HealthcarePapers
Trustworthy Evidence to Support Quality Digital Healthcare Policy for Underserved Communities: What Needs to Happen to Translate Evidence into Policy?
Abstract
In this paper, we explore what is needed to generate quality research to guide evidence-informed digital health policy and call the Canadian community of patients, clinicians, policy (decision) makers and researchers to action in setting digital health research priorities for supporting underserved communities. Using specific examples, we describe how evidence is produced and implemented to guide digital health policy. We study how research environments must change to reflect and include the communities for whom the policy is intended. Our goal is to guide how future evidence reaches policy makers to help them shape healthcare services and how these services are delivered to underserved communities in Canada. Understanding the pathways through which evidence can make a difference to equitable and sustainable digital health policy is vital for guiding the types of research that attract priority resources.
Introduction
Industry is investing billions in digital health (Duffy et al. 2022), and billions of people are downloading healthcare apps to their devices (Patrick et al. 2016). While the COVID-19 pandemic turbocharged public discussion about and demand for digital health services, digital health is not a new way of delivering healthcare (Bashshur and Armstrong 1976). Underserved communities have been at the forefront of developing and implementing digital healthcare (Bacon et al. 2023; Rothman 2023); through sheer necessity, they are pioneers of essential healthcare (Novak Lauscher et al. 2023).
Digital health research and policy must reflect the needs and experiences of underserved communities. Unfortunately, the foundational work required to develop digital health tools very often does not seek the input of the very people who are supposed to use the digital health solutions. Compounding the problem is the new technology developed in funded research projects that often does not integrate well with the existing healthcare systems.
Policy makers are recognizing the potential for digital health to help plug gaps in overstretched and understaffed health systems (BC Gov News 2022). The COVID-19 pandemic forced a rapid pivot in how healthcare services were prioritized and delivered –change can happen, and fast. Before policy makers start implementing a digital health solution, they need evidence to inform decisions that help them ensure that the digital health services they plan to implement work, that people can access what they need when they need it and that digital health services pair with existing systems.
In this paper, we explore what is needed to generate trustworthy evidence to inform policy about digital health for underserved communities. We call patients, clinicians, policy makers and researchers to action in conducting, disseminating and implementing digital health research.
Canadian (digital) health policy
Connecting health systems and their services to good health outcomes is the ideal state, yet it is difficult to achieve (Chen 2012). Generating trustworthy evidence and sharing the evidence to support health policy are priorities for improving health services (WHO 2012).
Canadian health policies are founded on the Canada Health Act (1985) and aspire to fairness and equity (Health Canada 2021). Policies can help address the needs of underserved communities by (1) identifying gaps in how services are delivered or inequities in health outcomes and (2) directing focus and funding to address priority issues. A joined-up care approach helps address underlying inequities (Pollack Porter et al. 2018; White et al. 2006).
In the early 2000s, the Accord on Health Care Renewal introduced information/digital health technology as part of a targeted health system reform to support timely access to quality care and sustainability (Health Canada 2021). Since then, reports have recommended how digital health can support the healthcare system; examples include using:
- virtual care to connect providers and people in rural and remote areas to services (Commission on the Future of Health Care in Canada and Romanow 2003);
- electronic health records and telehealth to improve access to services, enhance patient safety, and boost quality of care and productivity (Health Canada 2019); and
- learning health systems, supported by fully electronic and interoperable health systems, to aid in data access and sharing (Forest and Martin 2018).
What Is the Current Landscape of Digital Health Research in Canada?
Canada has been slow to adopt, disseminate and implement digital health solutions (Hamoni et al. 2021). The Pan-Canadian Health Data Strategy – the foundation to strengthen how health data are created, used and exchanged (Public Health Agency of Canada 2022) – provides national policy recommendations for virtual care (Falk 2021), and a virtual care framework outlines key policy levers to deliver sustainable digital healthcare (Health Canada 2021).
Federal research funding to underpin a sustainable health system
The Canadian Institutes of Health Research (CIHR) – Canada's federal health research funding agency – catalyzes and supports the digital health ecosystem. CIHR supports work that meets the needs of underserved communities and informs health policy and practice (CIHR 2021). CIHR funds often support co-designed research.
