As medical data have moved to being a solely digital artifact [1] and an increasing amount of health-related data has become available from noninstitutional sources [2], a large number of private businesses have emerged aiming to use these data and to profit from their use [3]. The inherently sensitive nature of medical data and the potential implications of it being misused or misappropriated mean that there is an increased need to constrain the use of this data from privacy, legal, and ethical standpoints [4]. These constraints, though, must be set against the potential of medical data to enable immense benefits in the areas of personal, public, and research-level health [5]. While government-funded or public initiatives to facilitate the use and reuse of medical data can play a role in leveraging secondary use of medical data for the public good, there is still a huge scope for benefit to be gained from allowing or encouraging private third parties to build for-profit businesses based on the consented use of personal medical information [6]. This then produces a natural tension between the benefit (both direct and indirect) of organizations obtaining medical data and associated permission to use it on the one side and a resistance, both on personal and jurisdictional levels, to sharing data on the other. Set alongside this is the need for such businesses to incentivize potential data subjects to consent to the use of their data and to do this in a way that is both ethically and legally sound [7].

Given the legal, ethical, and reputational risks and limitations associated with the use of medical data for profit-making means, the structure of the business models that enable this, and the means by which they navigate these risks, is of interest [8]. The sharing of medical data has the potential to transform health care, enabling better patient care, faster diagnoses, and more effective treatments [9]. However, it also raises important questions around privacy, ownership, and control. In recent years, there has been a proliferation of medical data sharing initiatives, from large-scale public-private partnerships to smaller-scale collaborations between health care providers and tech companies. While these initiatives hold promise for improving health care outcomes, they also raise important legal, ethical, and reputational concerns that must be carefully navigated. The structure of the business models underpinning these initiatives, and the measures taken to manage legal and ethical risks, therefore, warrants closer examination. To gauge the current landscape of medical data sharing initiatives, we have conducted a review, looking at existing medical data sharing initiatives and business models, their structure, and methods of incentivization. This paper presents the results of this survey.

This survey was conducted in the broader context of ongoing efforts to develop innovative and sustainable business models for the use of medical data. These efforts aim to balance the immense potential of medical data to improve health care outcomes with the critical need to address privacy, legal, and ethical concerns. By systematically reviewing and analyzing various medical data sharing initiatives, we look to inform the development of frameworks that ensure ethically sound and legally compliant use of medical data. The focus on blockchain technology reflects a growing interest in leveraging its capabilities to enhance data security and transparency, potentially addressing some of the key challenges in medical data sharing.

As health care data have become predominantly digitized [10,11], there is increasing recognition of the value of digital data in driving innovation in general areas of public health [12], including public responses to the recent COVID-19 epidemic [13] and specific research fields such as genomics [14], dementia [15], and diabetes [16], among others. Alongside the digitization of health care data at the source (eg, within health care institutions), there has been a rise in personal health monitoring and data collection through commercially available wearable devices [17,18]. The growth of informatics infrastructure necessary to process and transport data, enable data linkage across datasets [19], and manage consent and sharing preferences [20] has further enabled third-party entities to use medical data for both public and private use [21].

Given the sensitive and personal nature of medical data, there are additional requirements for data processing and handling, particularly by third parties [22]. Ethical considerations for the use of data [23] and moral concerns as to its use [24], often becoming more limited depending on the specific type of medical data (eg, that relating to sexual health [25,26], mean that, even in conjunction with general limitations on the use of personal data [27], the use of such data is inherently limited. These ethical considerations translate into more firmly enforced legislative constraints [28], placing additional constraints on business models that seek to leverage personal medical data for profit-making purposes [3].

