Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre.

BACKGROUND: Artificial intelligence (AI) technologies are expected to "revolutionise" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital. METHODS: Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. RESULTS: Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors' priorities and the needs and expectations of healthcare organisations and systems. CONCLUSION: Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.

Thematic analysis of tools for health innovators and organisation leaders to develop digital health solutions fit for climate change.

OBJECTIVES: While ethicists have largely underscored the risks raised by digital health solutions that operate with or without artificial intelligence (AI), limited research has addressed the need to also mitigate their environmental footprint and equip health innovators as well as organisation leaders to meet responsibility requirements that go beyond clinical safety, efficacy and ethics. Drawing on the Responsible Innovation in Health framework, this qualitative study asks: (1) what are the practice-oriented tools available for innovators to develop environmentally sustainable digital solutions and (2) how are organisation leaders supposed to support them in this endeavour? METHODS: Focusing on a subset of 34 tools identified through a comprehensive scoping review (health sciences, computer sciences, engineering and social sciences), our qualitative thematic analysis identifies and illustrates how two responsibility principles-environmental sustainability and organisational responsibility-are meant to be put in practice. RESULTS: Guidance to make environmentally sustainable digital solutions is found in 11 tools whereas organisational responsibility is described in 33 tools. The former tools focus on reducing energy and materials consumption as well as pollution and waste production. The latter tools highlight executive roles for data risk management, data ethics and AI ethics. Only four tools translate environmental sustainability issues into tangible organisational responsibilities. CONCLUSIONS: Recognising that key design and development decisions in the digital health industry are largely shaped by market considerations, this study indicates that significant work lies ahead for medical and organisation leaders to support the development of solutions fit for climate change.

Integrating environmental considerations in digital health technology assessment and procurement: Stakeholders' perspectives.

Background: Digital health technologies (DHTs) are promoted as means to reduce the environmental impact of healthcare systems. However, a growing literature is shedding light on the highly polluting nature of the digital industry and how it exacerbates health inequalities. Thus, the environmental footprint of DHTs should be considered when assessing their overall value to healthcare systems. The objectives of this article are to: (1) explore stakeholders' perspectives on integrating the environmental impacts of DHTs in assessment and procurement practices; (2) identify the factors enabling or constraining the operationalisation of such a change; and (3) encourage a constructive dialogue on how environmental issues fit within healthcare systems' push for more DHTs. Methods: Semi-structured interviews were conducted with 29 stakeholders involved in DHTs in a large Canadian academic healthcare centre. Data were collected and analysed through a mixed deductive-inductive process using a framework derived from diffusion of innovations theories. Results: The integration of the environmental impact of DHTs in assessment and procurement is contingent upon key micro-meso-macrosystemic factors that either enable or constrain changes in practices and processes. Innovation (micro) factors include stakeholders' recognition of the environmental issue and the extent to which it is feasible for them to address the environmental impact of DHTs. Organisational (meso) factors include the organisation's culture, leadership, policies, and practices, as well as the expertise and professional skillsets available. Finally, external (macro) factors include political and regulatory (e.g., national strategy, laws, standards, norms), economic (e.g., business models, public procurement), and professional and scientific factors (e.g., evidence, methodologies, clinical guidelines). Conclusion: Considering the environmental impact of DHTs depends on micro-meso-macrosystemic factors involving a variety of stakeholders and levels of governance, sometimes with divergent or even antagonistic objectives and expectations. It highlights the importance of better understanding the complexity inherent in the environmental shift in healthcare.

A Comprehensive, Valid, and Reliable Tool to Assess the Degree of Responsibility of Digital Health Solutions That Operate With or Without Artificial Intelligence: 3-Phase Mixed Methods Study.