To illustrate CIHR's investment in digital health over the past decade, we identified the research projects funded in the themes of digital health, health equity and underserved communities (Indigenous Peoples and historically excluded, under-represented, marginalized and at-risk populations) between 2013 and 2023. Our keyword search identified 506 projects; each project was checked and validated for accuracy. Here, we describe 420 projects (Figure 1).
In the past decade, CIHR invested $95.6 million in digital health–related projects; $16.9 million (or 17.7%) was directed to 62 projects (out of 420 digital health projects) related to health equity and/or underserved populations. CIHR's attention to and investment in digital health has steadily increased. In 2021–22, total investment peaked, so did the number of funded projects and nominated principal investigators. The proportion of funded projects that focused on health equity/underserved populations also rose, likely in response to major socio-cultural events, including the COVID-19 pandemic.
As one of the many research funders in Canada, CIHR contributes a portion of the funding for Canadian digital health research. The remaining funding comes from a mix of public and private organizations (e.g., national funding agencies, provincial health research funding organizations, health charities, pan-Canadian organizations, ministries of health and local and regional health-related organizations).
Data collected outside the traditional academic setting
Non-academic data, including those gathered and analyzed by health authorities, are important because they:
- provide a real-world insight into the challenges and opportunities in the local context and
- help decision makers design policies that meet the community's needs and deliver services that work in their environment/context.
Here, we share an example of how data from a non-academic setting play a central role in guiding digital health policy. The Fraser Health Authority, which is the largest health authority in British Columbia, collects and uses data for digital health research, evaluation and quality improvement projects.
Fraser Health delivers hospital and community-based health services to over 1.9 million people in 20 diverse communities within the traditional, ancestral and unceded territories of the Coast Salish and Nlaka'pamux Nations and is home to six Métis Chartered Communities. The diverse patient demographic includes 62,000 Indigenous Peoples and almost 250,000 South Asian community members (https://www.fraserhealth.ca/about-us/about-fraser-health).
Fraser Health's virtual health evaluation and strategy team
The following four guiding principles underpin the research and evaluation projects coordinated by Fraser Health (Figure 2):
- Engage with patients: Patient and family advisory councils actively participate using tools such as patient journey maps to identify opportunities to implement digital health services, visualize how digital health can integrate with existing health systems and enhance digital health strategy planning at a policy level (Borycki et al. 2020; McCarthy et al. 2020).
- Collaborate: The virtual health team collaborates with clinical leaders and other dedicated teams to embed virtual health solutions into clinical programs. An interdisciplinary approach embeds virtual health solutions into clinical care.
- Meet the community's needs: Internal data inform digital care services and promote accessibility and equity. By examining usage patterns among different demographic groups, policy makers can identify where interventions may fall short, and tailor policies accordingly.
- Evaluate: Ongoing evaluation and quality improvement projects are integral. The iterative process relies on data to refine and improve services.
Between September 2022 and September 2023, three in every 10 patient visits occurred online. Collecting data helps Fraser Health decision makers understand how technology helps the community access care and the outcomes of care. Guided by the data it collects and analyzes, Fraser Health's virtual health service follows a patient-centred care model to connect patients, families and providers.
How Evidence Can Inform Digital Health Policy and Shape Healthcare Services
In this section, we guide the reader to concepts of justice for people who seek healthcare. Before identifying and recommending appropriate next steps, we ask readers to consider how systems of producing and evaluating evidence are related to a society's power structures, which can erode equity and entrench disadvantage.
Epistemic injustice describes how systems and societies value different forms of knowledge, based on how they are produced and by whom (Fricker 2007). There are two types of epistemic injustice: testimonial injustice and hermeneutical injustice.
Testimonial injustice (Fricker 2007) occurs when those who are marginalized are perceived as having less capacity to understand and communicate the way they experience the world – they are perceived as having less capacity to know. Hermeneutical injustice (Hänel 2020) occurs when the experiences of marginalized people are not accurately perceived, interpreted or communicated because society has ignored or suppressed the language and frameworks that are needed to attach meaning to them.
