GIH diagnosis were randomly assigned to be cared for by one of two hospital services. Over 3 months, teams were randomized to receive feedback (GIH A) or no feedback (GIH B, control group); feedback consisted of an email sent twice weekly to all physicians on the GIH A service detailing the length of stay (LOS) and real-time cost of care accrued by each inpatient.
Over 3 months, care was provided to 56 (GIH A) and 47 (GIH B) inpatients with a GIH illness. Patient complexity level was similar for both services as demonstrated by mean relative value: 1.11 (GIH A) vs. 1.27 (GIH B), p=0.2. Weighted LOS and weighted cost of care values were calculated to adjust for the respective RV of each patient. Mean weighted LOS (10.8 [GIH A] vs. 13.8 days/pt [GIH B], p=0.02) and mean weighted cost of care (9,904 [GIH A] vs. 12,654 euros/pt [GIH B], p=0.02) were significantly lower in the feedback group. Subsequent hospital readmission rates did not differ among both groups.
Systematic feedback on cost of care was associated with lower healthcare costs without compromising quality. Incorporating a running total of patient costs into computer software used to order patient tests may represent one approach to controlling healthcare expenses.
Irish Journal of Medical Science 02/2013; 182(3). DOI:10.1007/s11845-013-0923-0 · 0.83 Impact Factor
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