Background: The use of wearable devices for remote health monitoring is a rapidly expanding field. These devices might benefit patients and providers; however, they are not yet widely used in healthcare. This scoping review assesses the current state of the literature on wearable devices for remote health monitoring in non-hospital settings. Methods: CINAHL, Scopus, Embase and MEDLINE were searched until August 5, 2024. We performed citation searching and searched Google Scholar. Studies on wearable devices in an outpatient setting with a clinically relevant, measurable outcome were included and were categorized according to intended use of data: monitoring of existing disease vs. diagnosis of new disease. Results: Eighty studies met eligibility criteria. Most studies used device data to monitor a chronic disease (68/80, 85%), most often neurodegenerative (22/68, 32%). Twelve studies (12/80, 15%) used device data to diagnose new disease, majority being cardiovascular (9/12, 75%). A range of wearable devices were studied with watches and bracelets being most common (50/80, 63%). Only six studies (8%) were randomized controlled trials, four of which (67%) showed evidence of positive clinical impact. Feasibility determinants were inconsistently reported, including compliance (51/80, 64%), patient-reported useability (13/80, 16%), and participant technology literacy (1/80, 1%). Conclusions: Evidence for clinical effectiveness of wearable devices remains scant. Heterogeneity across studies in terms of devices, disease targets and monitoring protocols makes data synthesis challenging, especially given the rapid pace of technical innovation. These findings provide direction for future research and implementation of wearable devices in healthcare.

Objective: Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data. Method: We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 ( n = 699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 ( n = 174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 ( n = 316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes. Results: With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders. Conclusion: Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.

Context/Setting: The script theory of diagnostic reasoning proposes that clinicians evaluate cases in the context of an “illness script,” iteratively testing internal hypotheses against new information eventually reaching a diagnosis. We present a novel tool for teaching diagnostic reasoning to undergraduate medical students based on an adaptation of script theory. Intervention: We developed a virtual patient case that used clinically authentic audio and video, interactive three-dimensional (3D) body images, and a simulated electronic medical record. Next, we used interactive slide bars to record respondents’ likelihood estimates of diagnostic possibilities at various stages of the case. Responses were dynamically compared to data from expert clinicians and peers. Comparative frequency distributions were presented to the learner and final diagnostic likelihood estimates were analyzed. Detailed student feedback was collected. Observations: Over two academic years, 322 students participated. Student diagnostic likelihood estimates were similar year to year, but were consistently different from expert clinician estimates. Student feedback was overwhelmingly positive: students found the case was novel, innovative, clinically authentic, and a valuable learning experience. Discussion: We demonstrate the successful implementation of a novel approach to teaching diagnostic reasoning. Future study may delineate reasoning processes associated with differences between novice and expert responses.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: First responders are confronted with traumatic event(s) in their work that have a significant toll on their psychological health and that may contribute to or result in post-traumatic stress injuries (PTSI) for many responders. Persons with a PTSI usually seek management therapies, which they hope will be effective. Evidence indicates that digital delivery of these therapies is an innovative, efficient and effective way to improve PTSI symptoms as in-person delivery. Objective: This project aims to implement and provide accessible, convenient and economical SMS text messaging services, known asText4PTSI and Text4Wellbeing, to first responders in Alberta, Canada, to prevent and improve the symptoms of PTSI among first responders and to improve their overall quality of life. We will evaluate post-traumatic symptoms and the impact of Text4PTSI and Text4Wellbeing on stress, anxiety and depression in relation to the correspondent's demographic backgrounds. Methods: First Responders who subscribe to Text4PTSI or Text4Wellbeing receive daily supportive and psychoeducational text messages for six months. The text messages are pre-programmed into an online software program delivering messages to subscribers. Baseline and follow-up data are collected through online questionnaires using validated scales at enrollments of 6 weeks, 12 weeks and 24 weeks (endpoint). In-depth interviews will be conducted to assess satisfaction with the text-based intervention. Results: We hypothesize that participants who enrol in this program will have improved PTSI symptoms, increased or improved quality of life and significant reduction of associated stress, depression and anxiety symptoms, among other psychological concerns. Improvement will be determined in comparison to established baseline parameters. Conclusions: This research will be beneficial for practitioners and will inform policy and decision-making regarding psychological interventions for PTSI. Lessons from this study will inform the scale-up of the intervention, a cost-effective, zero contact therapeutic option to manage PTSI.