Testimonial and hermeneutical injustice often reinforce one another (Medina 2012). People, whose capacity to know is devalued, are excluded from contributing to collective knowledge. Marginalized people, whose accounts of their experiences challenge what is already accepted as known, are perceived as being unreliable sources of knowledge (Byskov 2021).
What is evidence?
The paradigm of evidence-based medicine advocates for classifying evidence (Burns et al. 2011). Clinicians and policy makers are expected to make decisions using the highest level of evidence available to them. When evidence and decisions are framed in this way, it implies that evidence is high quality (i.e., trustworthy and valid) when it is obtained through large, highly controlled experiments and analyses; at the bottom of the evidence pyramid are individual experiences' data and preferences (Blease et al. 2017; Bueter 2023; Heggen and Berg 2021; Michaels 2021; Pozzi 2023).
Who is an expert?
More than three decades ago, the Evidence-Based Medicine Working Group asserted that their new approach to teaching the practice of medicine would flatten the hierarchy in how medical knowledge is produced, disseminated and used (Guyatt et al. 1992). Yet hierarchy and authority are omnipresent in the expertise of evidence-based practice and policy.
When expertise was invoked in evidence-based medicine, it was equated with the professional knowledge and qualifications of clinicians. Patients were not considered experts (Sackett et al. 1996; Wieten 2018). Since the late 1990s, proponents of evidence-based medicine have advocated for including patient preferences as a central tenet of evidence-based decision making (Sackett et al. 1996; Wieten 2018). Yet patient perspectives are framed as beliefs, not evidence or knowledge.
What counts as “valid” evidence?
Evidence-based medicine relies on statistical data about a group of patients to guide decisions for the individual – a situation some have labelled a fallacy (Anjum et al. 2015; Grotenhuis et al. 2011). For population-level trends to reliably predict an individual outcome, the individual must be the same as the study group, which is unlikely, given the complexity of the human body and pathophysiological mechanisms. Because underserved groups are under-represented in traditional research, the fallacy is perpetuated (Ellison et al. 2007).
The evidence-based medicine framework is challenged in clinical scenarios where little research evidence exists, including multimorbidity, rare, perceived-rare, under-researched and poorly understood conditions (Abrahamyan et al. 2016; Boyd and Kent 2014; Campbell-Scherer 2010; Goldenberg 2006; Greenhalgh et al. 2014; Kruer and Steiner 2008). These are scenarios in which patients from underserved social groups are over-represented (Moin et al. 2018).
Actions to Deliver Trustworthy Evidence for Excellent Policy in Digital Health for the Underserved
To move digital health research and policy forward in a way that makes a difference for underserved communities, the priorities of funders, people who do the research, the organizations that support the research and government agencies must align. We suggest four areas of focus.
Patients as research co-leaders
Our vision is for a research environment where patients contribute as full-fledged researchers with full access to the support of research institutions and infrastructure (e.g., academic libraries). Patients direct research priorities, design studies, lead analyses and disseminate the results. People, as experts in their own experiences and communities and in the care systems with which they interact, bring expert insights to the research and policy-making tables. Their personal encounters with health systems, access challenges and health outcomes provide a perspective that policy makers cannot ignore.
Meaningful collaboration between professional and lived-experience experts requires people to consider questions, such as what are the distinct bodies of knowledge and communities of practice to which clinical and patient researchers have access? Which elements of these communities are open and which are resistant to outsiders? What is needed to foster interdisciplinary and cross-cultural collaboration among groups?
Support for patient-oriented research
We call for targeted funds and grants that accommodate the specific needs and ways of engaging underserved communities. We are pleased to see tangible support for community-led research projects (The University of British Columbia 2023) and the success of CIHR's Strategy for Patient-Oriented Research (SPOR). In an impact assessment of CIHR's eHealth Initiative (CIHR 2023) – a $17.8 million digital health funding program – researchers reported that strong partnerships with patients and providers were key to the success of their project and to generating evidence for policy decisions. The co-designed digital health interventions better met the needs of the community and contributed to improving health equity and experiences of care, research and policy.
Lead investigators are expected to have an institutional affiliation or an affiliated partner who can support their funding application. The expectation excludes patient researchers who may have difficulty undertaking the kind of networking and social capital–building work required to get a foot in the door to support their research interests (Chenoweth and Stehlik 2004; Potts 2005; Sanders and Rogers 2007; Tankink 2023). We would like to see infrastructure that supports patient researchers to apply for funding and to connect with appropriate and interested co-investigators and institutional support as part of the funding process.
