Data as medicine’s backbone: redefining its value to foster innovation in the data economy

The effort of data generation and the asymmetry of value

At the core of the medical data economy lies a fundamental challenge: while data drive scientific progress, their collection and maintenance require significant financial and human resources. Hospitals and research institutions invest heavily in collecting, curating, annotating and analysing vast amounts of medical data, all while complying with strict regulatory and ethical requirements. These processes demand advanced technology, skilled personnel and secure digital infrastructures, yet the financial burden of it is often disproportionately shouldered by public institutions and healthcare providers.6

Despite these substantial investments, the economic returns from medical data are often realised much later and largely benefit private sector entities which commercialise insights through pharmaceuticals, medical devices or AI-driven diagnostics.7 This creates an inherent imbalance: while data originators bear the initial effort, financial rewards accrue downstream, where companies leverage refined datasets for product development and monetisation.

This pattern reflects a broader dynamic in the biomedical innovation pipeline, where public research institutions frequently contribute foundational knowledge and infrastructure in early-stage, non-commercial discovery, while private-sector actors engage in later-stage development with commercial potential, regulatory approval and market delivery.

This misalignment threatens the long-term sustainability of data collection efforts, raises ethical concerns about equitable benefit distribution, and hinders value creation across industry boundaries through establishing a true data economy, leveraging the tight business network required to serve the patient8 (see figure 1).

A major issue is the lack of structured mechanisms to ensure fair compensation for those generating and maintaining data. Publicly funded research institutions, which form the backbone of data collection, often lack capacity, budget, and formal pathways to share in the financial gains derived from their contributions. This mirrors broader issues in academic research, where universities drive fundamental discoveries, while their innovations often benefit the private sector.9 10

To address this challenge in a sustainable manner, innovative compensation models are needed.11 Revenue-sharing frameworks, licensing agreements that acknowledge data stewardship, and policies mandating reinvestment in data-generating institutions could help correct the imbalance. Additionally, well-structured public-private partnerships could redistribute benefits more equitably, ensuring that commercial gains contribute to sustaining data collection efforts and fuelling continued innovation.

Having such compensation models available will further enable the exchange of data for commercial use across different players and industries in the network, from data owners who do not have immediate use cases for the data they hold to potential users who could create additional value by developing new business models around such data within their own domains. These compensation models act as intermediaries, ensuring fair distribution of the economic value created and reducing friction in data transactions.

This mirrors the role of money in trade: just as money serves as an intermediary that enables transactions without requiring a direct exchange, structured compensation models allow data to flow efficiently, ensuring equitable benefits for all participants.12

If these economic disparities are not tackled, we risk a future where the institutions responsible for the most labour-intensive and costly aspects of data handling in the early phases of the value chain are unable to sustain their efforts. This could lead to diminished access to high-quality data sets but also to compromised data quality, reduced collaboration, weakened incentives for innovation, and growing inequities across the research and healthcare ecosystem, ultimately stalling or even breaking progress in medical research and innovation, not only within a single industry but also other entities within the related network.

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