Background: A prolonged COVID-19 pandemic has the potential to trigger a global mental health crisis increasing demand for mental health emergency services. We undertook a rapid review of the impact of pandemics and epidemics on emergency department utilization for mental health (MH) and substance use (SU).Objective: To rapidly synthesize available data on emergency department utilization for psychiatric concerns during COVID-19.Methods: An information specialist searched Medline, Embase, Psycinfo, CINAHL, and Scopus on June 16, 2020 and updated the search on July 24, 2020. Our search identified 803 abstracts, 7 of which were included in the review. Six articles reported on the COVID-19 pandemic and one on the SARS epidemic.Results: All studies reported a decrease in overall and MH related ED utilization during the early pandemic/epidemic. Two studies found an increase in SU related visits during the same period. No data were available for mid and late stage pandemics and the definitions for MH and SU related visits were inconsistent across studies.Conclusions: Our results suggest that COVID-19 has resulted in an initial decrease in ED visits for MH and an increase in visits for SU. Given the relative paucity of data on the subject and inconsistent analytic methods used in existing studies, there is an urgent need for investigation of pandemic-related changes in ED case-mix to inform system-level change as the pandemic continues.

Background: The exponential growth of publications regarding the application of machine learning (ML) tools in medicine highlights the significant potential for ML to revolutionize the field. Despite the multitude of literature surrounding this topic, there are limited publications addressing the implementation and feasibility of ML models in clinical practice. Currently, Machine Learning Operations (MLOps), a set of practices designed to deploy and maintain ML models in production, is used in various information technology and industrial settings. However, the MLOps pipeline is not well researched in medical settings, where multiple barriers exist to implementing ML pipelines into practice. Objective: This study aims to detail how MLOps is implemented in health care and propose a maturity framework for the health care implementations. Methods: A scoping review search was conducted according to the Joanna Briggs Institute Manual for Evidence Synthesis. Results were synthesized using the 3-stage basic qualitative content analysis. We searched 4 databases (eg, MEDLINE, Embase, Web of Science, and Scopus) to include any studies that involved proof of concept or real-world implementation of MLOps in health care. Studies not reported in English were excluded. Results: A total of 19 studies were included in this scoping review. The MLOps workflow identified within the studies included (1) data extraction (19/19 studies), (2) data preparation and engineering (18/19 studies), (3) model training (19/19 studies), (4) measured ML metrics and model evaluation (17/19 studies), (5) model validation and test in production (14/19 studies), (6) model serving and deployment (15/19 studies), (7) continuous monitoring (14/19 studies), and (8) continual learning (13/19 studies). We proposed a 3-stage MLOps maturity framework for health care based on existing studies in the field, that is, low (5/19 studies), partial (1/19 studies), and full maturity (13/19 studies). There were 8/19 studies that discussed ethical, legislative, and stakeholder considerations for MLOps implementations in health care settings. Conclusions: We investigated the implementation of MLOps in health care with a corresponding maturity framework. It is evident that only a limited number of studies reported the implementation of ML in health care contexts. Hence, it is imperative that we shift our focus toward creating an environment that supports the development of ML health care applications, such as improving existing data infrastructure, and engaging partners to support the development of MLOps applications. Specifically, we can include patients, policymakers, and health care professionals in the creation and implementation of ML applications. One of the main limitations includes the varying quality of each extracted study in terms of how the MLOps implementation was presented. Hence, it was difficult to verify the presence and discuss in depth all steps of the MLOps workflow for each study. Furthermore, due to the inherent nature of a scoping review protocol, there may be a compromise on an in-depth discussion of each step within the MLOps workflow.