Interdisciplinary, critical and qualitative frameworks are essential for transformative research, including in digital health. Unfortunately, design research and interdisciplinary projects often fall through the cracks of existing tri-agency opportunities. We call for creative funding opportunities that bring people together from across disciplines to solve shared problems. The New Frontiers in Research Fund (Canada Research Coordinating Committee 2023) is a good start; we call for more support in this vein.
A “stepped evidence” model of valuing knowledge
In lieu of hierarchies of evidence, we advocate for a stepped evidence framework. Informed by stepped care approaches to mental health and chronic illness (Cornish 2020; Davison 2000; Von Korff and Tiemens 2000), a stepped evidence model (Figure 3) recognizes healthcare options that vary in intensity and patient autonomy. Rather than elevating a gold-standard, stepped evidence prioritizes a flexible approach that is tailored to the context of the person who seeks healthcare.
Within a stepped evidence framework, researchers and funders evaluate the appropriateness of research methods (including traditional clinical research methods and sociological, user research and service co-design methods) for the specific parameters of a research project and population and balance the weighting of evidence gathered from clinical settings, people with lived experience and the community.
Inclusive design
Designing from the margins meets the needs of extra-ordinary users rather than designing for groups of normative users (Andrews 2016; Pullin and Newell 2007). Embedding inclusion principles in design – embracing user needs and behaviours – rather than treating inclusion as an after-thought produces solutions that work for more people (Andrews 2016; Conradie et al. 2014; Pullin and Newell 2007). A well-known example is the curb cut phenomenon (Newell and Gregor 1997; Reid 2022) where dropped curbs, built to create accessibility for wheelchair and other mobility device users, have improved access for others (e.g., parents pushing strollers and travellers pulling rolling luggage). Designing from the margins recognizes that marginalized users are experts on the design flaws within the existing systems and services because they have lived experience of the flaws.
Summary
Underserved communities deserve trustworthy evidence to support decisions about digital health policies that affect them. We encourage researchers and policy makers to bring a curious and creative open mind when producing and evaluating evidence. We suggest that the organizations and people who are funding, producing and interpreting evidence find ways to authentically involve and represent the lived experiences of non-researchers, including patients and caregivers from underserved communities. Involving patients and caregivers as co-leaders and incorporating principles of inclusive design into research are steps in the right direction.
Recommendations
To move digital health research and policy forward in a way that impacts underserved communities, the priorities of funders, people who do the research, the organizations that support the research and government agencies must align.
To generate quality research that guides quality, evidence-informed digital health policy, we call for:
- research teams to include patients as research co-leaders;
- funders to support targeted grants that support the specific needs and ways of engaging underserved communities;
- decision makers to embrace a stepped evidence framework when weighting evidence gathered from different sources; and
- designers to embrace inclusive ways of working that centre the needs of the “extra-ordinary” users rather than normative users.
Disclaimer
The views expressed herein are solely of the authors and do not necessarily reflect those of the CIHR.
About the Author(s)
Clare L. Ardern, BPhysio (Hons), PhD, Assistant Professor, Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC
Alex Haagaard, BSc, MDes, Patient Partner, Chronic Pain Network and Pain BC, Toronto, ON
Megan MacPherson, BA (Hons), MSc, PhD, Virtual Health Research and Knowledge Translation Lead, Virtual Health Department, Fraser Health Authority, Surrey, BC
Jessica Nadigel, BSc, PhD, Associate Scientific Director, Institute of Health Services and Policy Research, Canadian Institutes of Health Research, Toronto, ON
Bahar Kasaai, BSc, MSc, PhD, Research Impact and Strategic Initiatives Lead, Institute of Health Services and Policy Research, Canadian Institutes of Health Research, Toronto, ON
Sonya Cressman, BSc, PhD, MBA, Evaluation Lead, Digital Emergency Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC
Jennifer Cordeiro, BAA (Hons), Senior Research Coordinator, Digital Emergency Medicine, University of British Columbia, Vancouver, BC
Kendall Ho, MD, FRCPC, Lead, Digital Emergency Medicine, Professor, Department of Emergency Medicine, Vancouver, BC
Acknowledgment
We sincerely thank the Funding Analytics team at CIHR for their assistance in identifying and summarizing the research projects that CIHR funded in the themes of digital health and underserved communities between 2013 and 2023. We also thank Rhonda Boateng (lead, Communications and Strategic Initiatives at the Institute of Health Services and Policy Research, CIHR) for helping with the CIHR data validation. James Kempster (ambassador, Patient Engagement Research, Institute of Musculoskeletal Health and Arthritis, CIHR) and Deanne Taylor (Rural Coordination Centre of British Columbia) reviewed and shared invaluable feedback to make this paper better.
