Background Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs–based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. Objective This study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning–based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. Methods PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs,” “clinical deterioration,” and “machine learning.” Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. Results We identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. Conclusions In studies that compared performance, reported results suggest that machine learning–based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

Objectives Information and communication technology is often lauded as the key to enhancing communication among health care providers. However, its impact on interprofessional collaboration is unclear. The objective of this study was to determine the extent to which it improves communication and, subsequently, enhances interprofessional collaboration in chronic disease management. Methods A systematic review of academic literature using two electronic platforms: HealthSTAR and Web of Science (core collection and MEDLINE). To be eligible for inclusion in the review, articles needed to be peer-reviewed; accessible in English and focused on how technology supports, or might support, collaboration (through enhanced communication) in chronic disease management. Studies were assessed for quality and a narrative synthesis conducted. Results The searches identified 289 articles of which six were included in the final analysis (three used qualitative methods, two were descriptive and one used mixed methods). Various forms of information and communication technology were described including electronic health records, online communities/learning resources and telehealth/telecare. Three themes emerged from the studies that may provide insights into how communication that facilitates collaboration in chronic disease management might be enhanced: professional conflict, collective engagement and continuous learning. Conclusions The success of technology in enhancing collaboration for chronic disease management depends upon supporting the social relationships and organization in which the technology will be placed. Decision-makers should take into account and work toward balancing the impact of technology together with the professional and cultural characteristics of health care teams.

No abstract

Optimizing the transition between child and adolescent mental health services (CAMHS) and adult mental health services (AMHS) is a priority for healthcare systems. The purpose of this systematic review is to: (1) identify and compare models of care that may be used to facilitate the transition from CAMHS to AMHS; and (2) discuss trends and implications to inform future research and practice. Results identified three models of care which move beyond healthcare services and incorporate a broader range of services that better meet the dynamic needs of transition-aged youth. Joint working among providers, coupled with individualized approaches, is essential to facilitating continuity of care.

Evaluated interventions described in the transitions literature for youth with mental health disorders predominantly focus on vocational needs. The least studied areas were the personal and interpersonal domains. These domains were only incorporated within interventions addressing multiple domains of developmental transitions. These insights can be helpful in guiding evidence-based practice and policy development, as well as informing gaps for future research programmes.