BACKGROUND: Clinicians' scope of responsibilities is being steadily transformed by digital health solutions that operate with or without artificial intelligence (DAI solutions). Most tools developed to foster ethical practices lack rigor and do not concurrently capture the health, social, economic, and environmental issues that such solutions raise. OBJECTIVE: To support clinical leadership in this field, we aimed to develop a comprehensive, valid, and reliable tool that measures the responsibility of DAI solutions by adapting the multidimensional and already validated Responsible Innovation in Health Tool. METHODS: We conducted a 3-phase mixed methods study. Relying on a scoping review of available tools, phase 1 (concept mapping) led to a preliminary version of the Responsible DAI solutions Assessment Tool. In phase 2, an international 2-round e-Delphi expert panel rated on a 5-level scale the importance, clarity, and appropriateness of the tool's components. In phase 3, a total of 2 raters independently applied the revised tool to a sample of DAI solutions (n=25), interrater reliability was measured, and final minor changes were made to the tool. RESULTS: The mapping process identified a comprehensive set of responsibility premises, screening criteria, and assessment attributes specific to DAI solutions. e-Delphi experts critically assessed these new components and provided comments to increase content validity (n=293), and after round 2, consensus was reached on 85% (22/26) of the items surveyed. Interrater agreement was substantial for a subcriterion and almost perfect for all other criteria and assessment attributes. CONCLUSIONS: The Responsible DAI solutions Assessment Tool offers a comprehensive, valid, and reliable means of assessing the degree of responsibility of DAI solutions in health. As regulation remains limited, this forward-looking tool has the potential to change practice toward more equitable as well as economically and environmentally sustainable digital health care.

How can entrepreneurs experience inform responsible health innovation policies? A longitudinal case study in Canada and Brazil.

AIM: To foster equity and make health systems economically and environmentally more sustainable, Responsible Innovation in Health (RIH) calls for policy changes advocated by mission-oriented innovation policies. These policies focus, however, on instruments to foster the supply of innovations and neglect health policies that affect their uptake. Our study's aim is to inform policies that can support RIH by gaining insights into RIH-oriented entrepreneurs' experience with the policies that influence both the supply of, and the demand for their innovations. METHODS: We recruited 16 for-profit and not-for-profit organisations engaged in the production of RIH in Brazil and Canada in a longitudinal multiple case study. Our dataset includes three rounds of interviews (n = 48), self-reported data, and fieldnotes. We performed qualitative thematic analyses to identify across-cases patterns. FINDINGS: RIH-oriented entrepreneurs interact with supply side policies that support technology-led solutions because of their economic potential but that are misaligned with societal challenge-led solutions. They navigate demand side policies where market approval and physician incentives largely condition the uptake of technology-led solutions and where emerging policies bring some support to societal challenge-led solutions. Academic intermediaries that bridge supply and demand side policies may facilitate RIH, but our findings point to an overall lack of policy directionality that limits RIH. CONCLUSION: As mission-oriented innovation policies aim to steer innovation towards the tackling of societal challenges, they call for a major shift in the public sector's role. A comprehensive mission-oriented policy approach to RIH requires policy instruments that can align, orchestrate, and reconcile health priorities with a renewed understanding of innovation-led economic development.

An urgent call for the environmental sustainability of health systems: A 'sextuple aim' to care for patients, costs, providers, population equity and the planet.

Health systems have a duty to protect the health and well-being of individuals and populations. Yet, healthcare contributes about 4.6% of global greenhouse gas emissions. Health systems need to question and improve established practices, assume strong environmental leadership, and aim for ambitious, sometimes radical, actions in favour of the climate. In this paper, we interrogate the suitability and feasibility of integrating the aim of 'environmental sustainability' to form the 'Sextuple Aim.' Environmental sustainability may be in tension with, but also a potential lever to meet the other cardinal aims: (1) quality and experience of patient care; (2) population health; (3) quality of work and satisfaction of healthcare providers; (4) equity and inclusion; and (5) cost reduction. We propose policy and practical avenues to help move towards the Sextuple Aim.

Virtual Care and the Inverse Care Law: Implications for Policy, Practice, Research, Public and Patients.