References
Abrahamyan, L., B.M. Feldman, G. Tomlinson, M.E. Faughnan, S.R. Johnson, I.R. Diamond et al. 2016. Alternative Designs for Clinical Trials in Rare Diseases. American Journal of Medical Genetics Part C, Seminars in Medical Genetics 172(4): 313–31. doi:10.1002/ajmg.c.31533.
Andrews, C. 2016, March 18. Designing with People with Visual Impairments: An Exploration of the Value of Extraordinary Users in Design [Thesis]. Cardiff Metropolitan University. Retrieved September 18, 2023. <https://figshare.cardiffmet.ac.uk/articles/thesis/Designing_with_people_with_visual_impairments_an_exploration_of_the_value_of_extraordinary_users_in_design/20272413>.
Anjum, R.L., R. Kerry and S.D. Mumford. 2015. Evidence Based on What? Journal of Evaluation in Clinical Practice 21(6): E11–12. doi:10.1111/jep.12493.
Bacon, G., A. Sweeney, R. Batchelor, C. Grant, N. Mantovani, S. Peter et al. 2023. At the Edge of Care: A Systematic Review and Thematic Synthesis of Parent and Practitioner Views and Experiences of Support for Parents with Mental Health Needs and Children's Social Service Involvement. Health & Social Care in the Community 2023: 6953134. doi:10.1155/2023/6953134.
Bashshur, R.L. and P.A. Armstrong. 1976. Telemedicine: A New Mode for the Delivery of Health Care. Inquiry 13(3): 233–44.
BC Gov News. 2022, September 29. New Health Workforce Strategy Improves Access to Health Care, Puts People First. Government of British Columbia. Retrieved March 6, 2023. <https://news.gov.bc.ca/releases/2022HLTH0059-001464>.
Blease, C., H. Carel and K. Geraghty. 2017. Epistemic Injustice in Healthcare Encounters: Evidence from Chronic Fatigue Syndrome. Journal of Medical Ethics 43(8): 549–57. doi:10.1136/medethics-2016-103691.
Borycki, E.M., A.W. Kushniruk, E. Wagner and R. Kletke. 2020. Patient Journey Mapping: Integrating Digital Technologies into the Journey. Knowledge Management & E-Learning: An International Journal 12(4): 521–35. doi:10.34105/j.kmel.2020.12.029.
Boyd, C.M. and D.M. Kent. 2014. Evidence-Based Medicine and the Hard Problem of Multimorbidity. Journal of General Internal Medicine 29(4): 552–53. doi:10.1007/s11606-013-2658-z.
Bueter, A. 2023. Diagnostic Overshadowing in Psychiatric-Somatic Comorbidity: A Case for Structural Testimonial Injustice. Erkenntnis 88: 1135–55. doi:10.1007/s10670-021-00396-8.
Burns, P.B., R.J. Rohrich and K.C. Chung. 2011. The Levels of Evidence and Their Role in Evidence-Based Medicine. Plastic and Reconstructive Surgery 129(1): 305–10. doi:10.1097/PRS.0b013e318219c171.
Byskov, M.F. 2021. What Makes Epistemic Injustice an “Injustice”? Journal of Social Philosophy 52(1): 114–31. doi:10.1111/josp.12348.
Campbell-Scherer, D. 2010. Multimorbidity: A Challenge for Evidence-Based Medicine. Journal of Evidence-Based Medicine 15(6): 165–66. doi:10.1136/ebm1154.
