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BackgroundChatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. ObjectiveThis study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. MethodsWe searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. ResultsThe original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. ConclusionsMany chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.

BackgroundWhile selecting predictive tools for implementation in clinical practice or for recommendation in clinical guidelines, clinicians and health care professionals are challenged with an overwhelming number of tools. Many of these tools have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (the GRASP framework). This framework was based on the critical appraisal of the published evidence on such tools. ObjectiveThe aim of the study was to examine the impact of using the GRASP framework on clinicians’ and health care professionals’ decisions in selecting clinical predictive tools. MethodsA controlled experiment was conducted through a web-based survey. Participants were randomized to either review the derivation publications, such as studies describing the development of the predictive tools, on common traumatic brain injury predictive tools (control group) or to review an evidence-based summary, where each tool had been graded and assessed using the GRASP framework (intervention group). Participants in both groups were asked to select the best tool based on the greatest validation or implementation. A wide group of international clinicians and health care professionals were invited to participate in the survey. Task completion time, rate of correct decisions, rate of objective versus subjective decisions, and level of decisional conflict were measured. ResultsWe received a total of 194 valid responses. In comparison with not using GRASP, using the framework significantly increased correct decisions by 64%, from 53.7% to 88.1% (88.1/53.7=1.64; t193=8.53; P

Abstract Public health programs’ successful implementation often requires a thorough consideration ⁠ of local cultural contexts and effective community engagement. The complexities and obstacles that public health initiatives may face in culturally ⁠ diverse communities are highlighted by our case study, offering valuable insights. ‍ This article focuses on analyzing a particular incident that occurred in Gobe Challa Village, connected ⁠ to the implementation of the Child Health and Mortality Prevention Surveillance (CHAMPS) program. In Gobe Challa Village, an incident occurred that centered on discord within the community and religious fears, resulting ⁠ from misunderstandings surrounding and a general lack of comprehension about the CHAMPS program. Our resolution process involved engaging the community, and religious leaders in constructive dialogue. Throughout this involvement, we delivered extensive elucidation on the aims and ⁠ protocols of the CHAMPS program, mitigating conflicts within the community. The community successfully achieved acceptance of the CHAMPS program by directly ⁠ and respectfully addressing misconceptions and fears, fostering a common understanding. This study emphasizes the significance of effective communication, community engagement, and ⁠ consideration of cultural customs when implementing public health programs. The experiences derived from this incident can act as a practical ⁠ manual for future public health initiatives encountering analogous cultural and societal dynamics. The main emphasis is on highlighting how a proactive, sensitive, and inclusive approach can ⁠ alleviate potential conflicts while also promoting program acceptance and encouraging community engagement. These discoveries have relevance to various public health initiatives and emphasize the significance ⁠ of a culturally conscious, community-involved approach in executing global health programs. ‍