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Objectives This article describes a method for developing electronic health record (EHR) tools for use in primary care settings. Methods The “Translating Research into Agile Development” (TRIAD) method relies on the close collaboration of researchers, end users, and development teams. This five-step method for designing a tailored EHR tool includes (1) assessment, observation, and documentation; (2) structured engagement for collaboration and iterative data collection; (3) data distillation; (4) developmental feedback from clinical team members on high-priority EHR needs and input on design prototypes and EHR functionality; and (5) agile scrum sprint cycles for prototype development. Results The TRIAD method was used to modify an existing EHR for behavioral health clinicians (BHCs) embedded with primary care teams, called the BH e-Suite. The structured engagement processes stimulated discussions on how best to automate BHC screening tools and provide goal tracking functionality over time. Data distillation procedures rendered technical documents, with information on workflow steps, tasks, and associated challenges. In the developmental feedback phase, BHCs gave input on screening assessments, scoring needs, and other functionality to inform prototype feature development. Six 2-week sprint cycles were conducted to address three domains of prototype development: assessment and documentation needs, information retrieval, and monitoring and tracking. The BH e-Suite tool resulted with eight new EHR features to accommodate BHCs' needs. Conclusion The TRIAD method can be used to develop EHR functionality to address the evolving needs of health professionals in primary care and other settings. The BH e-Suite was developed through TRIAD and was found to be acceptable, easy to use, and improved care delivery during pilot testing. The BH e-Suite was later adopted by OCHIN Inc., which provided the tool to its 640 community health centers. This suggests that the TRIAD method is a promising research and development approach.

It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field ; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations.

IntroductionFor many organizations, limited budgets and phased funding restrict the development of digital health tools. This problem is often exacerbated by the ever-increasing sophistication of technology and costs related to programming and maintenance. Traditional development methods tend to be costly and inflexible and not client centered. The purpose of this study is to analyze the use of Agile software development and outcomes of a three-phase mHealth program designed to help young adult Quebecers quit smoking.MethodsIn Phase I, literature reviews, focus groups, interviews, and behavior change theory were used in the adaption and re-launch of an existing evidence-based mHealth platform. Based on analysis of user comments and utilization data from Phase I, the second phase expanded the service to allow participants to live text-chat with counselors. Phase II evaluation led to the third and current phase, in which algorithms were introduced to target pregnant smokers, substance users, students, full-time workers, those affected by mood disorders and chronic disease.ResultsData collected throughout the three phases indicate that the incremental evolution of the intervention has led to increasing numbers of smokers being enrolled while making functional enhancements. In Phase I (240 days) 182 smokers registered with the service. 51% (n = 94) were male and 61.5% (n = 112) were between the ages of 18-24. In Phase II (300 days), 994 smokers registered with the service. 51% (n = 508) were male and 41% (n = 403) were between the ages of 18-24. At 174 days to date 873 smokers have registered in the third phase. 44% (n = 388) were male and 24% (n = 212) were between the ages of 18-24.ConclusionsEmerging technologies in behavioral science show potential, but do not have defined best practices for application development. In phased-based projects with limited funding, Agile appears to be a viable approach to building and expanding digital tools.