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Background: Diabetes mellitus is a complex disease with serious complications. Electronic decision support, providing information that is shared and discussed by both patient and physician, encourages timely interventions and may improve the management of this chronic disease. However, it has rarely been tested in community-based primary care.Methods: In this pragmatic randomized trial, we randomly assigned adult primary care patients with type 2 diabetes to receive the intervention or usual care. The intervention involved shared access by the primary care provider and the patient to a Web-based, colour-coded diabetes tracker, which provided sequential monitoring values for 13 diabetes risk factors, their respective targets and brief, prioritized messages of advice. The primary outcome measure was a process composite score. Secondary outcomes included clinical composite scores, quality of life, continuity of care and usability. The outcome assessors were blinded to each patient's intervention status.Results: We recruited sequentially 46 primary care providers and then 511 of their patients (mean age 60.7 [standard deviation 12.5] years). Mean follow-up was 5.9 months. The process composite score was significantly better for patients in the intervention group than for control patients (difference 1.27, 95% confidence interval [CI] 0.79-1.75, p < 0.001); 61.7% (156/253) of patients in the intervention group, compared with 42.6% (110/258) of control patients, showed improvement (difference 19.1%, p < 0.001). The clinical composite score also had significantly more variables with improvement for the intervention group (0.59, 95% CI 0.09-1.10, p = 0.02), including significantly greater declines in blood pressure (-3.95 mm Hg systolic and -2.38 mm Hg diastolic) and glycated hemoglobin (-0.2%). Patients in the intervention group reported greater satisfaction with their diabetes care.Interpretation: A shared electronic decision-support system to support the primary care of diabetes improved the process of care and some clinical markers of the quality of diabetes care. (ClinicalTrials.gov trial register no. NCT00813085.). [ABSTRACT FROM AUTHOR]

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. Furthermore, feature importance analysis underscores the significance of fasting blood sugar and glycated hemoglobin, while counterfactuals explanations emphasize the centrality of keeping body mass index and cholesterol indicators within or close to the clinically desirable ranges. This research highlights the potential of machine learning and counterfactual explanations in guiding preventive interventions that may help slow down the progression from prediabetes to T2D on an individual basis, eventually fostering a recovery from prediabetes to a normoglycemic state.

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. To establish interoperability between healthcare organizations, standards must exist in relation to how information is governed and circulates in the healthcare system, not just on how it is structured, stored and used within an organization. In this paper we demonstrate that interoperability between organizations cannot coherently exist without consideration of information governance within organizations. Lack of coherence can lead to lack of data accessibility, decreased organizational efficiencies, and poor data quality. With this in mind, we propose a unified framework that integrates the principles of both information and interoperability governance to increase the adaptability, flexibility, and efficiency of health information usage across the entire healthcare system.

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. We propose a framework to utilize data from electronic medical records to identify patients at risk of developing chronic disease and reach out to them using digital health tools that are overseen by the primary care team. The framework leverages emerging technologies such as artificial intelligence, digital health tools, and patient-generated data to deliver evidence-based knowledge and behavior change to patients across Canada at scale. The framework is flexible to enable new technologies to be added without overwhelming providers, patients or implementers.

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. Evaluation results showed that healthcare providers who used the i4C (Insights for Care) dashboard experienced greater time efficiency than those who used traditional EMR information retrieval methods. A comprehensive evaluation of the i4C Dashboard confirms its effectiveness in facilitating diabetic care data management, as well as its potential application to a wide range of healthcare scenarios. In order to further maximize its effectiveness on clinical efficiency and patient care, future research should focus on improving its usability and scalability.

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. We used failure mode and effects analysis (FMEA) to identify the failure modes for care provided in primary care settings. We identified failure modes in appointment scheduling, patient-provider communications, referrals, laboratory and diagnostic procedures, and medication prescriptions as the main failure modes. To mitigate the detected risks, we recommend solutions to 'close the loop' on failure modes to prevent patients from falling through the cracks, as vulnerable patients who cannot advocate for themselves are most likely to do so. We provide preliminary requirements for a regulatory regime for electronic health records that can reduce provider burnout, improve regulatory compliance, and improve system efficiency, all while improving patient safety, experience, and outcomes.

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. Information retrieval takes approximately 40 minutes per day. Automation could save 75% of that time. We estimate that if every family doctor in Canada could save 30 minutes through automation of just this one process, we could free up time equivalent to >3000 physicians and >5 million patients; enough to absorb the vast majority of patients who currently do not have a doctor. We know of no more powerful intervention than workflow automation in Canadian EMRs to increase the supply of doctors while simultaneously reducing a major cause of burnout. We recommend an accelerated research program to identify additional opportunities for workflow automation and a regulatory program to ensure that every physician has access to workflow automation in their EMR.

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. However, the current model of in-person primary care is not designed nor capable of operationalizing that evidence. New developments in artificial intelligence that can predict who will develop NCD or the complications of NCD are increasingly available, making the challenge of delivering disease prevention even more urgent. This paper presents findings from stakeholder engagement on a design architecture to address three initial barriers to large-scale deployment of health management and behavior change evidence: 1) the challenges of regulating mobile health apps, 2) the challenge of creating a value-based rationale for payers to invest in deploying mobile health apps at scale, and 3) the high cost of customer acquisition for delivering mobile health apps to those at risk.

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. Blindness has a significant impact on the Canadian economy, on families and the quality of life of affected individuals. This paper provides an economic analysis on two potential interventions for preventing blindness and concludes that use of AI to identify high-risk individuals could significantly decrease the costs of identifying, recalling, and screening patients at risk of vision loss, while achieving similar results as a full-fledged screening and recall program. We propose that minimal data interoperability between optometrists and family physicians combined with artificial intelligence to identify and screen those at highest risk of vision loss can lower the costs and increase the feasibility of screening and treating large numbers of patients at risk of going blind in Canada.

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. The system is also capable of tracking new supply of providers and who does not have access to a primary care provider. Benefits, limitations and risks of the model are discussed.

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. In this study, we report on the ready availability, but lack of accessibility and real-time access to healthcare data required to treat five high-cost diseases that are predictable using AI and preventable using well-established evidence-based therapies. There is urgent need for action on the part of governments and other interest holders to define and invest in the infrastructure required to make data for training and deploying AI at scale more accessible.

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. Cases and controls were matched 1:1 on age, sex, patient status on being rostered to a primary care physician, cardiologist, or internist visit in the 6 months before study enrollment, Johns Hopkins Adjusted Clinical Group resource use bands, days between HF diagnosis and study enrollment (±2 years), and the logit of the propensity score. A Cox proportional hazards model assessed the association of Q050A with the outcome. A total of 59 664 cases had a Q050A billed, whereas 244 883 patients did not. Before matching, patients who had a Q050A billed were more likely to be men (52% versus 49%), were rostered to a primary care physician (100% versus 96%), had a higher Charlson Comorbidity Index, and had higher health care costs. The mean follow-up was 481 days for cases and 530 days for controls. The composite outcome (hazard ratio, 1.11 [95% CI, 1.09-1.12]) was significantly higher for cases than controls., Conclusions: The Q050A incentive improved financial compensation for primary care physicians managing patients with HF but was not associated with improvements in the outcome. Research on promoting evidence-based HF management is warranted.

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. While disease incidence declined, age-sex-standardized prevalence per 100 population increased for HF alone from 2.33 to 3.63, COPD alone from 3.44 to 5.96, and COPD with HF from 12.70 to 15.67. Annual visit rates were high and stable around 8 for COPD alone but declined significantly over time for HF alone (9.3-8.1, P = 0.04) or for patients with both conditions (14.3-11.9, P = 0.006). For HF alone, cardiovascular visits were common (29.4%), while respiratory visits were infrequent (3.5%), with the majority of visits being non-cardiorespiratory. For COPD alone, respiratory and cardiovascular visits were common (16.4% and 11.3%) and the majority were again non-cardiorespiratory. For concurrent disease, 39.0% of visits were cardiorespiratory. The commonest non-cardiorespiratory visit reasons were non-specific symptoms or signs, endocrine, musculoskeletal, and mental health. In patients with HF with and without COPD, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor-neprilysin inhibitor use was similar, while mineralocorticoid receptor antagonist use was marginally higher with concurrent COPD. Beta-blocker use was initially lower with concurrent COPD compared with HF alone (69.3% vs. 74.0%), but this progressively declined by 2018 (74.5% vs. 73.5%)., Conclusions: The prevalence of HF and COPD continues to rise. Although patients with either or both conditions are high utilizers of primary care, the majority of visits relate to non-cardiorespiratory comorbidities. Medical therapy for HF was similar and the initially lower beta-blocker utilization disappeared over time., (© 2023 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

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. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Naveed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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. In this work, a multi-input multi-output Gaussian Process model is proposed to describe the evolution of different biomarkers in patients who will/will not develop T2D considering the interdependencies between outputs. The preliminary results obtained suggest that the trends in biomarkers captured by the model are coherent with the literature and with real-world data, demonstrating the value of multi-input multi-output approaches. In future developments, the proposed method could be applied to assess how the biomarkers evolve and interact with each other in groups of patients having in common one or more risk factors.

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 0.89 (sensitivity of 0.89, specificity of 0.90) to predict the inclination of biomarkers (i.e., their trend towards a 'survival' or 'collapse' as defined by an inclination analysis) on a labeled, balanced dataset of 40 patients. Decision trees trained on the predicted inclination of biomarkers have significantly higher recall (0.69 vs. 0.53) and significantly higher negative predictive value (0.60 vs. 0.55) than those trained on the average values computed from the measures of biomarkers available before the onset of the disease, suggesting that an inclination analysis can help identify the onset of HF in the primary care patient population from routinely available clinical data. This exploratory study provides the basis for further investigations of inclination analyses to identify at-risk patients and generate preventive measures (i.e., personalized recommendations to reverse the trend of biomarkers towards collapse).

Competing Interests: Intérêts concurrents: James Stone déclare avoir reçu des honoraires et émoluments à titre de consultant pour Bayer Canada et Sanofi Canada. Le Dr Stone est directeur médical principal du Cardiovascular Health and Stroke Strategic Clinical Network des Services de santé de l’Alberta, et membre actif du conseil de gouvernance du Collège des médecins et chirurgiens de l’Alberta. Jason Andrade déclare avoir reçu une rémunération ou des honoraires de Medtronic, BiosenseWebster, Abbott, Biotronik, Bayer, Servier et BMS-Pfizer. Simon Bacon déclare avoir reçu des subventions de Moderna, versées à Respiplus pour la création de modules de formation sur la réticence aux vaccins et des honoraires de consultant et de conférencier de Respiplus. Il a fait partie de comités consultatifs pour Bayer, Sanofi, Respiplus et Lucilab, aucun n’étant en lien avec le présent article. Il est aussi titulaire d’une chaire en mentorat pour la Stratégie de recherche axée sur le patient des Instituts de recherche en santé du Canada (IRSC) (SMC-151518), et d’une chaire du Fonds de recherche du Québec (251618 et 309815). Harpreet Bajaj déclare avoir reçu des subventions de Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Gilead, Janssen, Kowa Pharmaceuticals, Merck, Novo Nordisk, Pfizer, Sanofi et Tricida, et des honoraires de conférencier de Eli Lilly et Novo Nordisk. Mark Gelfer déclare avoir reçu des honoraires de consultation de Cliniques Telus Santé et occupe un poste bénévole de président du conseil de GenXys Health Care Systems, dont il détient des actions. Jeffrey Habert déclare avoir reçu des honoraires de consultation de MDBriefcase, Liv, MedPlan, Master Clinician Alliance, Academy, Bridge, Seacourses, Thrombose Canada, Meducom, Antibody, Canadian Heart Research Centre, CTC Communications, STA Healthcare Communications, Canadian Collaborative Research Network, CPD Network, Telus Santé, EOCI Pharmacomm et Operatic. Le Dr Habert a aussi reçu des honoraires de Pfizer, Amgen, BMS, Bayer, Boehringer Ingelheim, Eli Lilly, GlaxoSmithKline, Purdue, Bausch, Allergan, AbbVie, AstraZeneca, Novartis, Lundbeck, CHEP+, Novo Nordisk, Janssen, Eisai, HLS, Otsuka, Sunovion et Jazz. Le Dr Habert a été coresponsable des Guides cliniques de Thrombose Canada (2018–2021). Karim Keshavjee déclare avoir reçu des honoraires de consultation de l’Université McMaster, Demers Professional Corporation et Normative. Darlene Kitty déclare avoir reçu une rémunération d’enseignante des facultés de médecine de l’Université d’Ottawa et de l’Université McGill pour des conférences occasionnelles et des bourses de voyage à titre de directrice du Programme autochtone de la Faculté de médecine de l’Université d’Ottawa. La Dre Kitty est présidente du Comité sur la santé autochtone du Collège des médecins de famille du Canada. David Lau déclare avoir reçu des subventions de Novo Nordisk, des honoraires de consultation de Amgen, Bayer, Boehringer Ingelheim, CME at Sea, HLS Therapeutics, Eli Lilly, Novo Nordisk et Viatris, des honoraires de Amgen, Bayer, Boehringer Ingelheim, CME at Sea, HLS Therapeutics, Eli Lilly et Novo Nordisk, et une rémunération en tant que témoin expert de l’Association canadienne de protection médicale. Le Dr Lau a occupé des postes de direction chez Obésité Canada et à l’Association canadienne des médecins et chirurgiens bariatriques. Laurent Macle déclare avoir reçu des subventions et des honoraires de BMS-Pfizer, Servier et Bayer. Michael McDonald déclare avoir reçu des honoraires de consultation de Boehringer Ingelheim, et des honoraires de Novartis et Servier. Le Dr McDonald a aussi fait partie du comité de surveillance de la sécurité des données pour l’essai IVVE. Kara Nerenberg déclare avoir fait partie des comités pour les lignes directrices de pratique clinique de la Fondation canadienne des maladies du cœur et de l’AVC, Obésité Canada, Hypertension Canada, Diabète Canada et la Société des obstétriciens et gynécologues du Canada. Glen Pearson déclare avoir reçu des honoraires de consultation de Novartis Canada en tant que participant à des comités consultatifs. Alexandre Poppe déclare avoir reçu une subvention de recherche de Stryker et des honoraires de Pfizer-BMS. Le Dr Poppe a aussi fait partie du comité de surveillance de la sécurité des données pour l’essai FLOW sur la fluoxétine après l’AVC et du comité de surveillance de l’innocuité pour l’essai AcT (Alteplase Compared to Tenecteplase in Patients With Acute Ischemic Stroke) sur le ténectéplase dans l’AVC aigu. Le Dr Poppe a reçu le soutien d’une bourse du programme de recherche de Servier. Diana Sherifali déclare avoir reçu un financement personnel par l’entremise de la chaire de recherche médicale interprofessionnelle de l’Institut de santé publique Heather M. Arthur et Hamilton Health Sciences. Peter Selby déclare avoir reçu des subventions du ministère ontarien de la Santé et de Pfizer (Bourses de recherche mondiale sur la dépendance à la nicotine). Il fait aussi partie du comité de rédaction C-CHANGE depuis 2010. Eric Smith déclare avoir reçu des honoraires de consultation de Bayer, Biogen, Alnylam et Cyclerion. Sol Stern déclare avoir reçu des émoluments et des honoraires de consultation de MDBriefcase.com et Sea Courses inc. George Thanassoulis déclare avoir reçu des subventions, des émoluments et des honoraires de consultation de Amgen, Sanofi, Novartis et Silence. Le Dr Thanassoulis a aussi été membre du Comité de rédaction de la Ligne directrice sur la dyslipidémie de la SCC. Kristin Terenzi déclare avoir reçu des honoraires de consultation pour des comités consultatifs, des honoraires de conférencière et un soutien pour sa participation à des réunions de AstraZeneca, Boehringer Ingelheim, Astellas, Allergan, Amgen, Aspen, Bayer, Lilly, Lundbeck, AbbVie, Aralez, GSK, Merck, Novo Nordisk, Pfizer et Novartis. Karen Tu déclare avoir reçu des subventions (versées à son établissement) des institutions suivantes: IRSC, Fondation de l’Hôpital St. Michael, Collège des médecins de famille du Canada, Fondation pour l’avancement de la médecine familiale, Fondation AMC, Fonds d’exploration de l’Hôpital général de North York, Fondations canadienne et ontarienne des maladies du cœur et de l’AVC, ministère de la Défense des États-Unis, Université de Toronto — Département de médecine familiale et communautaire, MaRS Innovation Fund, Fondation canadienne de dermatologie, Société canadienne de rhumatologie (Canadian Initiative for Outcomes in Rheumatology Care), PSI Foundation, Action Cancer Ontario, Toronto Rehab Institute Chair Fund, UTOPIAN, Société d’arthrite, Société canadienne de la SP, Réseau canadien vasculaire, Prix IMPACT ciblé de l’unité de soutien SPOR de l’Ontario et Fondation Rathlyn. La Dre Tu détient une bourse en tant que chercheuse au Département de médecine familiale et communautaire de l’Université de Toronto. Jacob Udell déclare avoir reçu des subventions de Boehringer Ingelheim, Janssen, Sanofi et Novartis, et des honoraires de AstraZeneca, GlaxoSmithKline et Sanofi. Le Dr Udell a aussi fait partie de comités consultatifs pour Boehringer Ingelheim, Sanofi et Novartis. Sean Virani déclare avoir reçu des subventions de Abbott, Amgen, AstraZeneca, Boehringer Ingelheim, Bayer, Novartis, Servier, Otsuka et Medtronic, des honoraires de consultation de Abbott, Amgen, AstraZeneca, Boehringer Ingelheim, Bayer, Novartis, Seriver et Otsuka, et des honoraires de Abbott, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bayer, Novartis, Servier, Otsuka et Medtronic. Le Dr Virani a aussi fait partie de comités de surveillance de la sécurité des données ou de comités consultatifs pour Abbott et Medtronic. Richard Ward déclare avoir reçu des honoraires de consultation de Eisai et Lilly-Boehringer Ingelheim, et une rémunération ou des honoraires de Boehringer Ingelheim, AstraZeneca, Janssen, Amgen, Lilly, Pfizer et Takeda. Le Dr Ward agit aussi à titre de directeur de l’Association médicale albertaine. Sean Wharton déclare avoir reçu des subventions et des honoraires de Novo Nordisk, Eli Lilly, Bausch Health Canada et Boehringer Ingelheim; il a fait partie comités de surveillance de la sécurité des données pour Novo Nordisk, Eli Lilly, Bausch Health Canada et Boehringer Ingelheim; et il a bénéficié de matériel et de services, de rédaction ou autres, de Novo Nordisk, Eli Lilly et Bausch Health Canada. Le Dr Wharton a aussi occupé un poste de responsabilité auprès de l’Obesity Society et d’Obésité Canada. Jennifer Zymantas déclare avoir reçu des honoraires de Pfizer comme modératrice lors de conférences portant sur l’anticoagulation. Diane Hua-Stewart occupe le poste de directrice du programme CHEP+. Peter Liu déclare avoir reçu des subventions de Génome Canada, de la Fondation des maladies du cœur et de l’AVC et des IRSC. Le Dr Liu détient un brevet pour l’IGFBP7, un biomarqueur de l’insuffisance cardiaque et il est directeur scientifique de l’Institut de cardiologie de l’Université d’Ottawa. Sheldon Tobe déclare avoir reçu une subvention et un soutien non matériel pour l’étude Zero to Five, remis à l’Institut de recherche Sunnybrook, des honoraires de consultation de AstraZeneca, et une rémunération pour le programme éducatif CHEP+ de Amgen, AstraZeneca, BMS, Bayer, Boehringer Ingelheim, Janssen, Lilly, Novartis, Novo Nordisk, Pfizer et Sanofi Genzyme. Le Dr Tobe agit aussi bénévolement auprès de l’American Hypertension Specialists Certification Program. Aucun autre intérêt n’a été déclaré.

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., Methods: We conducted a population-based cohort study using a subset of data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2000 to 2015. Cox proportional hazards (PH) regressions were conducted to estimate our primary outcome, which is the time to T2D among patients with prediabetes., Results: From the 4995 filtered prediabetic participants identified between 2000 and 2015, 2800 participants were diagnosed with high CVDs risk scores as measured by the Framingham risk score. 2195 participants were non-high CVDs risk controls. The covariate-adjusted hazard ratio (HR) of 1.24 [95% confidence interval (CI), 1.10-1.31] for T2D by CVDs risk among prediabetics was observed. The total effect of CVDs risk on developing T2D was decomposed to a natural direct effect of high CVDs risk HR= 1.18 [95% CI, 1.01-1.48] and an indirect effect through statin therapy of HR= 1.06 [95% CI, 0.97-1.30]., Conclusion: Patients with prediabetes and high CVDs risk had a 24% higher chance of developing T2D. The high CVDs risk effect was mediated by statin therapy. Regular monitoring and counselling of prediabetics using statins is likely warranted to prevent the incidence of T2D., Competing Interests: Declaration of Competing Interests The authors declare that they have no conflict of Interest., (Copyright © 2022. Published by Elsevier B.V.)

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. Two regions characterizing the two classes of patients were estimated using a Support Vector Data Description classifier. Counterfactual explanations (i.e., minimal changes in input features able to change the risk class) were generated for patients at high risk and evaluated using performance metrics (availability, validity, actionability, similarity, and discriminative power) and a qualitative survey administered to seven expert clinicians. Results showed that, on average, the requested minimum viable changes implied a significant reduction of fasting blood sugar, systolic blood pressure, and triglycerides and a significant increase of high-density lipoprotein in patients at risk of diabetes. A significant reduction in body mass index was also recommended in most of the patients at risk, except in females without hypertension. In general, greater changes were recommended in hypertensive patients compared to non-hypertensive ones. The experts were overall satisfied with the proposed approach although in some cases the proposed recommendations were deemed insufficient to reduce the risk in a clinically meaningful way. Future research will focus on a larger set of biomarkers and different comorbidities, also incorporating clinical guidelines whenever possible. Development of additional mathematical and clinical validation approaches will also be of paramount importance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Lenatti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Competing Interests: Competing interests: James Stone reports receiving consulting fees and honoraria from Bayer Canada and Sanofi Canada. Dr. Stone is senior medical director, Cardiovascular Health and Stroke Strategic Clinical Network, Alberta Health Services; and member at large, Governing Council, College of Physicians and Surgeons of Alberta. Jason Andrade reports receiving payment or honoraria from Medtronic, Biosense-Webster, Abbott, Bio-tronik, Bayer, Servier and BMS-Pfizer. Simon Bacon reports receiving grants from Moderna, paid through Respiplus for the development of vaccine hesitancy education modules, and consultancy and speaker fees from Respiplus. Dr. Bacon has served on advisory boards for Bayer, Sanofi, Respiplus and Lucilab, none of which are related to the current article. Dr. Bacon also holds a Canadian Institutes of Health Research (CIHR) Strategy for Patient-Oriented Research Mentoring Chair (SMC-151518) and Fonds de recherche du Québec Chair (251618 and 309815). Harpreet Bajaj reports receiving grants from Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Gilead, Janssen, Kowa Pharmaceuticals, Merck, Novo Nordisk, Pfizer, Sanofi and Tricida; and honoraria for lectures from Eli Lilly and Novo Nordisk. Mark Gelfer reports receiving consulting fees from Telus Health Care Centres and holding an unpaid role as board chair of GenXys Health Care Systems, in which Dr. Gelfer also holds stock options. Jeffrey Habert reports receiving consulting fees from MDBriefcase, Liv, MedPlan, Master Clinician Alliance, Academy, Bridge, Seacourses, Thrombosis Canada, Meducom, Antibody, the Canadian Heart Research Centre, CTC Communications, STA Healthcare Communications, Canadian Collaborative Research Network, CPD Network, Telus Health, EOCI Pharmacomm and Operatic. Dr. Habert has also received honoraria from Pfizer, Amgen, BMS, Bayer, Boehringer Ingelheim, Eli Lilly, GlaxoSmithKline, Purdue, Bausch, Allergan, AbbVie, AstraZeneca, Novartis, Lundbeck, Canadian Cardiovascular Health Education Program (CHEP+), Novo Nordisk, Janssen, Eisai, HLS, Otsuka, Sunovion and Jazz. Dr. Habert was the previous co-chair of the Thrombosis Canada Clinical Guides (2018–2021). Karim Keshavjee reports receiving consulting fees from McMaster University, Demers Professional Corporation and Normative. Darlene Kitty reports receiving teaching stipends from the University of Ottawa Faculty of Medicine and the McGill University Faculty of Medicine for occasional lectures, and travel support as director for the Indigenous Program of the University of Ottawa Faculty of Medicine. Dr. Kitty is the chair of the Indigenous Health Committee, College of Family Physicians of Canada. David Lau reports receiving grants from Novo Nordisk; consulting fees from Amgen, Bayer, Boehringer Ingelheim, CME at Sea, HLS Therapeutics, Eli Lilly, Novo Nordisk and Viatris; honoraria from Amgen, Bayer, Boehringer Ingelheim, CME at Sea, HLS Therapeutics, Eli Lilly and Novo Nordisk; and payment for expert testimony from the Canadian Medical Protective Association. Dr. Lau has served in leadership roles with Obesity Canada and the Canadian Association of Bariatric Physicians and Surgeons. Laurent Macle reports receiving grants and honoraria from BMS-Pfizer, Servier and Bayer. Michael McDonald reports receiving consulting fees from Boehringer Ingelheim, and honoraria from Novartis and Servier. Dr. McDonald also participated in the data safety monitoring board for the IVVE Trial. Kara Nerenberg reports membership with the clinical practice guideline committees for the Heart and Stroke Foundation, Obesity Canada, Hypertension Canada, Diabetes Canada and the Society of Obstetricians and Gynaecologists of Canada. Glen Pearson reports receiving consulting fees from Novartis Canada as an advisory board participant. Alexandre Poppe reports receiving a research grant from Stryker and an honorarium from Pfizer-BMS. Dr. Poppe has also participated on a data safety monitoring board for the FLOW trial of fluoxetine after stroke and the safety committee for the Alteplase Compared to Tenecteplase in Patients With Acute Ischemic Stroke (AcT trial) of tenecteplase for acute stroke. Dr. Poppe has received fellowship program support from Servier. Diana Sherifali reports receiving personal funding through her Heather M. Arthur Population Health Institute/Hamilton Health Sciences Chair in Interprofessional Health Research. Peter Selby reports receiving grants from the Ontario Ministry of Health and Pfizer (Global Research Awards for Nicotine Dependence). Dr. Selby has also participated on the C-CHANGE guideline panel since 2010. Eric Smith reports receiving consulting fees from Bayer, Biogen, Alnylam and Cyclerion. Sol Stern reports receiving consulting fees and honoraria from MDBriefcase.com and Sea Courses Inc. George Thanassoulis reports receiving grants, consulting fees and honoraria from Amgen, Sanofi, Novartis and Silence. Dr. Thanassoulis has also been a member of the CCS Dyslipidemia Guideline committee. Kristin Terenzi reports receiving consulting fees for advisory boards of, honoraria for speaking events for, and support for attending meetings from AstraZeneca, Boehringer Ingelheim, Astellas, Allergan, Amgen, Aspen, Bayer, Lilly, Lundbeck, AbbVie, Aralez, GSK, Merck, Novo Nordisk, Pfizer and Novartis. Karen Tu reports receiving grants (payments to institution) from CIHR, St. Michael’s Hospital Foundation, The College of Family Physicians of Canada, Foundation for Advancing Family Medicine, Canadian Medical Association Foundation, North York General Hospital Exploration Fund, Heart and Stroke Foundation of Canada, Heart and Stroke Foundation of Ontario, United States Department of Defense, University of Toronto — Department of Family and Community Medicine, MaRS Innovation Fund, Canadian Dermatology Foundation, Canadian Rheumatology Association (Canadian Initiative for Outcomes in Rheumatology Care), PSI Foundation, Cancer Care Ontario, Toronto Rehab Institute Chair Fund, UTOPIAN, Arthritis Society, MS Society of Canada, The Canadian Vascular Network, Ontario SPOR Support Unit Targeted IMPACT Award and Rathlyn Foundation. Dr. Tu holds a Research Scholar Award from the Department of Family and Community Medicine at the University of Toronto. Jacob Udell reports receiving grants from Boehringer Ingelheim, Janssen, Sanofi and Novartis; and honoraria from AstraZeneca, GlaxoSmithKline and Sanofi. Dr. Udell has also participated on advisory boards for Boehringer Ingelheim, Sanofi and Novartis. Sean Virani reports receiving grants from Abbott, Amgen, AstraZeneca, Boehringer Ingelheim, Bayer, Novartis, Servier, Otsuka and Medtronic; consulting fees from Abbott, Amgen, AstraZeneca, Boehringer Ingelheim, Bayer, Novartis, Seriver and Otsuka; and honoraria from Abbott, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bayer, Novartis, Servier, Otsuka and Medtronic. Dr. Virani has also participated in data safety monitoring boards or advisory boards for Abott and Medtronic. Richard Ward reports receiving consulting fees from Eisai and Lilly-Boehringer Ingelheim, and payment or honoraria from Boehringer Ingelheim, AstraZeneca, Janssen, Amgen, Lilly, Pfizer and Takeda. Dr. Ward also serves as director of the Alberta Medical Association. Sean Wharton reports receiving grants and honoraria from Novo Nordisk, Eli Lilly, Bausch Health Canada and Boehringer Ingelheim; has participated in data safety monitoring boards for Novo Nordisk, Eli Lilly, Bausch Health Canada and Boehringer Ingelheim; and has received equipment, medical writing or other services from Novo Nordisk, Eli Lilly and Bausch Health Canada. Dr. Wharton has also played a leadership role in The Obesity Society and Obesity Canada. Jennifer Zymantas reports receiving an honorarium from Pfizer for a moderator role during speaking engagements related to anticoagulation. Diane Hua-Stewart holds the role of director of CHEP+. Peter Liu reports receiving grants from Genome Canada, the Heart and Stroke Foundation and CIHR. Dr. Liu holds a patent for IGFBP7 as a biomarker for heart failure and is chief scientific officer with the University of Ottawa Heart Institute. Sheldon Tobe reports receiving a KMH clinic unrestricted grant and in-kind support for the Zero to Five study, paid to Sunnybrook Research Institute; consulting fees from AstraZeneca; and payment for the CHEP+ education program from Amgen, Astra-Zeneca, BMS, Bayer, Boehringer Ingelheim, Janssen, Lilly, Novartis, Novo Nordisk, Pfizer and Sanofi Genzyme. Dr. Tobe also holds a volunteer role with the American Hypertension Specialists Certification Program. No other competing interests were declared.

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. Our objective is to test whether a focused, expert medication management intervention using a mix of in-person consultation and virtual care follow-up, is feasible and effective in preventing anticoagulation-related adverse events, for patients transitioning from hospital to home., Methods and Analysis: A randomized, parallel, multicenter design enrolling consenting adult patients or the caregivers of cognitively impaired patients about to be discharged from medical wards with a discharge prescription for an OAC. The interdisciplinary multimodal intervention is led by a clinical pharmacologist and includes a detailed discharge medication reconciliation and management plan focused on oral anticoagulants at hospital discharge; a circle of care handover and coordination with patient, hospital team and community providers; and early post-discharge follow-up virtual medication check-up visits at 24 h, 1 week, and 1 month. The control group will receive usual care plus encouragement to use the Thrombosis Canada website. The primary feasibility outcomes include recruitment rate, participant retention rates, trial resources management, and the secondary clinical outcomes include adverse anticoagulant safety events composite (AASE), coordination and continuity of care, medication-related problems, quality of life, and healthcare resource utilization. Follow-up is 3 months., Discussion: This pilot RCT tests whether there is sufficient feasibility and merit in coordinating oral anticoagulant care early post-hospital discharge to warrant a full sized RCT., Trial Registration: NCT02777047., (© 2022. The Author(s).)

Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, along with a healthy lifestyle can help prevent the onset of disease. This preliminary study proposes the use of explainable artificial intelligence techniques with the aim of (i) characterizing diabetic patients through a set of easily interpretable rules and (ii) providing individualized recommendations for the prevention of the onset of the disease through the generation of counterfactual explanations, based on minimal variations of biomarkers routinely collected in primary care. The results of this preliminary study parallel findings from the literature as differences in biomarkers between patients with and without diabetes are observed for fasting blood sugar, body mass index, and high-density lipoprotein levels.

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. The COVID-19 pandemic has accelerated the interest in clinically relevant online exams, including automated assessment of short answer style questions. The goal of this project was to design a software to automate the assessment of learners' prescriptions written during low stakes formative assessments. After establishing the components of a legal prescription with multiple medications, and identifying the sources of errors in prescribing and prescribing assessment, we designed and validated an architecture and developed a prototype for automated parsing of learner prescriptions.

The aim of this study was to develop a peer-to-peer virtual intervention for patients with type 2 diabetes from different segments: patients who take several medications (medication group), patients who do not take diabetes medications (lifestyle group), and a mixed group. Preliminary results showed that patients in the lifestyle group were interested in preventive strategies, reporting better learning experience and higher motivation than those in the medication group. Future research is needed to design approaches tailored to patients in the medication group.

Many patients with Type 2 Diabetes (T2D) have difficulty in controlling their disease despite wide-spread availability of high-quality guidelines, T2D education programs and primary care follow-up programs. Current diabetes education and treatment programs translate knowledge from bench to bedside well, but underperform on the 'last-mile' of converting that knowledge into action (KTA). Two innovations to the last-mile problem in management of patients with T2D are introduced. 1) Design of a platform for peer-to-peer groups where patients can solve KTA problems together in a structured and psychologically safe environment using all the elements of the Action Cycle phase of the KTA framework. The platform uses Self-Determination Theory as the behavior change theory. 2) A novel patient segmentation method to enable the formation of groups of patients who have similar behavioral characteristics and therefore who are more likely to find common cause in the fight against diabetes.

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. We report on the design and development of a prototype digital tool that decreases cognitive effort for people at risk of developing T2D using the effort-optimized intervention framework.