Canada Health Act, R.S.C., 1985, c. C-6. Government of Canada. Retrieved January 18, 2024. <https://laws-lois.justice.gc.ca/eng/acts/c-6/page-1.html>.
Canada Research Coordinating Committee. 2023, August 15. New Frontiers in Research Fund. Government of Canada. Retrieved September 18, 2023. <https://www.sshrc-crsh.gc.ca/funding-financement/nfrf-fnfr/index-eng.aspx>.
Canadian Institutes of Health Research (CIHR). 2021. CIHR Strategic Plan 2021–2031. A Vision for a Healthier Future. Retrieved September 20, 2023. <https://cihr-irsc.gc.ca/e/documents/cihr-strategic-plan-2021-2031-en.pdf>.
Canadian Institutes of Health Research (CIHR). 2023. Evidence from CIHR's eHealth Innovations Impact Assessment [Expected publication date: May 2024].
Chen, L.C. 2012. Good Health at Low Cost: From Slogan to Wicked Problem. The Lancet 379(9185): 509–10. doi:10.1016/S0140-6736(12)60218-9.
Chenoweth, L. and D. Stehlik. 2004. Implications of Social Capital for the Inclusion of People with Disabilities and Families in Community Life. International Journal of Inclusive Education 8(1): 59–72. doi:10.1080/1360311032000139467.
Commission on the Future of Health Care in Canada and R.J. Romanow. 2003. Building on Values. The Future of Health Care in Canada: Executive Summary. Healthcare Papers 3(4): 11–22. doi:10.12927/hcpap.2003.17376.
Conradie, P., L. De Couvreur, J. Saldien and L. De Marez. 2014. Disabled Users as Lead Users in Product Innovation: A Literature Overview. In “DS 81: Proceedings of NordDesign 2014, Espoo, Finland 27-29th August 2014.” Retrieved January 18, 2024. <https://www.designsociety.org/publication/36153/DS+81%3A+Proceedings+of+NordDesign+2014%2C+Espoo%2C+Finland+27-29th+August+2014>.
Cornish, P. 2020. Stepped Care 2.0: A Paradigm Shift in Mental Health. Springer.
Davison, G.C. 2000. Stepped Care: Doing More with Less? Journal of Consulting and Clinical Psychology 68(4): 580–85. doi:10.1037/0022-006X.68.4.580.
Duffy, A., G.J. Christie and S. Moreno. 2022. The Challenges toward Real-World Implementation of Digital Health Design Approaches: Narrative Review. JMIR Human Factors 9(3): e35693. doi:10.2196/35693.
Ellison, G.T.H., A. Smart, R. Tutton, S.M. Outram, R.E. Ashcroft and P. Martin. 2007. Racial Categories in Medicine: A Failure of Evidence-Based Practice? PLoS Medicine 4(9): e287. doi:10.1371/journal.pmed.0040287.
Falk, W. 2021, June 29. The State of Virtual Care in Canada as of Wave Three of the COVID-19 Pandemic: An Early Diagnostic and Policy Recommendations. Retrieved September 20, 2023. <https://www.canada.ca/en/health-canada/corporate/transparency/health-agreements/bilateral-agreement-pan-canadian-virtual-care-priorities-covid-19/wave-three-early-diagnostic-policy-recommendations.html#a11>.
Forest, P.-G. and D. Martin. 2018, March. Fit for Purpose: Findings and Recommendations of the External Review of the Pan-Canadian Health Organizations – Summary Report. Retrieved September 20, 2023. <https://www.canada.ca/en/health-canada/services/health-care-system/reports-publications/health-care-system/findings-recommendations-external-review-pan-canadian-health-organization.html>.
Fricker, M. 2007. Testimonial Injustice. In Epistemic Injustice: Power and the Ethics of Knowing (pp. 9–29). Oxford University Press. doi:10.1093/acprof:oso/9780198237907.003.0002.
Goldenberg, M.J. 2006. On Evidence and Evidence-Based Medicine: Lessons from the Philosophy of Science. Social Science and Medicine 62(11): 2621–32. doi:10.1016/j.socscimed.2005.11.031.
