Innovation and Our Data: This Does Not Need to Be Scary

Abstract

The rapid emergence of Artificial Intelligence (AI) has unveiled the clinical, public health, and economic value of Canada’s health data assets. However, the Canadian industry developing technologies to improve the standard and quality of Canadian healthcare has limited, and often dysfunctional, access to data. The eighth principle of the Pan-Canadian Health Data Charter calls for data-driven social and technological innovation through partnership, invention, discovery, value creation, and international best practice. Trustworthy and efficient innovation data access is essential for realizing a data ecosystem that improves the health and clinical outcomes for Canadians. This article examines the innovation data access landscape in Canada and identifies structural and cultural challenges. It proposes governance mechanisms in the context of current legislation to address challenges and promote trust in ethical data use while looking internationally for inspiration, and reinforces that achieving the eighth principle is structurally dependant on addressing the other nine in turn.

Introduction

Canada’s disconnected health technology infrastructure is generating data in ever-increasing volume.¹ Yet data are often treated as an artefact of care rather than as a primary functional component of the healthcare system and critical enabler of the quintuple aim of enhancing patient experience, improving population health, reducing costs, improving the work life of providers, and advancing health equity.^2,3 As a result, patients are carrying paper copies of their private information between appointments, medical errors are prevalent, and innovation is diminished, among other harms.^3-6

The consequences of the disarray of our data environment were exposed during the COVID-19 pandemic in stark contrast to our international peers.⁷ Fixing it is starting to receive the attention it deserves.^3,8-10 Data are now recognized as an untapped resource¹¹ (economic/clinical/public health), in large part due to the advent of Artificial Intelligence (AI) in healthcare.¹² AI will transform healthcare through new and improved technologies to diagnose and treat disease.^13,14 It will play a pivotal role in alleviating our overburdened healthcare system and help address increasing provider burnout with tools such as AI scribes.^15,16 It will help shift healthcare from reactive to a more cost-effective (and humane) proactive model where chronic conditions are more effectively managed and emergencies are predicted and avoided.^17,18 It promises to allow more Canadians to age with dignity and comfort in their homes, rather than shuffling our increasingly elderly population through the crowded hallways of acute care.^19,20

AI development requires access to rich datasets, and Canada has some of the most valuable in the world thanks to its single-payer health system and diverse population.²¹ However, it is largely inaccessible for private sector technology development—the “innovation” addressed in this article. There is a culture of caution among healthcare data custodians for sharing data even with trusted partners.⁶ Canadian legislation punishes privacy-related harms, but there are few consequences if data are not shared when they should be.⁵ Highly publicized cases of potential data misuse have elevated reputational concerns.²²

Privacy and reputational concerns do not negate the need for data by Canadian innovators. Data access barriers disrupt collaborations between domestic industry and our world-class research and clinical institutions. They discourage technology adoption, drain resources, cause economic development opportunities to be lost as industry seeks data elsewhere, and elevate the risk of algorithmic bias.²³

AI and innovation can drive system alignment with the principles of the Pan-Canadian Health Data Charter,²⁴ which guides collective action towards a shared vision for health data in Canada. The eighth principle of the Charter calls for “data-driven social and technological innovation through partnership, invention, discovery, value creation, and international best practice.” In this article, we engage in a narrative review exploring the innovation data access environment in Canada, examine governance solutions to preserve privacy and ensure ethical use in the context of legislation, and consider international examples for inspiration. It will become clear through this narrative that achieving the vision of the Charter’s eighth principle requires attending to each of the other nine.

Public Perception

Government and privacy commissioners have largely taken the position of supporting AI healthcare innovation while balancing privacy and other risks.^25-29 However, recent reports suggest less than a third of individual members of the public are comfortable with AI in healthcare, and less consider themselves knowledgeable about its use.^30,31 The Office of the Information and Privacy Commissioner (OIPC) of Alberta found in a recent public engagement survey that 18% of respondents supported the use of health information to train commercial AI products.³¹ Further research suggests mixed public acceptance for sharing data with industry for purposes that could benefit the public good.³²

There is a trust deficit among the Canadian public for sharing data for commercial use.³³ There are good reasons for this.^22,34-36 However, these research findings highlight concerns that could be feasibly addressed, such as security, privacy, community oversight, transparency, and consent. Proposed stewardship approaches to health data in support of person-centric health data design do address these concerns,³⁷ but innovation data access entails risks and challenges requiring focused attention.

Research and Innovation

The terms research and innovation are often conflated in the literature.⁶ “Innovation” is often left to reader interpretation, which may be intentional. Private sector access to sensitive health data can be controversial, even when de-identified (i.e., changing data so that they no longer identify an individual or could, foreseeably, identify an individual in the future³⁸). Conversely, research data access is considered acceptable and Canada supports large research data sharing platforms (see GEMINI,³⁹ the Canadian Institute of Health Information (CIHI),⁴⁰ and the Institute for Clinical Evaluative Sciences (ICES)⁴¹). However, commercial use is mostly prohibited through these platforms. Industry can often access data through academic-industry collaborations. Unfortunately, the needs and pace of industry and academia are often not aligned.⁴² Startups with limited runways cannot afford to wait years to access data to develop and validate their technologies.

