The do's, don't and don't knows of supporting transition to more independent practice

Introduction: Transitions are traditionally viewed as challenging for clinicians. Throughout medical career pathways, clinicians need to successfully navigate successive transitions as they become progressively more independent practitioners. In these guidelines, we aim to synthesize the evidence from the literature to provide guidance for supporting clinicians in their development of independence, and highlight areas for further research. Methods: Drawing upon D3 method guidance, four key themes universal to medical career transitions and progressive independence were identified by all authors through discussion and consensus from our own experience and expertise: workplace learning, independence and responsibility, mentoring and coaching, and patient perspectives. A scoping review of the literature was conducted using Medline database searches in addition to the authors’ personal archives and reference snowballing searches. Results: 387 articles were identified and screened. 210 were excluded as not relevant to medical transitions (50 at title screen; 160 at abstract screen). 177 full-text articles were assessed for eligibility; a further 107 were rejected (97 did not include career transitions in their study design; 10 were review articles; the primary references of these were screened for inclusion). 70 articles were included of which 60 provided extractable data for the final qualitative synthesis. Across the four key themes, seven do’s, two don’ts and seven don’t knows were identified, and the strength of evidence was graded for each of these recommendations. Conclusion: The two strongest messages arising from current literature are first, transitions should not be viewed as one moment in time: career trajectories are a continuum with valuable opportunities for personal and professional development throughout. Second, learning needs to be embedded in practice and learners provided with authentic and meaningful learning opportunities. In this paper, we propose evidence-based guidelines aimed at facilitating such transitions through the fostering of progressive independence

Big Data for the Greater Good: An Introduction

Big Data, perceived as one of the breakthrough technological developments of our times, has the potential to revolutionize essentially any area of knowledge and impact on any aspect of our life. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, analysts, researchers, and business users can analyze previously inaccessible or unusable data to gain new insights resulting in better and faster decisions, and producing both economic and social value; it can have an impact on employment growth, productivity, the development of new products and services, traffic management, spread of viral outbreaks, and so on. But great opportunities also bring great challenges, such as the loss of individual privacy. In this chapter, we aim to provide an introduction into what Big Data is and an overview of the social value that can be extracted from it; to this aim, we explore some of the key literature on the subject. We also call attention to the potential ‘dark’ side of Big Data, but argue that more studies are needed to fully understand the downside of it. We conclude this chapter with some final reflections

Outcome measures in clinical trials of treatments for acute severe haemorrhage

BACKGROUND: Acute severe haemorrhage is a common complication of injury, childbirth, surgery, gastrointestinal pathologies and other medical conditions. Bleeding is a major cause of death, but patients also die from non-bleeding causes, the frequency of which varies by the site of haemorrhage and between populations. Because patients can bleed to death within hours, established interventions inevitably take priority over randomisation into a trial. These circumstances raise challenges in selecting appropriate outcome measures for clinical trials of haemostatic interventions. MAIN BODY: We use data from three large randomised controlled trials in acute severe haemorrhage (CRASH-2, WOMAN and HALT-IT) to explore the strengths and limitations of outcome measures commonly used in trials of haemostatic treatments, including all-cause and cause-specific mortality, blood transfusion and surgical interventions. Many deaths following acute severe haemorrhage are due to patient comorbidities or complications rather than bleeding. If non-bleeding deaths are unaffected by a haemostatic intervention, even large trials will have low power to detect an effect on all-cause mortality. Due to the dilution from deaths unaffected or reduced by the trial treatment, all-cause mortality can also obscure important harmful effects. Additionally, because the relative contributions of different causes of death vary within and between patient populations, all-cause mortality is not generalisable. Different causes of death occur at different time intervals from bleeding onset, with bleeding deaths generally occurring early. Time-specific mortality can therefore be used as a proxy for cause in un-blinded trials where bias is a concern or in situations where cause of death cannot be assessed. Urgent treatment is critical, and so post-randomisation blood transfusion and surgery are often planned before or at the time of randomisation and therefore cannot be influenced by the trial treatment. CONCLUSIONS: All-cause mortality has low power, lacks generalisability and can obscure harmful effects. Cause-specific mortality, such as death due to bleeding or thrombosis, avoids these drawbacks. In certain scenarios, time-specific mortality can be used as a proxy for cause-specific mortality. Blood transfusion and surgical procedures have limited utility as outcome measures in trials of haemostatic treatments