Greenhalgh, T., J. Howick and N. Maskrey. 2014. Evidence Based Medicine: A Movement in Crisis? BMJ 348: g3725. doi:10.1136/bmj.g3725.
Grotenhuis, M.Te, R. Eisinga and S.V. Subramanian. 2011. Robinson's Ecological Correlations and the Behavior of Individuals: Methodological Corrections. International Journal of Epidemiology 40(4): 1123–25. doi:10.1093/ije/dyr081.
Guyatt, G., J. Cairns, D. Churchill, D. Cook, B. Haynes, J. Hirsh et al. 1992. Evidence-Based Medicine. A New Approach to Teaching the Practice of Medicine. JAMA 268(17): 2420–25. doi:10.1001/jama.1992.03490170092032.
Hamoni, R., M. Matthews and M. Watson. 2021. Digital Transformation: The Next Big Leap in Healthcare. Information and Communications Technology Council. Retrieved September 20, 2023. <https://www.ictc-ctic.ca/wp-content/uploads/2021/08/ICTC_Report_DigitalTransformation_August-12.pdf>.
Hänel, H.C. 2020. Hermeneutical Injustice (Self-)Recognition, and Academia. Hypatia 35(2): 336–54. doi:10.1017/hyp.2020.3.
Health Canada. 2019. Canada's Health Care System. Government of Canada. Retrieved September 20, 2023. <https://www.canada.ca/en/health-canada/services/health-care-system/reports-publications/health-care-system/canada.html#a15>.
Health Canada. 2021, July 7. Virtual Care Policy Framework. Government of Canada. Retrieved September 20, 2023. <https://www.canada.ca/en/health-canada/corporate/transparency/health-agreements/bilateral-agreement-pan-canadian-virtual-care-priorities-covid-19/policy-framework.html#a2>.
Heggen, K.M. and H. Berg. 2021. Epistemic Injustice in the Age of Evidence-Based Practice: The Case of Fibromyalgia. Humanities and Social Sciences Communications 8: 235. doi:10.1057/s41599-021-00918-3.
Kruer, M.C. and R.D. Steiner. 2008. The Role of Evidence-Based Medicine and Clinical Trials in Rare Genetic Disorders. Clinical Genetics 74(3): 197–207. doi:10.1111/j.1399-0004.2008.01041.x.
McCarthy, S., P. O'Raghallaigh, S. Woodworth, Y.Y. Lim, L.C. Kenny and F. Adam. 2020. Embedding the Pillars of Quality in Health Information Technology Solutions Using “Integrated Patient Journey Mapping” (IPJM): Case Study. JMIR Human Factors 7(3): e17416. doi:10.2196/17416.
Medina, J. 2012. Hermeneutical Injustice and Polyphonic Contextualism: Social Silences and Shared Hermeneutical Responsibilities. Social Epistemology 26(2): 206. doi:10.1080/02691728.2011.652214.
Michaels, J.A. 2021. Potential for Epistemic Injustice in Evidence-Based Healthcare Policy and Guidance. Journal of Medical Ethics 47: 417–22. doi:10.1136/medethics-2020-106171.
Moin, J.S., R. Moineddin and R.E.G. Upshur. 2018. Measuring the Association between Marginalization and Multimorbidity in Ontario, Canada: A Cross-Sectional Study. Journal of Comorbidity 8(1). doi:10.1177/2235042x18814939.
Newell, A.F. and P. Gregor. 1997. Human Computer Interfaces for People with Disabilities. In M.G. Helander, T.K. Landauer and P.V. Prabhu, eds., Handbook of Human-Computer Interaction (2nd ed.) (pp. 813–24). Elsevier B.V.
Novak Lauscher, H., K. Stewart, R. Markham, J. Pawlovich, J. Mah, M. Hunt et al. 2023. Real-Time Virtual Supports Improving Health Equity and Access in British Columbia. Healthcare Management Forum 36(5): 285–92. doi:10.1177/08404704231183177.
Patrick, K., E.B. Hekler, D. Estrin, D.C. Mohr, H. Riper, D. Crane et al. 2016. The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research. American Journal of Preventive Medicine 51(5): 816–24. doi:10.1016/j.amepre.2016.05.001.