Virtual care spread rapidly at the outbreak of the COVID-19 pandemic. Restricting in-person contact contributed to reducing the spread of infection and saved lives. However, the benefits of virtual care were not evenly distributed within and across social groups, and existing inequalities became exacerbated for those unable to fully access to, or benefit from virtual services. This "perspective" paper discusses the extent to which challenges in virtual care access and use in the context of COVID-19 follow the Inverse Care Law. The latter stipulates that the availability and quality of health care is inversely proportionate to the level of population health needs. We highlight the inequalities affecting some disadvantaged populations' access to, and use of public and private virtual care, and contrast this with a utopian vision of technology as the "solution to everything". In public and universal health systems, the Inverse Care Law may manifests itself in access issues, capacity, and/or lack of perceived benefit to use digital technologies, as well as in data poverty. For commercial "Direct-To-Consumer" services, all of the above may be encouraged via a consumerist (i.e., profit-oriented) approach, limited and episodic services, or the use of low direct cost platforms. With virtual care rapidly growing, we set out ways forward for policy, practice, and research to ensure virtual care benefits for everyone, which include: (1) pay more attention to "capabilities" supporting access and use of virtual care; (2) consider digital technologies as a basic human right that should be automatically taken into account, not only in health policies, but also in social policies; (3) take more seriously the impact of the digital economy on equity, notably through a greater state involvement in co-constructing "public health value" through innovation; and (4) reconsider the dominant digital innovation research paradigm to better recognize the contexts, factors, and conditions that influence access to and use of virtual care by different groups.

Responsible innovation in health and health system sustainability: Insights from health innovators' views and practices.

Although healthcare managers make increasingly difficult decisions about health innovations, the way they may interact with innovators to foster health system sustainability remains underexplored. Drawing on the Responsible Innovation in Health (RIH) framework, this paper analyses interviews (n=37) with Canadian and Brazilian innovators to identify: how they operationalize inclusive design processes; what influences the responsiveness of their innovation to system-level challenges; and how they consider the level and intensity of care required by their innovation. Our qualitative findings indicate that innovators seek to: 1) engage stakeholders at an early ideation stage through context-specific methods combining both formal and informal strategies; 2) address specific system-level benefits but often struggle with the positioning of their solution within the health system; and 3) mitigate staff shortages in specialized care, increase general practitioners' capacity or patients and informal caregivers' autonomy. These findings provide empirical insights on how healthcare managers can promote and organize collaborative processes that harness innovation towards more sustainable health systems. By adopting a RIH-oriented managerial role, they can set in place more inclusive design processes, articulate key system-level challenges, and help innovators adjust the level and intensity of care required by their innovation.

How Can Health Systems Better Prepare for the Next Pandemic? Lessons Learned From the Management of COVID-19 in Quebec (Canada).

The magnitude of the COVID-19 pandemic challenged societies around our globalized world. To contain the spread of the virus, unprecedented and drastic measures and policies were put in place by governments to manage an exceptional health care situation while maintaining other essential services. The responses of many governments showed a lack of preparedness to face this systemic and global health crisis. Drawing on field observations and available data on the first wave of the pandemic (mid-March to mid-May 2020) in Quebec (Canada), this article reviewed and discussed the successes and failures that characterized the management of COVID-19 in this province. Using the framework of Palagyi et al. on system preparedness toward emerging infectious diseases, we described and analyzed in a chronologically and narratively way: (1) how surveillance was structured; (2) how workforce issues were managed; (3) what infrastructures and medical supplies were made available; (4) what communication mechanisms were put in place; (5) what form of governance emerged; and (6) whether trust was established and maintained throughout the crisis. Our findings and observations stress that resilience and ability to adequately respond to a systemic and global crisis depend upon preexisting system-level characteristics and capacities at both the provincial and federal governance levels. By providing recommendations for policy and practice from a learning health system perspective, this paper contributes to the groundwork required for interdisciplinary research and genuine policy discussions to help health systems better prepare for future pandemics.

"It's not just hacking for the sake of it": a qualitative study of health innovators' views on patient-driven open innovations, quality and safety.

BACKGROUND: Open do-it-yourself (DIY) health innovations raise new dilemmas for patient-oriented and service-oriented scholars and healthcare providers. Our study aimed to generate practical insights into quality and safety issues to patient care raised by two volunteer-run, open DIY solutions: Nightscout Project (patient-driven, open-source software for type 1 diabetes management) and e-NABLE (volunteers who design and three-dimensionally print upper-limb assistive devices). To this end, we examined the views of health innovators who are knowledgeable about medical devices standards and regulations. METHODS: We applied a multimedia-based, data-elicitation technique to conduct indepth interviews with a diversified sample of 31 health innovators practising in two Canadian provinces (Quebec and Ontario). An exploratory thematic analysis approach was used to identify respondents' reasoning processes and compare their overall judgements of Nightscout and e-NABLE. RESULTS: Respondents pondered the following quality and safety issues: importance of the need addressed; accessibility; volunteers' ability to develop and maintain a safe solution of good quality; risks involved for users; consequences of not using the solution; and liability. Overall, innovators see Nightscout as a high-risk DIY solution that requires expert involvement and e-NABLE as a low-risk one that fills a hard-to-meet gap. CONCLUSION: Health innovators generally support patient-driven initiatives but also call for the involvement of professionals who possess complementary skills and knowledge. Our findings provide a list of issues healthcare providers may discuss with patients during clinical consultations to document potential risks and benefits of open DIY solutions. To inform new policy approaches, we propose the development of publicly funded umbrella organisations to act as intermediaries between open DIY solutions and regulatory bodies to help them meet quality and safety standards.

