Knowledge is not all you need for comfort in use of AI in healthcare.

Objectives: The adoption of artificial intelligence (AI) in healthcare is rapidly expanding, transforming areas such as diagnostics, drug discovery, and patient monitoring. Despite these advances, public perceptions of AI in healthcare, particularly in Canada, remain underexplored. This study investigates the relationship between Canadians' knowledge, comfort, and trust in AI, focusing on key sociodemographic factors like age, gender, education, and income.

Estimation and Conformity Evaluation of Multi-Class Counterfactual Explanations for Chronic Disease Prevention.

Recent advances in Artificial Intelligence (AI) in healthcare are driving research into solutions that can provide personalized guidance. For these solutions to be used as clinical decision support tools, the results provided must be interpretable and consistent with medical knowledge. To this end, this study explores the use of explainable AI to characterize the risk of developing cardiovascular disease in patients diagnosed with chronic obstructive pulmonary disease.

Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records.

This study leverages data from a Canadian database of primary care Electronic Medical Records to develop machine learning models predicting type 2 diabetes mellitus (T2D), prediabetes, or normoglycemia. These models are used as a basis for extracting counterfactual explanations and derive personalized changes in biomarkers to prevent T2D onset, particularly in the still reversible prediabetic state. The models achieve satisfactory performance.

Validation of a Design Architecture to Deliver Health Management and Behavior Change Evidence at Scale.

Forty-four percent of Canadians over the age of 20 have a non-communicable disease (NCD). Millions of Canadians are at risk of developing the complications of NCDs; millions have already experienced those complications. Fortunately, the evidence base for NCD prevention and behavior change is large and growing and digital technologies can deliver them at scale and with high fidelity.

AI Can Improve the Economics of Blindness Prevention in Canada.

Diabetic retinopathy is a leading cause of vision loss in Canada and creates significant economic and social burden on patients. Diabetic retinopathy is largely a preventable complication of diabetes mellitus. Yet, hundreds of thousands of Canadians continue to be at risk and thousands go on to develop vision loss and disability.

Lack of Data Access, but Not Availability, Hinders AI Training for High-Risk Conditions in Ontario.

Advanced disease prediction is an important step toward achieving a proactive healthcare system. New technologies such as artificial intelligence are very promising in their ability to predict the onset of future disease much earlier than has been possible in the past. However, artificial intelligence requires training and training requires data.

Towards a Regulatory Framework for Electronic Medical Record Data Visualization.

Physicians struggle to retrieve data from electronic medical records. We evaluated a digital tool that enhances physician efficiency in retrieving and analyzing patient information for treatment decision-making. Our use case is the care of diabetic patients.

Towards a Regulatory Framework for Electronic Medical Record Interoperability in Canada.

All complex systems are potentially predisposed to failure. Healthcare systems are complex systems that are prone to many errors that can result in dire consequences for patients and healthcare providers. The healthcare system in Canada is under unprecedented strain due to shortages of healthcare providers, provider burnout, inefficient workflows, and a lack of appropriate digital infrastructure.

Towards a Regulatory Framework for Workflow Improvement in Electronic Medical Records.

Physicians have to complete several time-consuming and burnout-inducing tasks in their EMRs for everyday care of patients. Poor workflow design generates increased effort for physicians. In this study, we measure time doctors take to retrieve and review information in the patient chart at the beginning of a visit; one of approximately 12 tasks a doctor must do in the EMR during the visit.

Towards a Unified Framework for Information and Interoperability Governance.

Challenges in health data interoperability have highlighted overall health care system inefficiencies. Many organizations struggle to establish a robust data governance infrastructure to meet the increasing demands of advanced data uses, let alone sharing it with a large number of other organizations. There is a need for health care organizations to adopt information governance frameworks that encapsulates interoperability as a core attribute as this can improve data processing, knowledge translation and participation in the larger health data ecosystem.

Measuring and Managing Healthcare Supply and Demand in Real-Time.

Measuring the supply and demand for access to and wait-times for healthcare is key to managing healthcare services and allocating resources appropriately. Yet, few jurisdictions in distributed, socialized medicine settings have any way to do so. In this paper, we propose the requirements for a jurisdictional patient scheduling system that can measure key metrics, such as supply of and demand for regulated health care professional care, access to and wait times for care, real-time health system utilization and provide the data to compute patient journeys.

A Framework for Implementing Disease Prevention and Behavior Change Evidence at Scale.

