Shared decision-making, the working alliance, and patient-centered care: A simultaneous concept analysis and review of the literature.

Objective: To identify the conceptual similarities, differences, and interrelationships between shared decision-making (SDM), the working alliance, and patient-centered care (PCC) in primary care.

Methods: This study is a simultaneous concept analysis based on the method of Walker and Avant (2005) and Haase et al. (1992).

Canadian Adaptive Platform Trial of Treatments for COVID in Community Settings (CanTreatCOVID): protocol for a randomised controlled adaptive platform trial of treatments for acute SARS-CoV-2 infection in community settings.

Introduction: SARS-CoV-2 is now endemic and expected to remain a health threat, with new variants continuing to emerge and the potential for vaccines to become less effective. While effective vaccines and natural immunity have significantly reduced hospitalisations and the need for critical care, outpatient treatment options remain limited, and real-world evidence on their clinical and cost-effectiveness is lacking. In this paper, we present the design of the Canadian Adaptive Platform Trial of Treatments for COVID in Community Settings (CanTreatCOVID).

Unveiling the Impact: A Scoping Review of the COVID-19 Pandemic's Effects on Racialized Populations in Canada.

Objectives: The objective of this study was to examine the impact of the COVID-19 pandemic on racialized communities and individuals in Canada.

Methods: This review followed the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR guidance on reporting scoping reviews. Ovid MEDLINE ALL, Embase Classic + Embase, CINAHL (Ebsco platform), PsycINFO, and Cochrane were searched for documents that were published after March 2020 and that reported on the social and economic impacts and health outcomes of the COVID-19 pandemic on generally healthy racialized populations that reside in Canada.

A Multi-Site, Randomized, Parallel-Group, Controlled Trial of Virtually-Delivered Sahaj Samadhi Meditation for the Management of Moderate Depressive Symptoms in Chronic Pain.

Background: Chronic pain (CP) often co-occurs with depression, but promising scalable interventions have been under-investigated. We assessed the effectiveness of the virtually-delivered Sahaj Samadhi Meditation (SSM) program in reducing depressive symptoms in people with CP and moderate depressive symptoms.

Methods: We conducted a randomized controlled trial comparing SSM to the Health Enhancement Program (HEP), an active control.

Machine learning used to study risk factors for chronic diseases: A scoping review.

Objectives: Machine learning (ML) has received significant attention for its potential to process and learn from vast amounts of data. Our aim was to perform a scoping review to identify studies that used ML to study risk factors for chronic diseases at a population level, notably those that incorporated methods to mitigate algorithmic bias. We focused on ML applications for the most common risk factors for chronic disease: tobacco use, alcohol use, unhealthy eating, physical activity, and psychological stress.

Scientific production in primary health care in Latin American and Caribbean Countries (1980-2024): A web of science perspective.

Objectives: To analyze the scientific production of primary care research in Latin American and Caribbean (LAC) countries from 1980 to 2024 and to provide recommendations for improvement.

Design: Observational, machine learning-based bibliometric study.

Data Sources: Review and research articles indexed in the Web of Science database.

Monitoring patient pathways at a secondary healthcare services through process mining via Fuzzy Miner.

Background: This study explored workflow pathways followed by patients seeking secondary healthcare services at a local hospital in a rural part of Turkey using process mining to improve hospital resource management.

Methods: The study used process mining to discover process flows as patient pathways implied by hospital records for in-patient, out-patient, biochemical laboratory, and radiology services. Utilizing its flexibility, visualizations and robustness, authors implemented fuzzy-miner algorithm.

Virtual care in community pharmacy services: a scoping review.

Background: The integration of virtual care has been essential for maintaining continuity of patient care during and after the COVID-19 pandemic. Community pharmacists were among the healthcare professionals who used virtual care to provide remote pharmacy services. However, the use of virtual care in community pharmacy has not been comprehensively reviewed.

Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.

In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. This study, as one of the most detailed investigations into cancer research in primary care, comprehensively evaluates cancer studies from the perspective of PHC using bibliometric techniques and machine learning. The dataset for the analyses was sourced from the Web of Science (WoS) Core Collection database on March 20, 2024.