Fostering Responsible Innovation in Health: An Evidence-Informed Assessment Tool for Innovation Stakeholders.

BACKGROUND: Responsible innovation in health (RIH) emphasizes the importance of developing technologies that are responsive to system-level challenges and support equitable and sustainable healthcare. To help decision-makers identify whether an innovation fulfills RIH requirements, we developed and validated an evidence-informed assessment tool comprised of 4 inclusion and exclusion criteria, 9 assessment attributes and a scoring system. METHODS: We conducted an inter-rater reliability assessment to establish the extent to which 2 raters agree when applying the RIH Tool to a diversified sample of health innovations (n=25). Following the Tool's 3-step process, sources of information were collected and cross-checked to ensure their clarity and relevance. Ratings were reported independently in a spreadsheet to generate the study's database. To measure inter-rater reliability, we used: a non-adjusted index (percent agreement), a chance-adjusted index (Gwet's AC) and the Pearson's correlation coefficient. Results of the Tool's application to the whole sample of innovations are summarized through descriptive statistics. RESULTS: Our findings show complete agreement for the screening criteria, "almost perfect" agreement for 7 assessment attributes, "substantial" agreement for 2 attributes and "almost perfect" agreement for the RIH overall score. A large portion of the sample obtained high scores for 6 attributes (health relevance, health inequalities, responsiveness, level and intensity of care and frugality) and low scores for 3 attributes (ethical, legal, and social issues [ELSIs], inclusiveness and eco-responsibility). At the rating step, 88% of the innovations had a sufficient number of attributes documented (≥ 7/9), but the assessment was based on sources of moderate to high quality (mean score ≥ 2 points) for 36% of the sample. While "Almost all RIH features" were present for 24% of the innovations (RIH mean score between 4.1-5.0 points), "Many RIH features" were present for 52% of the sample (3.1-4.0 points) and "Few RIH features" were present for 24% of the innovations (2.1-3.0 points). CONCLUSION: By confirming key aspects of the RIH Tool's reliability and applicability, our study brings its development to completion. It can be jointly put into action by innovation stakeholders who want to foster innovations with greater social, economic and environmental value.

Modes of coordination for health technology adoption: Health Technology Assessment agencies and Group Procurement Organizations in a polycentric regulatory regime.

The challenge of novel and high cost health technologies has encouraged the growth of regulatory agencies such as Health Technology Assessment (HTA) organizations and Group Procurement Organizations (GPO). Yet the existence of several agencies in the same polycentric regulatory regime raises questions about whether and how their work can be coordinated. Drawing on a case study of GPOs and HTA agencies across four provinces in Canada, involving document review and key informant interviews (n = 44) conducted between 2013 and 2016, we explore the separate evolution of these agencies, emerging connections between them for non-drug technologies, and the organizational processes and evaluative judgments that underpin coordination efforts. HTA agencies and GPOs developed separately; connections emerged recently in three provinces and suggest four modes of coordination. One mode aligns most closely with that recommended by health economists and HTA practitioners, whereby HTA precedes procurement, with coverage decisions informing technology acquisition. The second mode is a version of the first, where procurement refers cases to HTA for coverage or technology management support; unlike the first, it recognizes procurement's evaluative strengths. Yet both the first and second modes focus on exceptional cases and will be infrequent. The third mode is more systemic, reflecting a generalized complementary of purpose as public agencies. HTA could support GPOs in contested technology acquisition efforts through timely and responsive input, while procurement could expand HTA's impact and inform HTA's growing interest in responsible innovation and environmental sustainability. The final mode is non-coordination, reflecting the potential for agencies to occupy quite distinct regulatory niches within the same regime. We conclude that consistency and convergence around a single model of resource allocation is not inevitable; nor is it necessary for coordinated effort. Thus, where differences in regulatory practice and epistemology persist, mutual accommodation and shared learning may prove most productive.

