Testing E-mail Content to Encourage Physicians to Access an Audit and Feedback Tool: A Factorial Randomized Experiment

Background: In Ontario, an online audit and feedback tool that provides primary care physicians with detailed information about patients who are overdue for cancer screening is underused. In the present study, we aimed to examine the effect of messages operationalizing 3 behaviour change techniques on access to the audit and feedback tool and on cancer screening rates. Methods: During May–September 2017, a pragmatic 2×2×2 factorial experiment tested 3 behaviour change techniques: anticipated regret, material incentive, and problem-solving. Outcomes were assessed using routinely collected administrative data. A qualitative process evaluation explored how and why the e-mail messages did or did not support Screening Activity Report access. Results: Of 5449 primary care physicians randomly allocated to 1 of 8 e-mail messages, fewer than half opened the messages and fewer than 1 in 10 clicked through the messages. Messages with problem-solving content were associated with a 12.9% relative reduction in access to the tool (risk ratio: 0.871; 95% confidence interval: 0.791 to 0.958; p = 0.005), but a 0.3% increase in cervical cancer screening (rate ratio: 1.003; 95% confidence interval: 1.001 to 1.006; p = 0.003). If true, that association would represent 7568 more patients being screened. No other significant effects were observed. Conclusions: For audit and feedback to work, recipients must engage with the data; for e-mail messages to prompt activity, recipients must open and review the message content. This large factorial experiment demonstrated that small changes in the content of such e-mail messages might influence clinical behaviour. Future research should focus on strategies to make cancer screening more user-centred

Effects of design features of explicit values clarification methods: A systematic review

Background. Diverse values clarification methods exist. It is important to understand which, if any, of their design features help people clarify values relevant to a health decision. Purpose. To explore the effects of design features of explicit values clarification methods on outcomes including decisional conflict, values congruence, and decisional regret. Data Sources. MEDLINE, all EBM Reviews, CINAHL, EMBASE, Google Scholar, manual search of reference lists, and expert contacts. Study Selection. Articles were included if they described the evaluation of 1 or more explicit values clarification methods. Data Extraction. We extracted details about the evaluation, whether it was conducted in the context of actual or hypothetical decisions, and the results of the evaluation. We combined these data with data from a previous review about each values clarification method's design features. Data Synthesis. We identified 20 evaluations of values clarification methods within 19 articles. Reported outcomes were heterogeneous. Few studies reported values congruence or postdecision outcomes. The most promising design feature identified was explicitly showing people the implications of their values, for example, by displaying the extent to which each of their decision options aligns with what matters to them. Limitations. Because of the heterogeneity of outcomes, we were unable to perform a meta-analysis. Results should be interpreted with caution. Conclusions. Few values clarification methods have been evaluated experimentally. More research is needed to determine effects of different design features of values clarification methods and to establish best practices in values clarification. When feasible, evaluations should assess values congruence and postdecision measures of longer-term outcomes

A Personalized Avatar-Based Web Application to Help People Understand How Social Distancing Can Reduce the Spread of COVID-19: Cross-sectional, Observational, Pre-Post Study

BackgroundTo reduce the transmission of SARS-CoV-2 and the associated spread of COVID-19, many jurisdictions around the world imposed mandatory or recommended social or physical distancing. As a result, at the beginning of the pandemic, various communication materials appeared online to promote distancing. Explanations of the science underlying these mandates or recommendations were either highly technical or highly simplified. ObjectiveThis study aimed to understand the effects of a dynamic visualization on distancing. Our overall aim was to help people understand the dynamics of the spread of COVID-19 in their community and the implications of their own behavior for themselves, those around them, the health care system, and society. MethodsUsing Scrum, which is an agile framework; JavaScript (Vue.js framework); and code already developed for risk communication in another context of infectious disease transmission, we rapidly developed a new personalized web application. In our application, people make avatars that represent themselves and the people around them. These avatars are integrated into a 3-minute animation illustrating an epidemiological model for COVID-19 transmission, showing the differences in transmission with and without distancing. During the animation, the narration explains the science of how distancing reduces the transmission of COVID-19 in plain language in English or French. The application offers full captions to complement the narration and a descriptive transcript for people using screen readers. We used Google Analytics to collect standard usage statistics. A brief, anonymous, optional survey also collected self-reported distancing behaviors and intentions in the previous and coming weeks, respectively. We launched and disseminated the application on Twitter and Facebook on April 8, 2020, and April 9, 2020. ResultsAfter 26 days, the application received 3588 unique hits from 82 countries. The optional survey at the end of the application collected 182 responses. Among this small subsample of users, survey respondents were nearly (170/177, 96%) already practicing distancing and indicated that they intended to practice distancing in the coming week (172/177, 97.2%). Among the small minority of people (n=7) who indicated that they had not been previously practicing distancing, 2 (29%) reported that they would practice distancing in the week to come. ConclusionsWe developed a web application to help people understand the relationship between individual-level behavior and population-level effects in the context of an infectious disease spread. This study also demonstrates how agile development can be used to quickly create personalized risk messages for public health issues like a pandemic. The nonrandomized design of this rapid study prevents us from concluding the application’s effectiveness; however, results thus far suggest that avatar-based visualizations may help people understand their role in infectious disease transmission

Involving members of vulnerable populations in the development of patient decision aids: a mixed methods sequential explanatory study

Abstract Background Patient decision aids aim to present evidence relevant to a health decision in understandable ways to support patients through the process of making evidence-informed, values-congruent health decisions. It is recommended that, when developing these tools, teams involve people who may ultimately use them. However, there is little empirical evidence about how best to undertake this involvement, particularly for specific populations of users such as vulnerable populations. Methods To describe and compare the development practices of research teams that did and did not specifically involve members of vulnerable populations in the development of patient decision aids, we conducted a secondary analysis of data from a systematic review about the development processes of patient decision aids. Then, to further explain our quantitative results, we conducted semi-structured telephone interviews with 10 teams: 6 that had specifically involved members of vulnerable populations and 4 that had not. Two independent analysts thematically coded transcribed interviews. Results Out of a total of 187 decision aid development projects, 30 (16%) specifically involved members of vulnerable populations. The specific involvement of members of vulnerable populations in the development process was associated with conducting informal needs assessment activities (73% vs. 40%, OR 2.96, 95% CI 1.18–7.99, P = .02) and recruiting participants through community-based organizations (40% vs. 11%, OR 3.48, 95% CI 1.23–9.83, P = .02). In interviews, all developers highlighted the importance, value and challenges of involving potential users. Interviews with developers whose projects had involved members of vulnerable populations suggested that informal needs assessment activities served to center the decision aid around users’ needs, to better avoid stigma, and to ensure that the topic truly matters to the community. Partnering with community-based organizations may facilitate relationships of trust and may also provide a non-threatening and accessible location for research activities. Conclusions There are a small number of key differences in the development processes for patient decision aids in which members of vulnerable populations were or were not specifically involved. Some of these practices may require additional time or resources. To address health inequities, researchers, communities and funders may need to increase awareness of these approaches and plan accordingly