Leveraging Artificial Intelligence to Inform Care Coordination by Identifying and Intervening in Patients' Unmet Social Needs: A Scoping Review.

Aim: We reviewed how artificial intelligence has been applied to inform care coordination by identifying and/or intervening in patients' unmet social needs.

Design: Scoping review.

Data Sources: PubMed, CINAHL, PsycInfo, and Scopus databases were searched for articles published by November 2023.

Perspectives on Using Artificial Intelligence to Derive Social Determinants of Health Data From Medical Records in Canada: Large Multijurisdictional Qualitative Study.

Background: Data on the social determinants of health could be used to improve care, support quality improvement initiatives, and track progress toward health equity. However, this data collection is not widespread. Artificial intelligence (AI), specifically natural language processing and machine learning, could be used to derive social determinants of health data from electronic medical records.

Addressing the challenges and constraints of social protection policies for Peruvian women domestic workers: the ANITA project study protocol.

Introduction: Domestic workers (DWs) are vulnerable to precarious or informal working conditions with limited access to social protection policies such as employer-paid health insurance or retirement pensions. This study aims to examine the working conditions, health status and access to healthcare for women DWs in Peru and propose recommendations to improve their access to social protection policies.

Methods And Analysis: The project uses a participatory action research approach by engaging three committees: a DW co-researcher committee, an advisory committee and a steering committee.

Primary care in the COVID-19 pandemic and beyond: Lessons from Ontario.

Objective: To understand the role of primary care in the COVID-19 pandemic to provide insight into its functioning and inform potential reforms.

Composition Of The Committee: The now dissolved Ontario COVID-19 Science Advisory Table (Science Table) was formed in July 2020 to provide decision makers and the public with a synthesis of rapidly evolving evidence related to COVID-19. The Science Table was based at the Dalla Lana School of Public Health at the University of Toronto, and supported by Public Health Ontario.

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, forming a significant area of service in Primary Health Care (PHC), which healthcare professionals often confront. It serves as a precursor to many critical illnesses and can lead to fatalities if not addressed promptly.

Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.

Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence.

Background: Early warning systems (EWSs) are tools that integrate clinical observations to identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and appropriate interventions. EWSs can mitigate the impact of global infectious diseases by enhancing information exchange, monitoring, and early detection.

Objective: We aimed to evaluate the effectiveness of EWSs in acute respiratory infections (ARIs) through a scoping review of EWSs developed, described, and implemented for detecting novel, exotic, and re-emerging ARIs.

Using Primary Health Care Electronic Medical Records to Predict Hospitalizations, Emergency Department Visits, and Mortality: A Systematic Review.

Introduction: High-quality primary care can reduce avoidable emergency department visits and emergency hospitalizations. The availability of electronic medical record (EMR) data and capacities for data storage and processing have created opportunities for predictive analytics. This systematic review examines studies which predict emergency department visits, hospitalizations, and mortality using EMR data from primary care.

Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines.

Background: Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities.

Changes in Primary Care Health Services During the COVID-19 Pandemic: A Longitudinal Analysis of Data From Ontario.

The COVID-19 pandemic significantly impacted primary care, but its effect on quality of care is not well understood. We used health administrative data to understand the changes in quality-of-care measures for primary care between October 2018 and April 2022. We examined the following domains: cancer screening, chronic disease (diabetes) management, high-risk prescribing, continuity of care and capacity of primary care services.

Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.

Background: The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care.

Objective: This scoping review aims to analyze studies published since 2019, focusing on ML algorithms, their applications, and predictive performances.

Methods: We searched Ovid MEDLINE ALL and Embase on Ovid, the Cochrane Library (Wiley), CINAHL (EBSCO), and Web of Science (core collection).

Developing an AI Tool to Derive Social Determinants of Health for Primary Care Patients: Qualitative Findings From a Codesign Workshop.

Purpose: Information about social determinants of health (SDOH) is essential for primary care clinicians in the delivery of equitable, comprehensive care, as well as for program planning and resource allocation. SDOH are rarely captured consistently in clinical settings, however. Artificial intelligence (AI) could potentially fill these data gaps, but it needs to be designed collaboratively and thoughtfully.