Parental Perspectives on Pediatric Surgical Recovery: Narrative Analysis of Free-Text Comments From a Postoperative Survey.

Background: Qualitative experience data can inform health care providers how to best support families during pediatric postoperative recovery. Patient experience data can also provide actionable information to guide health care quality improvement; positive feedback can confirm the efficacy of current practices and systems, while negative comments can identify areas for improvement.

Objective: This study aimed to understand families' perspectives regarding their children's surgical recovery using qualitative patient experience data (free-text comments) from a prospective cohort study conducted within a larger study developing a postoperative-outcome risk stratification model.

Development and internal validation of time-to-event risk prediction models for major medical complications within 30 days after elective colectomy.

Background: Patients undergoing colectomy are at risk of numerous major complications. However, existing binary risk stratification models do not predict when a patient may be at highest risks of each complication. Accurate prediction of the timing of complications facilitates targeted, resource-efficient monitoring.

Evaluating a mental health support mobile app for adults with type 1 diabetes living in rural and remote communities: The REACHOUT pilot study.

Aims: To evaluate a mobile app that delivers mental health support to adults with type 1 diabetes (T1D) living in rural and remote communities using the Reach, Effectiveness, Adoption, Intervention fidelity, Maintenance (RE-AIM) framework.

Methods: This study recruited 46 adults to participate in a 6-month intervention using REACHOUT, a mobile app that delivers peer-led mental health support (one-on-one, group-based texting and face-to-face virtual). Baseline and 6-month assessments measured diabetes distress (DD), depressive symptoms and perceived support (from family/friends, health care team and peers) along with other RE-AIM metrics.

Impact of Digitally Enabled Peer Support Interventions on Diabetes Distress and Depressive Symptoms in People Living with Type 1 Diabetes: A Systematic Review.

Purpose Of Review: To explore the impact of digitally-enabled peer support interventions on diabetes distress and depression for individuals living with Type 1 Diabetes (T1D).

Recent Findings: We synthesized the results of nine key studies from a review of 3,623 English-language articles published between January 2012 and January 2024. Three studies demonstrated significant reductions in diabetes distress, and two studies reported reductions in depression.

Caregiver and Youth Characteristics That Influence Trust in Digital Health Platforms in Pediatric Care: Mixed Methods Study.

Background: Combining patient-generated health data and digital health platforms may improve patient experience and population health, mitigate rising health care costs, reduce clinician burnout, and enable health equity. However, lack of trust may be a notable barrier to the data-sharing required by such platforms. Understanding sociodemographic, health, and personal characteristics will enable developers and implementers of such technologies to consider these in their technical design requirements.

Anesthetic-sparing effect of dexmedetomidine during total intravenous anesthesia for children undergoing dental surgery: A randomized controlled trial.

Background: Dexmedetomidine, an α-adrenergic agonist, reduces propofol and remifentanil requirements when used as an adjunct to total intravenous anesthesia in adults, but studies in a pediatric population are sparse. This study investigates the magnitude of dose-sparing effects of a postinduction dexmedetomidine bolus on propofol and remifentanil requirements during pediatric surgery.

Methods: In this randomized, double-blind, controlled trial, children aged 2-10 years undergoing elective dental surgery were assigned to one of four groups: placebo, 0.

Postsurgical Pain Risk Stratification to Enhance Pain Management Workflow in Adult Patients: Design, Implementation, and Pilot Evaluation.

Background: Exposure to opioids after surgery is the initial contact for some people who develop chronic opioid use disorder. Hence, effective postoperative pain management, with less reliance on opioids, is critical. The Perioperative Opioid Quality Improvement (POQI) program developed (1) a digital health platform leveraging patient-survey-reported risk factors and (2) a postsurgical pain risk stratification algorithm to personalize perioperative care by integrating several commercially available digital health solutions into a combined platform.

Pediatric Delirium Educational Tool Development With Intensive Care Unit Clinicians and Caregivers in Canada: Focus Group Study.

