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BACKGROUND: Systematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial intelligence and active-machine learning (AML) have been implemented into several SR software applications. As some of the barriers to adoption of new technologies are the challenges in set-up and how best to use these technologies, we have provided different situations and considerations for knowledge synthesis teams to consider when using artificial intelligence and AML for title and abstract screening. METHODS: We retrospectively evaluated the implementation and performance of AML across a set of ten historically completed systematic reviews. Based upon the findings from this work and in consideration of the barriers we have encountered and navigated during the past 24 months in using these tools prospectively in our research, we discussed and developed a series of practical recommendations for research teams to consider in seeking to implement AML tools for citation screening into their workflow. RESULTS: We developed a seven-step framework and provide guidance for when and how to integrate artificial intelligence and AML into the title and abstract screening process. Steps include: (1) Consulting with Knowledge user/Expert Panel; (2) Developing the search strategy; (3) Preparing your review team; (4) Preparing your database; (5) Building the initial training set; (6) Ongoing screening; and (7) Truncating screening. During Step 6 and/or 7, you may also choose to optimize your team, by shifting some members to other review stages (e.g., full-text screening, data extraction). CONCLUSION: Artificial intelligence and, more specifically, AML are well-developed tools for title and abstract screening and can be integrated into the screening process in several ways. Regardless of the method chosen, transparent reporting of these methods is critical for future studies evaluating artificial intelligence and AML.

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The Australian clinical trials sector has grown steadily over the past decade, particularly with respect to early phase trials where Australia's research capacity, capability and quality of research is revered. With an increase in the number of internationally sponsored clinical research projects being conducted in Australia, particularly in the early phase setting, there has been a corresponding growth in the number of clinical research sites conducting early phase clinical trials. Australian researchers are guided by a multitude of research codes, guidance and statements which govern the conduct of clinical trials. Although international guidance regarding the conduct of early phase clinical trials exists, there is currently no single source outlining best practice recommendations for the conduct of early phase clinical trials in Australia. In recognition of this Clinical Trials: Impact & Quality (CT:IQ), a collaborative of sector stakeholders, convened a project team with comprehensive knowledge of the Australian clinical trials sector and particularly early phase research, to evaluate and collate broadly applicable and implementable guidance for the conduct of early phase clinical trials. Although the initial intent was to create guidance specific to early phase, we recognize the project outcomes are more broadly implementable irrespective of the research phase and are intended to support all clinical research sites to conduct high-quality clinical trials in Australia.

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OBJECTIVES: To develop best practice guidance for the use of retention strategies in randomized clinical trials (RCTs). STUDY DESIGN AND SETTING: Consensus development workshops conducted at two UK Clinical Trials Units. Sixty-six statisticians, clinicians, RCT coordinators, research scientists, research assistants, and data managers associated with RCTs participated. The consensus development workshops were based on the consensus development conference method used to develop best practice for treatment of medical conditions. Workshops commenced with a presentation of the evidence for incentives, communication, questionnaire format, behavioral, case management, and methodological retention strategies identified by a Cochrane review and associated qualitative study. Three simultaneous group discussions followed focused on (1) how convinced the workshop participants were by the evidence for retention strategies, (2) barriers to the use of effective retention strategies, (3) types of RCT follow-up that retention strategies could be used for, and (4) strategies for future research. Summaries of each group discussion were fed back to the workshop. Coded content for both workshops was compared for agreement and disagreement. Agreed consensus on best practice guidance for retention was identified. RESULTS: Workshop participants agreed best practice guidance for the use of small financial incentives to improve response to postal questionnaires in RCTs. Use of second-class post was thought to be adequate for postal communication with RCT participants. The most relevant validated questionnaire was considered best practice for collecting RCT data. Barriers identified for the use of effective retention strategies were: the small improvements seen in questionnaire response for the addition of monetary incentives, and perceptions among trialists that some communication strategies are outdated. Furthermore, there was resistance to change existing retention practices thought to be effective. Face-to-face and electronic follow-up technologies were identified as retention strategies for further research. CONCLUSIONS: We developed best practice guidance for the use of retention strategies in RCTs and identified potential barriers to the use of effective strategies. The extent of agreement on best practice is limited by the variability in the currently available evidence. This guidance will need updating as new retention strategies are developed and evaluated.

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There is a great divide between rural and urban areas, particularly in medical emergency care. Although medical best practice guidelines exist and are in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. In this paper, we propose the concept of distributed executable medical best practice guidance systems to assist adherence to best practice from the time that a patient first presents at a rural hospital, through diagnosis and ambulance transfer to arrival and treatment at a regional tertiary hospital center. We codify complex medical knowledge in the form of simplified distributed executable disease automata, from the thin automata at rural hospitals to the rich automata in the regional center hospitals. However, a main challenge is how to efficiently and safely synchronize distributed best practice models as the communication among medical facilities, devices, and professionals generates a large number of messages. This complex problem of patient diagnosis and transport from rural to center facility is also fraught with many uncertainties and changes resulting in a high degree of dynamism. A critically ill patient's medical conditions can change abruptly in addition to changes in the wireless bandwidth during the ambulance transfer. Such dynamics have yet to be addressed in existing literature on telemedicine. To address this situation, we propose a pathophysiological model-driven message exchange communication architecture that ensures the real-time and dynamic requirements of synchronization among distributed emergency best practice models are met in a reliable and safe manner. Taking the signs, symptoms, and progress of stroke patients transported across a geographically distributed healthcare network as the motivating use case, we implement our communication system and apply it to our developed best practice automata using laboratory simulations. Our proof-of-concept experiments shows there is potential for the use of our system in a wide variety of domains.

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