Malaria vector species in Colombia: a review

Here we present a comprehensive review of the literature on the vectorial importance of the major Anopheles malaria vectors in Colombia. We provide basic information on the geographical distribution, altitudinal range, immature habitats, adult behaviour, feeding preferences and anthropophily, endophily and infectivity rates. We additionally review information on the life cycle, longevity and population fluctuation of Colombian Anopheles species. Emphasis was placed on the primary vectors that have been epidemiologically incriminated in malaria transmission: Anopheles darlingi, Anopheles albimanus and Anopheles nuneztovari. The role of a selection of local, regional or secondary vectors (e.g., Anopheles pseudopunctipennis and Anopheles neivai) is also discussed. We highlight the importance of combining biological, morphological and molecular data for the correct taxonomical determination of a given species, particularly for members of the species complexes. We likewise emphasise the importance of studying the bionomics of primary and secondary vectors along with an examination of the local conditions affecting the transmission of malaria. The presence and spread of the major vectors and the emergence of secondary species capable of transmitting human Plasmodia are of great interest. When selecting control measures, the anopheline diversity in the region must be considered. Variation in macroclimate conditions over a species' geographical range must be well understood and targeted to plan effective control measures based on the population dynamics of the local Anopheles species

Digital health training programs for medical students: a scoping review

Background: Medical schools worldwide are accelerating the introduction of digital health courses into their curricula. This review collated and analyzed the literature evaluating digital health education for medical students to inform development of future courses and identify areas where curricula may need to be strengthened. Methods: We carried out a scoping review following the Joanna Briggs Institute’s guidance and reported in line with PRISMA-ScR guidelines. We searched six major bibliographic databases and grey literature sources for the articles published from January 2000 to November 2019. Two authors independently screened the retrieved citations and extracted the data from the included studies. Discrepancies were resolved by consensus discussion between the authors. The findings were analyzed using thematic analysis and presented narratively. Results: A total of 34 studies focusing on different digital courses were included in this review. Most (n=22) were published from 2010 to 2019 and originated from the US (n=20). The reported digital health courses were mostly elective (n=20), integrated into the existing curriculum (n=24) and focused mainly on medical informatics (n=17). Most of the courses targeted medical students from first to third year (n=17) and the duration of the courses ranged from an hour to three academic years. Most (n=22) reported the use of blended education. Six of 34 delivered courses entirely digitally using online modules, offline learning, Massive Open Online Courses, and virtual patient simulations. The reported courses used various assessment approaches such as paper-based assessments, in person observations and/or online-based assessment. Thirty studies evaluated courses mostly using uncontrolled before and after design and generally reported improvements in students’ learning outcomes. Conclusions Digital health courses reported in the literature were mostly elective, focused on a single area of digital health and lacked robust evaluation. They had diverse delivery, development, and assessment approaches. There is an urgent need for high-quality studies evaluating digital health education

Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators

Background: Though artificial intelligence (AI) in healthcare has great potential, medicine has been slowto adopt AI tools. Barriers and facilitators to clinical AI implementation among healthcare professionals (theend-users) are ill defined, nor have appropriate implementation strategies to overcome them been suggested.Therefore, we aim to study these barriers and facilitators, and find general insights that could be applicableto a wide variety of AI-tool implementations in clinical practice.Methods: We conducted a mixed-methods study encompassing individual interviews, a focus group, and anationwide survey. End-users of AI in healthcare (physicians) from various medical specialties were included.We performed deductive direct content analysis, using the Consolidated Framework for ImplementationResearch (CFIR) for coding. CFIR constructs were entered into the Expert Recommendations forImplementing Change (ERIC) to find suitable implementation strategies. Quantitative survey data wasdescriptively analyzed.Results: We performed ten individual interviews, and one focus group with five physicians. The mostprominent constructs identified during the qualitative interim analyses were incorporated in the nationwidesurvey, which had 106 survey respondents. We found nine CFIR constructs important to AI implementation:evidence strength, relative advantage, adaptability, trialability, structural characteristics, tension for change,compatibility, access to knowledge and information, and knowledge and beliefs about the intervention.Consequently, the ERIC tool displayed the following strategies: identify and prepare champions, conducteducational meetings, promote adaptability, develop educational materials, and distribute educationalmaterials. (PDF) Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators. Available from: https://www.researchgate.net/publication/366636295_Implementing_artificial_intelligence_in_clinical_practice_a_mixed-method_study_of_barriers_and_facilitators [accessed Jan 17 2023]