Organizational readiness for artificial intelligence in health care: insights for decision-making and practice.

PURPOSE: Artificial intelligence (AI) raises many expectations regarding its ability to profoundly transform health care delivery. There is an abundant literature on the technical performance of AI applications in many clinical fields (e.g. radiology, ophthalmology). This article aims to bring forward the importance of studying organizational readiness to integrate AI into health care delivery. DESIGN/METHODOLOGY/APPROACH: The reflection is based on our experience in digital health technologies, diffusion of innovations and healthcare organizations and systems. It provides insights into why and how organizational readiness should be carefully considered. FINDINGS: As an important step to ensure successful integration of AI and avoid unnecessary investments and costly failures, better consideration should be given to: (1) Needs and added-value assessment; (2) Workplace readiness: stakeholder acceptance and engagement; (3) Technology-organization alignment assessment and (4) Business plan: financing and investments. In summary, decision-makers and technology promoters should better address the complexity of AI and understand the systemic challenges raised by its implementation in healthcare organizations and systems. ORIGINALITY/VALUE: Few studies have focused on the organizational issues raised by the integration of AI into clinical routine. The current context is marked by a perplexing gap between the willingness of decision-makers and technology promoters to capitalize on AI applications to improve health care delivery and the reality on the ground, where it is difficult to initiate the changes needed to realize their full benefits while avoiding their negative impacts.

Guiding Pay-As-You-Live Health Insurance Models Toward Responsible Innovation in Health.

While the transition toward digitalized health care and service delivery challenges many publicly and privately funded health systems, patients are already producing a phenomenal amount of data on their health and lifestyle through their personal use of mobile technologies. To extract value from such user-generated data, a new insurance model is emerging called Pay-As-You-Live (PAYL). This model differs from other insurance models by offering to support clients in the management of their health in a more interactive yet directive manner. Despite significant promises for clients, there are critical issues that remain unaddressed, especially as PAYL models can significantly disrupt current collective insurance models and question the social contract in so-called universal and public health systems. In this paper, we discuss the following issues of concern: the quantification of health-related behavior, the burden of proof of compliance, client data privacy, and the potential threat to health insurance models based on risk mutualization. We explore how more responsible health insurance models in the digital health era could be developed, particularly by drawing from the Responsible Innovation in Health framework.

Transforming Disciplinary Traditions Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling".

Grutters et al show that economic assessments can inform the development of new health technologies at an early stage. This is an important contribution to health services and policy research, which implies a "shift away" from the more traditional forms of academic health economic modeling. Because transforming established disciplinary traditions is both valuable and demanding, we invite scholars to further the discussion on how the value of health innovations should be appraised in view of today's societal challenges.

Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries.

The World Health Organization and other institutions are considering Artificial Intelligence (AI) as a technology that can potentially address some health system gaps, especially the reduction of global health inequalities in low- and middle-income countries (LMICs). However, because most AI-based health applications are developed and implemented in high-income countries, their use in LMICs contexts is recent and there is a lack of robust local evaluations to guide decision-making in low-resource settings. After discussing the potential benefits as well as the risks and challenges raised by AI-based health care, we propose five building blocks to guide the development and implementation of more responsible, sustainable, and inclusive AI health care technologies in LMICs.

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity.

Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI's value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.

Rethinking the electronic health record through the quadruple aim: time to align its value with the health system.

Electronic health records (EHRs) are considered as a powerful lever for enabling value-based health systems. However, many challenges to their use persist and some of their unintended negative impacts are increasingly well documented, including the deterioration of work conditions and quality, and increased dissatisfaction of health care providers. The "quadruple aim" consists of improving population health as well as patient and provider experience while reducing costs. Based on this approach, improving the quality of work and well-being of health care providers could help rethinking the implementation of EHRs and also other information technology-based tools and systems, while creating more value for patients, organizations and health systems.