Background: Pediatric intensive care unit (PICU)-associated delirium contributes to a decline in postdischarge quality of life, with worse outcomes for individuals with delayed identification. As delirium screening rates remain low within PICUs, caregivers may be able to assist with early detection, for which they need more education, as awareness of pediatric delirium among caregivers remains limited.

Objective: This study aimed to develop an educational tool for caregivers to identify potential delirium symptoms during their child's PICU stay, educate them on how to best support their child if they experience delirium, and guide them to relevant family resources.

An Individualized Postoperative Pain Risk Communication Tool for Use in Pediatric Surgery: Co-Design and Usability Evaluation.

Background: Risk identification and communication tools have the potential to improve health care by supporting clinician-patient or family discussion of treatment risks and benefits and helping patients make more informed decisions; however, they have yet to be tailored to pediatric surgery. User-centered design principles can help to ensure the successful development and uptake of health care tools.

Objective: We aimed to develop and evaluate the usability of an easy-to-use tool to communicate a child's risk of postoperative pain to improve informed and collaborative preoperative decision-making between clinicians and families.

Enhanced recovery support for people with eating disorders during the COVID-19 pandemic: quality improvement using a web-based, stepped-care programme in Canada.

Background: Throughout the COVID-19 pandemic, the number of individuals struggling with eating disorders (EDs) increased substantially. Body Brave (a not-for-profit) created and implemented a web-based stepped-care Recovery Support Programme (RSP) to improve access to community-based ED services. This quality improvement study describes the RSP and assesses its ability to deliver timely access to treatment and platform engagement.

Dashboard of Short-Term Postoperative Patient Outcomes for Anesthesiologists: Development and Preliminary Evaluation.

Background: Anesthesiologists require an understanding of their patients' outcomes to evaluate their performance and improve their practice. Traditionally, anesthesiologists had limited information about their surgical outpatients' outcomes due to minimal contact post discharge. Leveraging digital health innovations for analyzing personal and population outcomes may improve perioperative care.

Using Human-Centered Design and Cocreation to Create the Live 5-2-1-0 Mobile App to Promote Healthy Behaviors in Children: App Design and Development.

Background: The prevalence of obesity among Canadian children is rising, partly because of increasingly obesogenic environments that limit opportunities for physical activity and healthy nutrition. Live 5-2-1-0 is a community-based multisectoral childhood obesity prevention initiative that engages stakeholders to promote and support the message of consuming ≥5 servings of vegetables and fruits, having <2 hours of recreational screen time, participating in ≥1 hour of active play, and consuming 0 sugary drinks every day. A Live 5-2-1-0 Toolkit for health care providers (HCPs) was previously developed and piloted in 2 pediatric clinics at British Columbia Children's Hospital.

Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks.

Improving the prediction of blood glucose concentration may improve the quality of life of people living with type 1 diabetes by enabling them to better manage their care. Given the anticipated benefits of such a prediction, numerous methods have been proposed. Rather than attempting to predict glucose concentration, a deep learning framework for prediction is proposed in which prediction is performed using a scale for hypo- and hyper-glycemia risk.

Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study.

Background: The perioperative period is a data-rich environment with potential for innovation through digital health tools and predictive analytics to optimize patients' health with targeted prehabilitation. Although some risk factors for postoperative pain following pediatric surgery are already known, the systematic use of preoperative information to guide personalized interventions is not yet widespread in clinical practice.

Objective: Our long-term goal is to reduce the incidence of persistent postsurgical pain (PPSP) and long-term opioid use in children by developing personalized pain risk prediction models that can guide clinicians and families to identify targeted prehabilitation strategies.

Identification of Requirements for a Postoperative Pediatric Pain Risk Communication Tool: Focus Group Study With Clinicians and Family Members.

Background: Pediatric surgery is associated with a risk of postoperative pain that can impact the family's quality of life. Although some risk factors for postoperative pain are known, these are often not consistently communicated to families. In addition, although tools for risk communication exist in other domains, none are tailored to pediatric surgery.

Has Virtual Care Arrived? A Survey of Rural Canadian Providers During the Early Stages of the COVID-19 Pandemic.