The current corpus of evidence-based information for chronic disease prevention and treatment is vast and growing rapidly. Behavior change theories are increasingly more powerful but difficult to operationalize in the current healthcare system. Millions of Canadians are unable to access personalized preventive and behavior change care because our in-person model of care is running at full capacity and is not set up for mass education and behavior change programs.

Association of a Heart Failure Management Incentive in Primary Care With Clinical Outcomes: A Retrospective Cohort Study.

Background: We aim to examine the association between primary care physicians' billing of Q050A, a pay-for-performance heart failure (HF) management incentive fee code, and the composite outcome of mortality, hospitalization, and emergency department visits.

Methods And Results: This population-based cohort study linked administrative health databases in Ontario, Canada, for patients with HF aged >66 years between January 1, 2008, and March 31, 2020. Cases were patients with HF who had a Q050A fee code billed.

Artificial intelligence with temporal features outperforms machine learning in predicting diabetes.

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction.

Multi-Input Multi-Output Dynamic Modelling of Type 2 Diabetes Progression.

Type 2 Diabetes Mellitus (T2D) is a chronic health condition that affects millions of people globally. Early identification of risk can support preventive intervention and therefore slow down disease progression. Risk characterization is also necessary to monitor the mechanisms behind the pathology through the analysis of the interrelationships between the predictors and their time course.

Epidemiology and treatment of heart failure with chronic obstructive pulmonary disease in Canadian primary care.

Aims: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are largely managed in primary care, but their intersection in terms of disease burden, healthcare utilization, and treatment is ill-defined.

Methods And Results: We examined a retrospective cohort including all patients with HF or COPD in the Canadian Primary Care Sentinel Surveillance Network from 2010 to 2018. The population size in 2018 with HF, COPD, and HF with COPD was 15 778, 27 927, and 4768 patients, respectively.

Characterization of Inclination Analysis for Predicting Onset of Heart Failure from Primary Care Electronic Medical Records.

The aim of this study is to characterize the performance of an inclination analysis for predicting the onset of heart failure (HF) from routinely collected clinical biomarkers extracted from primary care electronic medical records. A balanced dataset of 698 patients (with/without HF), including a minimum of five longitudinal measures of nine biomarkers (body mass index, diastolic and systolic blood pressure, fasting glucose, glycated hemoglobin, low-density and high-density lipoproteins, total cholesterol, and triglycerides) is used. The proposed algorithm achieves an accuracy of 0.

A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations.

Despite the growing availability of artificial intelligence models for predicting type 2 diabetes, there is still a lack of personalized approaches to quantify minimum viable changes in biomarkers that may help reduce the individual risk of developing disease. The aim of this article is to develop a new method, based on counterfactual explanations, to generate personalized recommendations to reduce the one-year risk of type 2 diabetes. Ten routinely collected biomarkers extracted from Electronic Medical Records of 2791 patients at low risk and 2791 patients at high risk of type 2 diabetes were analyzed.

High cardiovascular disease risk-associated with the incidence of Type 2 diabetes among prediabetics.

Background: Prediabetes is a risk factor for developing Type 2 diabetes mellitus (T2D). We report on the first cohort study of the association between high cardiovascular diseases (CVD) risk with the incidence of T2D in prediabetics. First, estimate the direct effect of developing T2D on patients with prediabetes who have high CVDs risk; and 2) assess the potential increased risk of developing T2D mediated by statins.

Coordination of Oral Anticoagulant Care at Hospital Discharge (COACHeD): protocol for a pilot randomised controlled trial.

Background: Oral anticoagulants (OACs) are commonly prescribed, have well-documented benefits for important clinical outcomes but have serious harms as well. Rates of OAC-related adverse events including thromboembolic and hemorrhagic events are especially high shortly after hospital discharge. Expert OAC management involving virtual care is a research priority given its potential to reach remote communities in a more feasible, timely, and less costly way than in-person care.

Software Architecture for Automated Assessment of Prescription Writing.

Prescribing skills are a crucial competency in medical practice considering the increasing numbers of medications available and the increasingly complex patients with multiple diseases faced in clinical practice. Medical students need to become proficient in these skills during training, as required by medical licensing colleges. Not only is teaching the fundamentals of safe and cost-effective prescribing to medical students challenging but evaluating their prescribing skills by faculty members is difficult and time consuming.

Improving Shared Decision-Making Using Cognitive Effort-Optimization.

Diabetes Prevention Programs (DPPs) can prevent or delay type 2 diabetes (T2D). However, the participation rates in DPPs have been limited. Many individuals at risk of developing diabetes have difficulties making healthy choices because of the cognitive effort required to understand the risks, the role of biomarkers, the consequences of inaction and the actions required to delay or avoid development of T2D.