One area of interest in the use of medical data is the emergence of blockchain technologies [29], which offer opportunities for actors in a business transaction to communicate, transact, and share information in an open, tamper-proof, and trustless manner [30]. Blockchain is the underlying technology initially used to implement the Bitcoin cryptocurrency [31]. A blockchain is a data structure that encodes a series of blocks of data in series, with each block containing a cryptographic reference to the previous block in the chain. Combined with a consensus mechanism, which is a protocol for determining among a distributed set of potentially untrustworthy peers which series of blocks is the currently accepted chain, blockchains offer a technological means of sharing canonical sets of information in a trustless manner across a wide range of participants [32]. Blockchain-based systems have been enhanced by the development and application of smart contract technologies, which allow the formation of blockchains to be controlled and enhanced through the use of programmatic functions that control the input and output of transactions and data on-chain [33]. Blockchain technology has been adopted across a wide range of industries [34]. The use of blockchain technologies in the context of medical data sharing initiatives potentially allows for the construction of systems that provide guaranteed openness of data use [29], removal of the need for trusted third parties to coordinate actions such as data linkage [35], and a native layer for offering, processing, and distributing tokens of incentivization [36]. The use of blockchain technology as the underlying mechanism for enabling the sharing and use of personal medical data by third parties is increasingly common at the present time [37].

In the era of data-driven health care, the growth in digital health information offers potential for improving patient outcomes and public health [38]. However, fragmented and siloed health data present significant challenges [39]. Data cooperatives address these challenges by fostering collective sharing, management, and governance of data among diverse stakeholders, including patients, health care providers, researchers, and institutions [40]. These cooperatives promote transparency, privacy, and informed consent, empowering individuals with greater control over their health information while adhering to regulatory and ethical standards [41]. Through collaboration and data pooling, data cooperatives bridge the gap between data availability and research, enabling comprehensive datasets for innovative medical research and evidence-based policymaking, driving improvements in patient outcomes and public health [42]. Data cooperatives differ from traditional data management approaches in several key ways. They operate on principles of inclusivity and collaboration, involving a wide range of stakeholders in the data sharing process. Patients have a central role, ensuring the protection of their privacy rights and determining access to their health information [43]. Advanced technologies and standardized protocols are used to ensure data interoperability, enhancing the quality and comprehensiveness of shared data [44]. Governance structures involve representatives from legal, ethical, and technical fields to develop policies that safeguard data integrity and security while fostering trust among participants [45].

In health care, data cooperatives can revolutionize the sharing, access, and use of sensitive medical information. By pooling diverse health datasets, cooperatives provide comprehensive views of patient medical histories, aiding health care professionals in making informed decisions about diagnosis, treatment, and care plans. Collaborative research efforts are facilitated by access to large and diverse datasets, promoting discoveries and advancements in medical science. Patients benefit from improved health outcomes by gaining control over their data and ensuring its protection [22]. Data cooperatives enhance population health initiatives by supporting data-driven policy decisions and public health interventions, advancing health care systems, and contributing to improved health outcomes on a broader scale. Despite their collaborative nature, viable business models are still needed to support the operation and sustainability of these cooperatives.

The Civic Data Identity Partnership (CDIP) project aimed to develop a technical platform to manage access to health data. The primary objective was to create an infrastructure that would allow citizens to see, control, and provide consent for the usage of their health data in research and decision-making processes. One of the aims of CDIP was to explore the viability of business models that support the sustainability of the platform [46].

To achieve these aims, the project engaged in consultations with a diverse range of stakeholders, including citizens, clinicians, medical personnel, and data commissioners. These consultations were crucial in identifying the technical, legal, social, and ethical challenges associated with using the CDIP platform to enhance access and control over health data. The project aimed to understand the level of control individuals desired over their health information sharing and the data requirements of other parties. The collaborative efforts of the CDIP project provided insights into the complex landscape of health data management. Stakeholder consultations addressed technical intricacies, legal implications, societal concerns, and ethical considerations [46]. By incorporating these perspectives, the project aimed to design a platform that integrated access and control of health data, fostering transparency, privacy, and informed decision-making. Ultimately, the CDIP project sought to establish an infrastructure that met the preferences and requirements of citizens, clinicians, and other key stakeholders, enabling them to leverage health data while maintaining autonomy over its usage [47].

This study aimed to examine a selected set of blockchain-based medical data sharing initiatives, analyzing their governance structures, incentive mechanisms, ownership models, business approaches, transaction mechanisms, and sustainability strategies. The objective was to identify common characteristics and contextual factors that may influence their development and persistence.