Provincial and territorial privacy legislation governing most health data in Canada describes requirements for research data access, such as Research Ethics Board (REB) approval.⁴³ While the definitions of research and innovation are often blurred, in practice there is a meaningful distinction between sharing data for an REB-approved research study and to build an AI product. Legislation largely omits data sharing for this second kind of innovation. In many jurisdictions, if data are de-identified they are no longer considered Personal Health Information (PHI)⁴³ or subject to the protections of health privacy legislation. Therefore, it is often permissible to share de-identified data without informed patient consent (potentially with notification requirements³⁵). Legislative gaps have enabled some custodians to sell de-identified data for targeted marketing.^22,35 This has damaged public trust in custodians, deterred domestic industry data access for legitimate purposes, and potentially contravenes the Charter’s seventh principle of ethical use of health data (de-identified or not).

Decisions on protections needed for sharing de-identified data outside the scope of research have largely been left to custodians. Reputational risk and the threat of large privacy penalties are driving data sharing decision-making.⁵ Lacking a duty to share for public good in legislation, custodians have been disincentivized from sharing data with domestic industry, and many avoid it entirely. Many custodians will not provide access to de-identified data with the private sector for innovation purposes without deploying complex data-protection infrastructure (e.g., Trusted Research Environments (TREs) or federated learning^44-46). Such solutions are important and should be actively pursued. However, there is a real risk of well-resourced hospitals or platforms adopting these solutions with most other custodians unable to participate.

The pace of AI development is measured in weeks and months, not years. With every day that passes, Canada risks falling further behind in commercial AI development to an unrecoverable degree. An acceptable and accessible near-term solution for innovation data access is required.

Enabling Responsible Innovation Data Access

Interoperability

Interoperability is essential for person-centric health data design and the timely availability and accessibility of data.⁴ It would also greatly enable health technology innovation.

Interoperability, common data standards, and a prohibition on data blocking would unlock longitudinal datasets across care settings. It would facilitate industry-academic collaborations and inter-jurisdictional data sharing initiatives. It would lower the barrier to entry for domestic companies, make data usable sooner, and reduce algorithmic bias with larger, more diverse datasets.

At the time of writing, Bill S-5 is progressing through Parliament.⁹ The Connected Care for Canadians Act would prohibit data blocking by health technology vendors and regulate common data standards, which would reduce the significant time and energy wasted by custodians, industry, and even government cleaning data, and would reduce inefficiencies in Canada’s health technology innovation ecosystem.

Social License, Transparency, and Consent

Three of the principal components for social license to share data for innovation are transparency, consent, and literacy.

Transparency must be embedded as the foundational principle for all data sharing in Canada.⁴⁷ For the public to trust their data are being managed responsibly, they must be fully aware of how data are being used, shared, and protected.

Legislated consent requirements are being followed by custodians. However, since PHI about individual members of the public is the source of de-identified data, the public should (and may already) regard de-identified data with a sense of ownership and expectation for ethical use.³² The public should be provided the opportunity to opt out of their PHI being de-identified for secondary use, supported by notification, technology, and infrastructure.⁴⁸ Interoperability would support centralized opt-out for secondary use such as with the National Health Service in England⁴⁹ and My Health Record in Australia.⁵⁰

Literacy is needed to build trust.⁵¹ Public support to share data for AI development is constrained by lack of awareness of the potential benefits of Canadian healthcare innovation and the role that data play in it. Coordinated national and local efforts are needed to increase public and provider literacy about the benefits of sharing data for innovation. The role of data should be clarified while addressing concerns over risks. Social license relies on a fully informed public, which hinges around trustworthy and transparent data practices.^32,51 Trustworthy practices are limited in impact if the public is not aware of them.⁵¹

Lastly, messaging on sharing data with industry is in desperate need of change. When the public is asked about whether data should be shared for commercial purposes, it is usually without context about how such data would be protected or the benefits that may be obtained. The response is understandably and nearly universally “no.” The reality is far more nuanced, but unfortunately decisions on whether to share data with industry are informed by this kind of dialogue, which promotes a culture of apprehension among decision-makers and a public perspective of industry being unethical. In healthcare especially, entrepreneurs are often clinicians and researchers who genuinely want to serve the public good. For each negative story in the media on improper use or protection of data, many more should highlight the good who are treating data with the appropriate care.

Oversight and Community Engagement

Oversight of innovation data access is critical to promoting the Charter’s seventh principle on ethical use, while supporting the fourth principle, which identifies quality, privacy, and security of data as needed to maximize benefits, build trust, and reduce harm to individuals and populations.