We investigated the uptake and perceptions of virtual care solutions by rural Canadian primary and specialist providers during the early phase (May-June 2020) of the COVID-19 pandemic. A web-based, cross-sectional survey of rural primary and specialty care providers examined types of virtual care platforms used (eg, phone, video), appointment length, experience and satisfaction with the solution used, plans for future use of virtual care, and patients' use of virtual care services. Targeted participants were actively-practicing providers in rural Western Canada who were emailed an invitation for the study and its survey link.

Empowering Patients Through Virtual Care Delivery: Qualitative Study With Micropractice Clinic Patients and Health Care Providers.

Background: Prior to the wider adoption of digital health technologies during the COVID-19 pandemic, applications of virtual care were largely limited to specialist visits and remote care using telehealth (phone or video) applications. Data sharing approaches using tethered patient portals were mostly built around hospitals and larger care systems. These portals offer opportunities for improved communication, but despite a belief that care has improved, they have so far shown few outcome improvements beyond medication adherence.

Effectiveness of Mobile Apps in Promoting Healthy Behavior Changes and Preventing Obesity in Children: Systematic Review.

Background: Mobile apps have been increasingly incorporated into healthy behavior promotion interventions targeting childhood obesity. However, their effectiveness remains unclear.

Objective: This paper aims to conduct a systematic review examining the effectiveness of mobile apps aimed at preventing childhood obesity by promoting health behavior changes in diet, physical activity, or sedentary behavior in children aged 8 to 12 years.

Blood pressure nomograms for children undergoing general anesthesia, stratified by age and anesthetic type, using data from a retrospective cohort at a tertiary pediatric center.

Reference values for non-invasive blood pressure (NIBP) are available for children undergoing general anesthesia, but have not been analyzed by type of anesthetic. This study establishes age-specific pediatric NIBP reference values, stratified by anesthetic type: inhalational anesthesia (IHA), total intravenous anesthesia (TIVA), and mostly intravenous anesthesia (MIVA, an inhalational induction followed by intravenous maintenance of anesthesia). NIBP measurements were extracted from a de-identified vital signs database for children < 19 years undergoing anesthesia between Jan/2013-Dec/2016, excluding cardiac surgery.

Perceptions of Personal Health Records in Rural Primary Health Clinics in Canada - Patient Perspectives.

Personal health records are increasingly being deployed in healthcare settings. In this study we explored patients' perceptions of personal health records in a rural community in Canada where a primary health network is being deployed. A focus group was held and data were thematically analysed.

Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry.

Objectives: To evaluate the performance of pragmatic imputation approaches when estimating model coefficients using datasets with varying degrees of data missingness.

Design: Performance in predicting observed mortality in a registry dataset was evaluated using simulations of two simple logistic regression models with age-specific criteria for abnormal vital signs (mentation, systolic blood pressure, respiratory rate, WBC count, heart rate, and temperature). Starting with a dataset with complete information, increasing degrees of biased missingness of WBC and mentation were introduced, depending on the values of temperature and systolic blood pressure, respectively.

An Artificial Neural Network-Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry.

Background: In the pediatric intensive care unit (PICU), quantifying illness severity can be guided by risk models to enable timely identification and appropriate intervention. Logistic regression models, including the pediatric index of mortality 2 (PIM-2) and pediatric risk of mortality III (PRISM-III), produce a mortality risk score using data that are routinely available at PICU admission. Artificial neural networks (ANNs) outperform regression models in some medical fields.

Association of dexmedetomidine with recovery room and hospital discharge times: A retrospective cohort analysis.

Background: Dexmedetomidine is a useful anesthetic adjunct, increasingly popular during pediatric surgery and procedural sedation. Its half-life of 2-3 hours might prolong recovery and discharge times when compared with an un-supplemented propofol anesthetic. This may create an additional burden in a busy post-anesthetic care unit (PACU).

Derivation and internal validation of a data-driven prediction model to guide frontline health workers in triaging children under-five in Nairobi, Kenya.

Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking.