Ebook: The Role of Digital Health Policy and Leadership

Many healthcare-system interest holders in Canada lightheartedly debate whether the Canadian Healthcare System is truly a ‘system’? Several have called it the ‘non-system’. We will settle the debate for good right now. The way our healthcare is organized is indeed a system, it is, however, a flawed one. The fact that feedback loops are poorly structured, that critical information is not available or accessible, and that important elements of a system are not in place is attributable to those who are the stewards and governors of the system, and not the fault of the system itself. We get what we design.

Arguably, the Canadian healthcare system is more reactive than proactive. In other words, our priorities as a system are to spend our limited resources on treating illness rather than on preventing the occurrence of diseases. We pour resources into shortening waiting lists and purchasing the latest productivity-enhancing technologies in the glorious hope that soon, very soon, we will turn the corner and finally get ahead of all the surgeries and procedures that need to be done, and everyone in Canada will be healthy again. We try to reassure ourselves that this is not a vain hope, but in our heart of hearts we know that perhaps we need to take another route.

The Future of Health Leadership, Informatics and Policy conference was inaugurated at the Dalla Lana School of Public Health and the Institute for Health Policy, Management and Evaluation at the University of Toronto as an initiative to bring back hope to our healthcare system. We are pleased to be working with the health informatics community at the University of Victora, the University of Waterloo, and Dalhousie University in bringing these proceedings to life.

It is our collective belief that informatics thinking and digital health technologies can change the trajectory of our current system and help to convert it to one that is more proactive. New, advanced, predictive technologies have become available which make this more possible than ever before, nevertheless, it is unlikely to occur without improved policies, regulations, and governance of our health system.

We are delighted to present the papers in this collection on the topic of From Reactive to Proactive: The Role of Digital Health Policy. It is our belief that health informatics and digital health have much to say about how our healthcare system can function more efficiently. To provide policymakers, decision makers and other stakeholders with the information they need to make better allocative decisions and intervene more effectively, informatics needs to play at the macro level, not just at the user level or the inter-organizational level.

The authors have thought deeply about the key issues that plague our healthcare system, and here they present their ideas about how to address them. The topics in this collection range from interoperability to governance, regulation of electronic medical records to addressing the needs of vulnerable populations. Some authors have discussed the roles of innovative approaches and new digital technologies – including artificial intelligence – to solve healthcare-system issues.

We hope you enjoy reading these papers and considering the creative ideas the authors have developed to solve important issues in our healthcare system to help us move From Reactive, to Proactive.

Karim Keshavjee

Assistant Professor, Teaching Stream

Program Director, Health Informatics Program

Institute for Health Policy, Management and Evaluation

Dalla Lana School of Public Health

University of Toronto

Alireza Khatami

Institute for Health Policy, Management and Evaluation

Dalla Lana School of Public Health

University of Toronto

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.

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.

In partnership with clinician advisors, a text-based program, BeWell, was co-created to support clinician well-being at a Canadian mental health hospital. This paper briefly describes the process of designing BeWell with clinician advisors and highlights key lessons learned in engaging clinicians as advisors in the design and development of a digital health intervention. The lessons learned can serve as best practices for health systems, organizations, and researchers to consider when engaging clinicians in the design, development, and implementation of digital health interventions.

The opioid crisis in Ontario has led to a surge in preventable overdose deaths. Challenges in the mental health and addiction system, along with various contributing factors, have amplified this crisis. Underutilization of data exacerbates service gaps and hinders innovative solutions. Through stakeholder engagement, interrelated problems emerged, emphasizing the pervasive data underutilization. This research explores data usage in mental health and addictions, focusing on the opioid epidemic in Ontario and comparative jurisdictions. To improve service quality, Ontario should implement a comprehensive data management strategy. Two key recommendations include increased investment in exploring additional data use cases and evaluating policy initiatives using dynamic models throughout a patient’s journey.

Ontario is shifting to a Precision Medicine (PM) model, which emphasizes tailored patient care, an initiative reflected in the formation of Ontario Health Teams. However, this shift faces significant data governance, policy formulation, and technology integration hurdles. To overcome these barriers, we advocate for a comprehensive PM framework to orchestrate collaboration among healthcare providers, policymakers, and technologists. This framework enhances data management, propels digital health innovations, and uphold ethical standards in AI applications. Effective deployment of this framework is crucial for actualizing PM’s promise in Ontario, potentially revolutionizing healthcare delivery.

The surge of AI-driven technologies in the digital health market demands a concurrent evolution in evaluation standards, a pace currently lagging behind innovation. This paper explores the pivotal inadequacies within existing evaluation models, highlighting the necessity for refined methodologies that align with the unique complexities of digital health. We critically examine the initiatives of key entities such as Health Canada, CADTH, and CNDHE, pinpointing the deficiencies in addressing the volatility and intricacies of AI applications. To bridge these gaps, we advocate for a nuanced evaluation paradigm, proposing the establishment of an oversight body, implementing detailed category-specific criteria, and a robust six-step evaluation framework tailored for AI health solutions. The paper culminates by underscoring the indispensable role of strategic leadership and agile policymaking in cultivating a resilient digital health environment that prioritizes patient care without compromising the ingenuity of technological advances.

The Centre for Addiction and Mental Health has implemented mechanisms to standardize routine data collection with the vision of a Learning Health System. To improve clinical decision-making and patient outcomes, a clinical dashboard was implemented to provide a real-time visualization of data from patient self-assessments and other physical and mental health indicators. This case report shares early findings of dashboard implementation to understand user uptake and improve fidelity of the technology and processes that need to support adoption. Moreover, these findings will inform the strategy and development of a hospital-wide scalable dashboard that will span across clinical areas and leverage artificial intelligence to continuously improve patient outcomes and equitable care delivery.

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.

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.

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 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.

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.

The rapid growth of digital health and use of technology has led to an increased demand for qualified professionals in the areas of health informatics (HI) and health information management (HIM). This is reflected by the growth in the number of educational programs and graduates in these areas. However, to develop a culture of digital health innovation in Canada, the role of research needs to be critically examined. In this paper we discuss some of these issues around the relation between research and innovation, and the development of an innovation culture in health informatics, health information management and digital health in Canada. Recommendations for facilitating this development in terms of funding, granting and policy are also explored.

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.

The adoption of Artificial Intelligence (AI) in the Canadian healthcare system falls behind that of other countries. Socio-technological considerations such as organizational readiness and a limited understanding of the technology are a few barriers impeding its adoption. To address this need, this study implemented a five-month AI mentorship program with the primary objective of developing participants’ AI toolset. The analysis of our program’s effectiveness resulted in recommendations for a successful mentorship and AI development and implementation program. 12 innovators and 11 experts from diverse backgrounds were formally matched and two symposiums were integrated into the program design. 8 interviewed participants revealed positive perceptions of the program underscoring its contribution to their professional development. Recommendations for future programs include: (1) obtaining organizational commitment for each participant; (2) incorporating structural supports throughout the program; and (3) adopting a team-based mentorship approach. The findings of this study offer a foundation rooted in evidence for the formulation of policies necessary to promote the integration of AI in Canada.

This project aimed to accurately assess the current state of routine immunization program delivery in a Central Zone community in Alberta and provide actionable recommendations supported by literature review. Engaging with frontline public health nurses responsible for immunization program delivery in the community, contributing factors to low vaccination rates, process inefficiencies and policy gaps were identified. Based on additional literature, strategies to mitigate these gaps with the goal of increasing vaccination rates were proposed and validated. Although in this case, strategies to mitigate process inefficiencies were the most supported given program funding, a multi-pronged approach is still recommended to drive long-term improvements in vaccination rates.

This paper maps suicide help-seeking needs identified in the literature, on to the features and functionalities of suicide prevention mobile apps using the adapted ecological model, thereby revealing existing gaps between help-seeking needs and available apps. This paper builds upon previous work by our team, which includes 1) a rapid scoping review aimed at identifying barriers and facilitators of help-seeking related to suicide within psychiatric populations, and 2) a review of suicide prevention apps, including a content analysis of app features and functionalities.

Canadian healthcare suffers rural disparities, especially in maternal and prenatal care. Drawing on a literature review, the paper highlights the potential of mobile health (mHealth) applications to bridge this gap and improve maternal care in rural communities. mHealth tools have great potential for knowledge and trust-building among healthcare workers and pregnant women. To support the success of these solutions, more funding and policy support are required. mHealth solutions have a great potential for great economic savings while addressing healthcare disparities and ensuring everyone has access to high quality care.

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.