Pollack Porter, K.M., L. Rutkow and E.E. McGinty. 2018. The Importance of Policy Change for Addressing Public Health Problems. Public Health Reports 133(1_suppl): 9s–14s. doi:10.1177/0033354918788880.
Potts, B. 2005. Disability and Employment: Considering the Importance of Social Capital. The Journal of Rehabilitation 71(3): 20–25.
Pozzi, G. 2023. Testimonial Injustice in Medical Machine Learning. Journal of Medical Ethics 49(8): 536–40. doi:10.1136/jme-2022-108630.
Public Health Agency of Canada. 2022, December 22. The Pan-Canadian Health Data Strategy: Expert Advisory Group Overview. Government of Canada. Retrieved September 20, 2023. <https://www.canada.ca/en/public-health/corporate/mandate/about-agency/external-advisory-bodies/list/pan-canadian-health-data-strategy-overview.html>.
Pullin, G. and A. Newell. 2007. Focussing on Extra-Ordinary Users. In C. Stephanidis, eds., Universal Access in Human Computer Interaction. Coping with Diversity (vol. 4554, pp. 253–62). Springer.
Reid, B.E. 2022. The Curb-Cut Effect and the Perils of Accessibility without Disability. Feminist Cyberlaw, University of Colorado Law Legal Studies Research Paper No. 22–24. Retrieved September 20, 2023. <https://ssrn.com/abstract=4262991>.
Rothman, Z. 2023. The Edge of Care [Film]. UBC Faculty of Medicine – Digital Solutions: Education – EdTech. Retrieved September 20, 2023. <https://www.youtube.com/watch?v=0YYMNtnBkis>.
Sackett, D.L., W.M. Rosenberg, J.A. Gray, R.B. Haynes and W.S. Richardson. 1996. Evidence Based Medicine: What It Is and What It Isn't. BMJ 312(7023): 71–72. doi:10.1136/bmj.312.7023.71.
Sanders, C. and A. Rogers. 2007. Theorising Inequalities in the Experience and Management of Chronic Illness: Bringing Social Networks and Social Capital Back in (Critically). In J.J. Kronenfeld, eds., Inequalities and Disparities in Health Care and Health: Concerns of Patients, Providers and Insurers (Vol. 25, pp. 15–42). Emerald Group Publishing Limited.
Tankink, T. 2023, April 26. Ableism at University: Experiencing Student Life with an Invisible Disability [Research master thesis]. Retrieved September 20, 2023. <https://studenttheses.uu.nl/bitstream/handle/20.500.12932/45169/ResMa%20Thesis%20Tara%20Tankink.pdf?sequence=1&isAllowed=y>.
The University of British Columbia. 2023. Community-University Engagement Support (CUES) Fund. Retrieved September 19, 2023. <https://communityengagement.ubc.ca/our-work/cues-fund/>.
Von Korff, M. and B. Tiemens. 2000. Individualized Stepped Care of Chronic Illness. The Western Journal of Medicine 172(2): 133–37. doi:10.1136/ewjm.172.2.133.
White, M., D.J. Guibert Herrera and M.C. Lim-Quizon. 2006. Joined-Up Public-Health Initiatives. The Lancet 367(9519): 1301–02. doi:10.1016/S0140-6736(06)68556-5.
Wieten, S. 2018. Expertise in Evidence-Based Medicine: A Tale of Three Models. Philosophy, Ethics, and Humanities in Medicine 13(1): 2. doi:10.1186/s13010-018-0055-2.
World Health Organization (WHO). 2012. Changing Mindsets: Strategy on Health Policy and Systems Research. Retrieved September 20, 2023. <https://iris.who.int/bitstream/handle/10665/77942/9789241504409_eng.pdf?sequence=1&isAllowed=y>.
Comments
TRUSTWORTHY EVIDENCE TO SUPPORT QUALITY DIGITAL HEALTH HEALTH wrote:
Posted 2024/06/13 at 10:21 AM EDT
This is a commendable, well written article that outlines a well-rounded evaluation and recommendations concerning the use of digital means to address the many healthcare needs surrounding the evaluation and delivery of health services to a large UNDERSERVED community.
Personal Subscriber? Sign In
Note: Please enter a display name. Your email address will not be publically displayed