Background and Objective: In the medical field, data techniques for prediction and finding patterns of prevalent diseases are of increasing interest. Classification is one of the methods used to provide insight into predicting the future onset of type 2 diabetes of those at high risk of progression from pre-diabetes to diabetes. When applying classification techniques to real-world datasets, imbalanced class distribution has been one of the most significant limitations that leads to patients' misclassification. In this paper, we propose a novel balancing method to improve the prediction performance of type 2 diabetes mellitus in imbalanced electronic medical records (EMR)., Methods: A novel undersampling method is proposed by utilizing a fixed partitioning distribution scheme in a regular grid. The proposed approach retains valuable information when balancing methods are applied to datasets., Results: The best AUC of 80% compared to other classifiers was obtained from the logistic regression (LR) classifier for EMR by applying our proposed undersampling method to balance the data. The new method improved the performance of the LR classifier compared to existing undersampling methods used in the balancing stage., Conclusion: The results demonstrate the effectiveness and high performance of the proposed method for predicting diabetes in a Canadian imbalanced dataset. Our methodology can be used in other areas to overcome the limitations of imbalanced class distributions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Heart failure (HF) affects many older individuals in North America, with recurrent hospitalizations despite postdischarge strategies to prevent readmission. Proper HF self-care can potentially lead to better clinical outcomes, yet many older patients find self-care challenging. Mobile health (mHealth) apps can provide support to patients with respect to HF self-care. However, many mHealth apps are not designed to consider potential patient barriers, such as literacy, numeracy, and cognitive impairment, leading to challenges for older patients. We previously demonstrated that a paper-based standardized diuretic decision support tool (SDDST) with daily weights and adjustment of diuretic dose led to improved self-care., Objective: The aim of this study is to better understand the self-care challenges that older patients with HF and their informal care providers (CPs) face on a daily basis, leading to the conversion of the SDDST into a user-centered mHealth app., Methods: We recruited 14 patients (male: 8/14, 57%) with a confirmed diagnosis of HF, aged ≥60 years, and 7 CPs from the HF clinic and the cardiology ward at the Hamilton General Hospital. Patients were categorized into 3 groups based on the self-care heart failure index: patients with adequate self-care, patients with inadequate self-care without a CP, or patients with inadequate self-care with a CP. We conducted semistructured interviews with patients and their CPs using persona-scenarios. Interviews were transcribed verbatim and analyzed for emerging themes using an inductive approach., Results: Six themes were identified: usability of technology, communication, app customization, complexity of self-care, usefulness of HF-related information, and long-term use and cost. Many of the challenges patients and CPs reported involved their unfamiliarity with technology and the lack of incentive for its use. However, participants were supportive and more likely to actively use the HF app when informed of the intervention's inclusion of volunteer and nurse assistance., Conclusions: Patients with varying self-care adequacy levels were willing to use an mHealth app if it was simple in its functionality and user interface. To promote the adoption and usability of these tools, patients confirmed the need for researchers to engage with end users before developing an app. Findings from this study can be used to help inform the design of an mHealth app to ensure that it is adapted for the needs of older individuals with HF., (©Sahr Wali, Karim Keshavjee, Linda Nguyen, Lawrence Mbuagbaw, Catherine Demers. Originally published in JMIR Cardio (http://cardio.jmir.org), 09.11.2020.)

This study evaluates mobile apps using a theory-based evaluation framework to discover their applicability for patients at risk of gestational diabetes. This study assessed how well the existing mobile apps on the market meet the information and tracking needs of patients with gestational diabetes and evaluated the feasibility of how to integrate these apps into patient care. A search was conducted in the Apple iTunes and Google Play store for mobile apps that contained keywords related to the following concepts of nutrition: diet, tracking, diabetes, and pregnancy. Evaluation criteria were developed to assess the mobile apps on five dimensions. Overall, the apps scored well on education and information functions and scored poorly on engagement functions. There are few apps that provide comprehensive evidence-based educational content, tracking tools, and integration with electronic health records. This study demonstrates the need to develop apps that have comprehensive content, tracking tools, and ability to bidirectionally share data.

Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient's evolving health condition is imperative to comprehending the patient's current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton's Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM., (Copyright © 2020 Perveen, Shahbaz, Ansari, Keshavjee and Guergachi.)

Background: Heart failure (HF) is a chronic disease that affects over 1% of Canadians and at least 26 million people worldwide. With the continued rise in disease prevalence and an aging population, HF-related costs are expected to create a significant economic burden. Many mobile health (mHealth) apps have been developed to help support patients' self-care in the home setting, but it is unclear if they are suited to the needs or capabilities of older adults., Objective: This study aimed to identify HF apps and evaluate whether they met the criteria for optimal HF self-care., Methods: We conducted a systematic search of all apps available exclusively for HF self-care across Google Play and the App Store. We then evaluated the apps according to a list of 25 major functions pivotal to promoting HF self-care for older adults., Results: A total of 74 apps for HF self-care were identified, but only 21 apps were listed as being both HF and self-care specific. None of the apps had all 25 of the listed features for an adequate HF self-care app, and only 41% (31/74) apps had the key weight management feature present. HF Storylines received the highest functionality score (18/25, 72%)., Conclusions: Our findings suggest that currently available apps are not adequate for use by older adults with HF. This highlights the need for mHealth apps to refine their development process so that user needs and capabilities are identified during the design stage to ensure the usability of the app., (©Sahr Wali, Catherine Demers, Hiba Shah, Huda Wali, Delphine Lim, Nirav Naik, Ahmad Ghany, Ayushi Vispute, Maya Wali, Karim Keshavjee. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.11.2019.)

Background: Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities., Methods: Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity - the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest., Results: The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models., Conclusions: The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.

Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing diabetes in an individual effectively? To our knowledge, no study has attempted use of HMM to validate the performance of FDRSM. We used Electronic Medical Record (EMR) data, of 172,168 primary care patients to derive the 8-year risk of developing diabetes in an individual using HMM. The Area Under Receiver Operating Characteristic Curve (AROC) in our study sample of 911 individuals for whom all risk factors and follow up data were available is 86.9% compared to AROCs of 78.6% and 85% reported in a previously conducted validation study of FDRSM in the same Canadian population and the Framingham study respectively. These results demonstrate that the discrimination capability of our proposed HMM is superior to the validation study conducted using the FDRSM in a Canadian population and in the Framingham population. We conclude that HMM is capable of identifying patients at increased risk of developing diabetes within the next 8-years.

This contribution outlines a conceptual model of a novel approach to chronic disease prevention and management: Preventative Health Account Supported by Electronic Medical Records (PHASE). The PHASE model combines predictive computation of risk scores for chronic disease and use of alternative currencies on blockchain technology to build a preventative health account to help individuals invest in a healthier lifestyle, reduce their risk scores and, in turn, reduce the related potential cost to the healthcare system.