The specific objectives were:

In addition to categorizing and analyzing blockchain-based medical data sharing initiatives, the study included a longitudinal assessment to examine changes in their operational status. This involved tracking whether previously identified initiatives remained active and whether they had evolved in terms of governance, business strategies, or technical implementation. To conduct this assessment, publicly available sources—including websites, white papers, and social media updates—were reviewed, and project teams were contacted where possible to gather additional insights. The objective was to better understand the factors influencing the stability and evolution of these initiatives over time. By examining the characteristics and status of blockchain-based medical data sharing initiatives, this study aimed to contribute to ongoing discussions around their feasibility, governance, and sustainability. The findings provide insights for stakeholders considering the adoption of blockchain in medical data sharing and highlight areas for future research.

Our search found 42 initiatives related to medical data sharing that were identified through the 2-stage methodology outlined in the previous section. Each initiative was systematically screened and analyzed along different dimensions to categorize them based on various characteristics. The resulting table provides an overview of the initiatives and their features, including information on their country of origin, the type of blockchain technology used, incentives for citizen participation, ownership structure, governance, business model, transaction model, sustainability, and whether they are public, private, or hybrid blockchains. A description of each initiative is provided in Table 1, highlighting key features and characteristics of each project.

The analysis of configurational patterns was conducted using the QCA package in R (The R Foundation), which provides a well-documented and widely used framework for performing QCA [51]. Specifically, fsQCA was applied to analyze configurations of conditions leading to the observed outcomes [52]. Unlike crisp-set QCA, which requires binary presence or absence of conditions, or mvQCA, which assigns discrete categorical values, fsQCA allows for graded membership scores between 0 and 1. This flexibility made it well-suited for capturing the continuous nature of governance models, transaction mechanisms, incentive structures, and ownership types observed in the dataset.

The minimization process was conducted using the Quine-McCluskey algorithm [53], which systematically reduces the truth table into a set of minimal sufficient conditions by identifying prime implicants—combinations of conditions that consistently lead to the outcome. This method provides an exhaustive and deterministic approach to simplification, contrasting with heuristic-based solutions that rely on approximation.

A central consideration in fsQCA is equifinality, the principle that multiple distinct configurations can lead to the same outcome. By applying fsQCA, the analysis accounted for the possibility that different combinations of governance models, transaction mechanisms, incentive structures, and ownership types could all contribute to similar sustainability or operational outcomes. This approach enabled a comparative yet flexible assessment of how initiatives maintain viability over time.

To assess the robustness of the findings, a sensitivity analysis was conducted by systematically varying the inclusion threshold, which determines the minimum consistency score required for a configuration to be considered sufficient for the outcome. The analysis tested inclusion thresholds between 0.60 and 0.80, examining whether the identified configurations remained stable or changed across different threshold settings. This step helped evaluate the reliability of the results and detect marginal cases that may influence the solutions.

The analysis was implemented in R, using scripts that preprocessed the data, calibrated conditions, constructed the truth table, and performed the minimization process. The resulting configurations were analyzed to provide insight into the structural pathways associated with initiative status in the follow-up assessment.

The analysis identified multiple configurations of conditions associated with sustained initiative activity. The base model, run at an inclusion threshold of 0.65, produced two primary solutions, each representing distinct pathways leading to continued operation. The presence of multiple valid configurations supports the principle of equifinality, indicating that different combinations of governance, ownership, business, incentive, and transaction models can contribute to sustained activity. The solutions demonstrated that initiatives without a strong ownership structure could persist if supported by governance mechanisms, suggesting that centralized control is not always necessary when alternative oversight structures are in place. Similarly, the absence of a defined business model in several configurations indicates that some initiatives operate successfully without a clear commercial framework, relying instead on cooperative models, external funding, or nonmonetary incentives.

The analysis also highlighted the role of financial incentives and sustainability planning in initiative survival. Some pathways included sustainability mechanisms and incentive structures, reinforcing the idea that structured support mechanisms contribute to long-term viability. However, other solutions demonstrated that initiatives without explicit financial incentives or sustainability frameworks could still maintain activity, particularly if supported by effective governance and transaction models. This suggests that while financial and sustainability considerations may enhance stability, they are not universally necessary for success.

The sensitivity analysis, conducted across multiple inclusion thresholds, provided further insight into the robustness of these findings. At lower thresholds, a broader range of configurations was identified, capturing a wider set of conditions leading to sustained initiative status. As the threshold increased, the number of valid solutions decreased, with only the most consistent configurations remaining at the highest inclusion levels. This pattern indicates that while multiple pathways can lead to continued activity, only a subset demonstrates the strongest and most consistent relationships between conditions and outcomes.