Industry sponsored research is a common practice where a company pays for research to be performed by a healthcare institution.⁵² In many cases, data are exchanged.⁵³ A key distinction between sponsored research and sharing data solely for product development is the former explicitly requires REB approval. The implication is if ethical governance solutions were deployed for oversight of innovation data sharing, custodians may be more willing to engage. Hospitals in Canada are not-for-profit entities (which is not the case for many custodians). However, there are increasing budgetary pressures to monetize data. A solution is needed for independent and unbiased oversight and decision-making for sharing data outside the scope of REBs. A data trust is a legal framework whereby a trustee holds property as its nominal owner for the good of one or more beneficiaries.⁵⁴ In this scenario, trustees could be independent entities responsible for innovation data access decisions on behalf of custodians.

When the decision to share is made, appropriate controls must be deployed.⁴⁷ When data are licensed (ie, not sold), ownership stays with the custodian and responsibilities may be conferred to licensees through strong contractual controls, including what the data can be used for and how they are protected. Guidance and principles for responsible data licensing for Canadian healthcare institutions are publicly available and can be readily adopted.⁴⁷

Finally, in keeping with the Charter’s second and third principles, oversight committees should include representative patients and community members. Patients are the most important interest-holders in what their data are used for.⁴³ CIHI’s Data Stewardship program exemplifies how patients and the public can be engaged in the development of governance models.³⁷ Communities must be engaged in oversight and represented in decisions impacting their data and sovereignty. Frameworks such as the First Nations Principles of Ownership, Control, Access, and Possession (OCAP),⁵⁵ CARE Principles for Indigenous Data Governance,⁵⁶ and the black community’s Engagement, Governance, Access, and Protection (EGAP)⁵⁷ should be consulted and respected.

International Inspiration

Principle 10 of the Charter calls for harmonization of governance and oversight across jurisdictions. Canada has historically lacked coordination in its data efforts. Initiatives are often disconnected and duplicated between custodians and across provinces and territories. A solution is required to tie these disparate ecosystems together to enable functional innovation data access without rebuilding our technology infrastructure. We can look for inspiration from international examples.

The European Health Data Space (EHDS)⁵⁸ is a regulatory framework for unifying and harmonizing the health data environment across the European Union. The EHDS is deploying national Health Data Access Bodies (HDABs).^48,59,60 Such regional centres governing secondary use of data are attractive for the federated system of Canada given the degree of autonomy between provinces and territories. This would permit local control of data assets with coordination supported by a higher-level body. Santé Québec’s Research Access Centre is a Canadian example of a regional centre governing secondary use of data established through legislation.^61,62

Processes and agreements are also not harmonized, and custodian legal and fulfilment processes can delay access by months or even years. Innovators seeking data in Canada often need to start fresh with every custodian. The Trusted Exchange Framework and Common Agreement (TEFCA) is the United States’ national health information sharing framework.⁶³ It connects health information networks to enable network-to-network data exchange. TEFCA has adopted common agreements on policies and technology standards which simplify data sharing between networks. A network-of-network model based on common agreements could be deployed in Canada, which would greatly support the sixth principle of the Charter of timely availability and accessibility of health data.

Conclusion

The principles of the Pan-Canadian Health Data Charter should not exist in isolation. They are each needed to achieve a vision of a data environment in Canada that benefits the health and well-being of Canadians. Innovation is an integral part of this vision and must be supported.

As this has article described, governance mechanisms can be leveraged to unlock Canada’s data for innovation in a responsible and transparent manner, supported by legislation and informed by solutions deployed in international jurisdictions.

To quote the Information and Privacy Commissioner of British Columbia²⁷:

We have the opportunity now to build the information society we want through strong independent oversight and transparent, independent and inclusive governance, with legislation that puts the guardrails we need in place.

In an era where Canada is building sovereign data centres and AI capacity,^64,65 controlling who and for what purposes we share our data with is important. Harmonized national, provincial, and territorial innovation data strategies should be deployed, aided by legislation, regulation, and policy. With the right tools and coordinated efforts, trustworthy and responsible data access for innovation is feasible and achievable.

Limitations

Grey literature is used alongside peer-reviewed sources to support the assertions and conclusions of this article, which is a limitation of this work.

Footnotes

Acknowledgements

The authors would like to acknowledge the Government of Canada’s Strategic Response Fund for its support of INOVAIT and the Health Data Sharing and Governance Working Group. INOVAIT is Canada’s national network for image-guided therapy and AI, hosted by Sunnybrook Research Institute.

ORCID iD

Philip Boyer

Ethical Approval

Institutional review board approval was not required.

Funding

INOVAIT is Canada’s national network for image-guided therapy and AI, hosted by Sunnybrook Research Institute and supported by the Government of Canada’s Strategic Response Fund.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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