The fall of the innovation empire and its possible rise through open science

Gold, E. Richard

doi:10.1016/j.respol.2021.104226

Cited by 35 publications

(31 citation statements)

References 121 publications

(173 reference statements)

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“…To counter the entrenched, IP-focused approach to biomedical research, the government should support open science approaches to drug and vaccine development ( Gold 2021 ). This should take at least two forms: Firstly, the government should create specific funding programs aimed at those agreeing to abide by open science principles – open data, open materials, open tools, open publications and the absence of IP rights, which restrict others from using those data, materials and tools or building new products.…”

Section: Policy Actions To Improve Global Access To Publicly Funded R...mentioning

confidence: 99%

University Technology Transfer Has Failed to Improve Access to Global Health Products during the COVID-19 Pandemic

Herder¹,

Gold²,

Murthy³

2022

hcpol

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Publicly funded research has contributed enormously to many products that were developed in the face of the COVID-19 pandemic. Yet universities' technology transfer practices have failed to ensure that these products are available in low- and middle-income settings. Drawing upon the example of the lipid nanoparticle delivery technology – which was developed in and around the University of British Columbia in Vancouver, BC, and incorporated into the Pfizer/BioNTech COVID-19 vaccine – we show the divide between the university's stated principles to serve global health and technology transfer in practice. We outline three policy actions to realign universities' technology transfer practices in the service of global health.

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Section: Policy Actions To Improve Global Access To Publicly Funded R...mentioning

confidence: 99%

University Technology Transfer Has Failed to Improve Access to Global Health Products during the COVID-19 Pandemic

Herder¹,

Gold²,

Murthy³

2022

hcpol

View full text Add to dashboard Cite

show abstract

“…Specifically, farmers sharing information on soil type, composition and nutrient availability, land surface phenology, choice of crops, amount of fertilizer used, crop rotations, historical crop yield records and actual yield should all follow open-science data-sharing requirements, specifying the repository and dataset. Addressing ownership issues by democratizing data access and use via standards-compliant repositories is likely to be a foundational aspect of this approach, enabling more open, multi-stakeholder science and technology development 34 . In this context, data-stewardship tools that facilitate agricultural data lakes are essential as global agriculture contends with a deluge of data from multiple sources of varying types.…”

Section: Governance Mechanismsmentioning

confidence: 99%

Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

et al. 2022

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Global agriculture is poised to benefit from the rapid advance and diffusion of artificial intelligence (AI) technologies. AI in agriculture could improve crop management and agricultural productivity through plant phenotyping, rapid diagnosis of plant disease, efficient application of agrochemicals and assistance for growers with location-relevant agronomic advice. However, the ramifications of machine learning (ML) models, expert systems and autonomous machines for farms, farmers and food security are poorly understood and under-appreciated. Here, we consider systemic risk factors of AI in agriculture. Namely, we review risks relating to interoperability, reliability and relevance of agricultural data, unintended socio-ecological consequences resulting from ML models optimized for yields, and safety and security concerns associated with deployment of ML platforms at scale. As a response, we suggest risk-mitigation measures, including inviting rural anthropologists and applied ecologists into the technology design process, applying frameworks for responsible and human-centred innovation, setting data cooperatives for improved data transparency and ownership rights, and initial deployment of agricultural AI in digital sandboxes.

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“…Open science partnerships could further enhance the efficiency of evidence production. 84 Any new process will undoubtedly have unintended impacts. Deadoption decisions will be politically sensitive and HTA agencies may be required to include parameters that are inconsequential to ENBS estimation, but important to patients and sponsors in ROMA.…”

Section: Uncertaintymentioning

confidence: 99%

A Conceptual Framework for Life-Cycle Health Technology Assessment

et al. 2022

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Health technology assessment (HTA) uses evidence appraisal and synthesis with economic evaluation to inform adoption decisions. Standard HTA processes sometimes struggle to (1) support decisions that involve significant uncertainty and (2) encourage continued generation of and adaptation to new evidence. We propose the life-cycle (LC)-HTA framework, addressing these challenges by providing additional tools to decision makers and improving outcomes for all stakeholders.Methods: Under the LC-HTA framework, HTA processes align to LC management. LC-HTA introduces changes in HTA methods to minimize analytic time while optimizing decision certainty. Where decision uncertainty exists, we recommend risk-based pricing and research-oriented managed access (ROMA). Contractual procurement agreements define the terms of reassessment and provide additional decision options to HTA agencies. LC-HTA extends value-of-information methods to inform ROMA agreements, leveraging routine, administrative data, and registries to reduce uncertainty.Results: LC-HTA enables the adoption of high-value high-risk innovations while improving health system sustainability through risk-sharing and reducing uncertainty. Responsiveness to evolving evidence is improved through contractually embedded decision rules to simplify reassessment. ROMA allows conditional adoption to obtain additional information, with confidence that the net value of that adoption decision is positive. Conclusions:The LC-HTA framework improves outcomes for patients, sponsors, and payers. Patients benefit through earlier access to new technologies. Payers increase the value of the technologies they invest in and gain mechanisms to review investments. Sponsors benefit through greater certainty in outcomes related to their investment, swifter access to markets, and greater opportunities to demonstrate value.

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The fall of the innovation empire and its possible rise through open science

Cited by 35 publications

References 121 publications

University Technology Transfer Has Failed to Improve Access to Global Health Products during the COVID-19 Pandemic

University Technology Transfer Has Failed to Improve Access to Global Health Products during the COVID-19 Pandemic

Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

A Conceptual Framework for Life-Cycle Health Technology Assessment

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