At higher thresholds, the solutions became more restrictive, eliminating cases with lower consistency scores. By the highest inclusion level of 0.80, only two configurations remained, representing the most reliable pathways associated with sustained initiative activity. This suggests that while various factors contribute to initiative sustainability, only a few consistently demonstrate strong explanatory power. The reduction in configurations at higher thresholds reinforces the importance of governance structures and transaction mechanisms as key determinants of continued operation, particularly when financial incentives and sustainability planning are absent.

The methodology used in this study followed a structured multistage approach. First, a snowball sampling technique was used to identify relevant medical data sharing initiatives, with a particular focus on governance, ownership, transaction models, and sustainability. This approach allowed for the inclusion of initiatives that may not be widely documented in academic literature but were actively discussed in industry reports, online databases, and open-source repositories. The initial set of initiatives was identified through keyword searches and industry reports, and additional initiatives were identified through references, expert consultations, and monitoring relevant discussions.

Following identification, each initiative was systematically analyzed across predefined factors, including governance structures, ownership models, business and transaction mechanisms, incentive structures, and sustainability approaches. These attributes were extracted from publicly available documentation, technical white papers, organizational policies, and, where possible, direct engagement with initiative representatives. The initiatives were then categorized using multivalue attributes to facilitate structured comparative analysis.

To assess the operational status of the initiatives, a follow-up analysis was conducted between December 2023 and March 2024. This verification process included reviewing official websites, press releases, regulatory filings, industry reports, and online community discussions. Initiatives were classified as either active or inactive, depending on the presence of ongoing operations, product updates, or sustained engagement.

Finally, fsQCA was applied to identify configurational relationships between the different factors and sustained initiative activity. This approach allowed for an analysis of multiple pathways leading to continued operation rather than assuming a single linear relationship. By systematically comparing different configurations of governance, ownership, incentives, and transaction mechanisms, the analysis identified the strongest structural factors associated with long-term initiative viability.

While the use of snowball sampling can introduce certain biases, it was particularly useful in this context due to the dynamic and rapidly evolving nature of the field of medical data sharing. Many initiatives in this space are emerging and may not be widely documented in traditional academic databases or directories. By consulting a range of sources and engaging with experts and stakeholders in the field, we aimed to capture a comprehensive and nuanced understanding of the current landscape of medical data sharing initiatives. This approach ensured that the review included as many relevant initiatives as possible, even those less visible or established, providing a thorough and representative survey of the field.

The use of fsQCA provided a structured approach to examining the relationships between organizational factors and initiative sustainability. In contrast to variable-based statistical methods, fsQCA identifies multiple configurations leading to an outcome, allowing for the identification of equifinality, where different sets of conditions can result in similar outcomes. This approach was suited to the diverse characteristics of the initiatives studied, which varied across governance structures, business models, and sustainability mechanisms. The calibration of categorical variables into fuzzy-set membership scores allowed for degrees of presence or absence rather than strict binary classifications. A sensitivity analysis across multiple inclusion thresholds was conducted to assess the consistency of the results. This allowed for a structured analysis of relationships between factors while maintaining a focus on individual initiatives.

The classification of initiatives by governance, ownership, business, transaction, incentive, sustainability models, and blockchain usage provided a structured basis for analyzing patterns within medical data sharing initiatives. Governance models varied from centralized board-based structures to decentralized user-regulated approaches, reflecting differing levels of oversight and participant control. Ownership structures showed a predominance of private and cooperative models, with some initiatives operating as public-private partnerships or under government control. Business models highlighted distinctions between for-profit, nonprofit, and cooperative models, emphasizing different economic strategies for sustainability. Transaction models ranged from direct payments and tokenized systems to reward-based mechanisms and freely accessible services, demonstrating diverse approaches to financial interactions. Incentive structures incorporated self-interest, financial compensation, and altruistic motivations, illustrating how initiatives attract participation. Sustainability models varied widely, including membership fees, data sales, investment funding, and subscription-based services, indicating different strategies for long-term viability. Blockchain usage was primarily concentrated in initiatives aiming to enhance security, transparency, and data ownership, though some projects operated without blockchain-based frameworks. These classifications allowed for a comparative analysis of how different structural and economic factors contribute to the continued operation and impact of medical data sharing initiatives.

