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Objective: The primary objective of this scoping review was to identify and describe state-of-the-art models that use vital sign monitoring to predict clinical deterioration on the general ward. The secondary objective was to identify facilitators, barriers, and effects of implementing these models., Data Sources: PubMed, Embase, and CINAHL databases until November 2020., Study Selection: We selected studies that compared vital signs-based automated real-time predictive algorithms to current track-and-trace protocols in regard to the outcome of clinical deterioration in a general ward population., Data Extraction: Study characteristics, predictive characteristics and barriers, facilitators, and effects., Results: We identified 1741 publications, 21 of which were included in our review. Two of the these were clinical trials, 2 were prospective observational studies, and the remaining 17 were retrospective studies. All of the studies focused on hospitalized adult patients. The reported area under the receiver operating characteristic curves ranged between 0.65 and 0.95 for the outcome of clinical deterioration. Positive predictive value and sensitivity ranged between 0.223 and 0.773 and from 7.2% to 84.0%, respectively. Input variables differed widely, and predicted endpoints were inconsistently defined. We identified 57 facilitators and 48 barriers to the implementation of these models. We found 68 reported effects, 57 of which were positive., Conclusion: Predictive algorithms can detect clinical deterioration on the general ward earlier and more accurately than conventional protocols, which in one recent study led to lower mortality. Consensus is needed on input variables, predictive time horizons, and definitions of endpoints to better facilitate comparative research.

Background: Collaborative writing applications (CWAs), such as wikis and Google Documents, hold the potential to improve the use of evidence in both public health and healthcare. Although a growing body of literature indicates that CWAs could have positive effects on healthcare, such as improved collaboration, behavioural change, learning, knowledge management, and adaptation of knowledge to local context, this has never been assessed systematically. Moreover, several questions regarding safety, reliability, and legal aspects exist., Objectives: The objectives of this review were to (1) assess the effects of the use of CWAs on process (including the behaviour of healthcare professionals) and patient outcomes, (2) critically appraise and summarise current evidence on the use of resources, costs, and cost-effectiveness associated with CWAs to improve professional practices and patient outcomes, and (3) explore the effects of different CWA features (e.g. open versus closed) and different implementation factors (e.g. the presence of a moderator) on process and patient outcomes., Search Methods: We searched CENTRAL, MEDLINE, Embase, and 11 other electronic databases. We searched the grey literature, two trial registries, CWA websites, individual journals, and conference proceedings. We also contacted authors and experts in the field. We did not apply date or language limits. We searched for published literature to August 2016, and grey literature to September 2015., Selection Criteria: We included randomised controlled trials (RCTs), non-randomised controlled trials (NRCTs), controlled before-and-after (CBA) studies, interrupted time series (ITS) studies, and repeated measures studies (RMS), in which CWAs were used as an intervention to improve the process of care, patient outcomes, or healthcare costs., Data Collection and Analysis: Teams of two review authors independently assessed the eligibility of studies. Disagreements were resolved by discussion, and when consensus was not reached, a third review author was consulted., Main Results: We screened 11,993 studies identified from the electronic database searches and 346 studies from grey literature sources. We analysed the full text of 99 studies. None of the studies met the eligibility criteria; two potentially relevant studies are ongoing., Authors' Conclusions: While there is a high number of published studies about CWAs, indicating that this is an active field of research, additional studies using rigorous experimental designs are needed to assess their impact and cost-effectiveness on process and patient outcomes.

Background: Health data personally collected by individuals with wearable devices and smartphones is becoming an important data source for healthcare, but also for medical research., Objective: To describe a new consent model that allows people to control their personally collected health data and determine to what extent they want to share these for research purposes., Methods: We developed, in close collaboration with patients, researchers, healthcare professionals, privacy experts, and an accredited Medical Ethical Review Committee, an innovative concept called "personalized consent flow" within a research platform connected to a personal health record. The development was an iterative process with informal meetings, semistructured interviews, and surveys. The final concept of the personalized consent flow was reviewed by patients and improved and approved by the same patients in a focus group., Results: This concept could result in optimal control for individual users, since they will answer questions about how they will share data. Furthermore, it enables users to collect data for specific studies and add expiration dates to their data. This work facilitates further discussion about dynamic and personalized consent. A pilot study with the personalized consent model is currently being carried out.