The results of the analysis highlight several key factors associated with the continued activity of medical data sharing initiatives. Governance structures and ownership models emerged as significant influences, with initiatives that incorporated structured governance mechanisms—such as cooperative models, regulatory compliance, or board oversight—more frequently sustaining operations over time. While strong ownership models were not universally necessary, the presence of governance frameworks appeared to compensate for weaker ownership structures. This suggests that clearly defined decision-making and accountability mechanisms contribute to long-term viability, even in the absence of centralized control.

Financial sustainability and incentive structures also played a role in distinguishing active initiatives. The findings indicate that a well-defined business model is not always essential, as some initiatives without clear revenue-generating mechanisms remained operational. However, those that did sustain themselves often incorporated diverse funding strategies, including membership fees, data access sales, or external investment. Incentive models varied widely, with some initiatives relying on direct financial compensation for participation, while others leveraged nonfinancial motivations such as improved health care access or research contributions. The variability in sustainability and incentive strategies suggests that no single approach guarantees success, but initiatives that align their financial model with stakeholder expectations and engagement mechanisms are more likely to persist. Finally, the analysis demonstrated that transaction models, particularly those leveraging token economies or alternative financial structures, were not necessarily a determinant of success but functioned as supporting mechanisms that could enhance participation and sustainability when combined with effective governance and incentive models.

Several limitations should be considered when interpreting these findings. The study analyzed a selective set of initiatives identified through snowball sampling and available documentation. While this approach allowed for a broad survey of relevant initiatives, it may have introduced selection biases, as less visible or undocumented initiatives could have been excluded. The reliance on publicly available information also meant that classification was sometimes based on inference rather than direct confirmation, particularly for governance structures and financial sustainability models. This introduces potential uncertainty, as internal decision-making processes and strategic priorities may not have been fully captured.

Additionally, the rapidly evolving nature of the space means that some findings may not remain applicable over time. New governance models, technological advancements, and shifts in regulatory frameworks could alter the viability of different configurations. Methodologically, QCA is sensitive to case selection and threshold settings, meaning that small changes in the dataset or inclusion criteria could influence the resulting configurations. While sensitivity analysis helped assess the robustness of the findings, the results should be understood as identifying patterns rather than deterministic causal relationships. Expanding the dataset to include a wider range of initiatives, including those without blockchain integration, could provide a more comprehensive perspective on the factors influencing long-term sustainability.

Future research could build on this study by conducting longitudinal analyses to track how governance and business models evolve over time and how they impact long-term viability. Additional qualitative research could provide deeper insights into decision-making processes within initiatives, particularly regarding governance and financial sustainability. Interviews with key stakeholders could help validate the findings and offer further context on the mechanisms driving success or failure.

Further studies could also explore the role of emerging technologies, such as AI-driven data management and decentralized identity frameworks, in shaping the future of medical data sharing. Investigating how different regulatory environments influence the adoption and sustainability of these initiatives would also be valuable. Finally, expanding the dataset to include a broader range of initiatives, particularly those outside blockchain-based models, could provide a more comprehensive understanding of the factors influencing success in the medical data sharing ecosystem.

Our findings highlight the diverse governance, ownership, and financial sustainability models adopted by medical data sharing initiatives. The results suggest that no single factor determines long-term viability, but rather that different configurations of governance structures, business models, and incentives can contribute to sustained activity. Initiatives with centralized governance structures and clearly defined financial sustainability strategies were more likely to remain active. However, the presence of decentralized governance models and alternative funding mechanisms also demonstrated viability, particularly in cooperative and blockchain-based initiatives. The findings reinforce the importance of aligning governance structures with financial models to ensure long-term sustainability while maintaining transparency and user trust.

The analysis also suggests that financial incentives and transaction mechanisms play varying roles depending on the broader structural configuration of an initiative. While some successful initiatives relied on direct financial incentives, others sustained engagement through cooperative governance, shared decision-making, or intrinsic motivations such as access to health insights. This variability underscores the need for initiatives to tailor their incentive structures to their specific context, balancing financial sustainability with participant engagement and ethical data sharing practices.