Who Owns Your Data? Reclaiming Ownership in a Data-Driven World

Regardless of your background, economic status, education, or job, we are all medical consumers. As you read this piece, step back into that primary role and think of how you would want this to be, not what it is.

Data is the lifeblood of the modern digital economy. From health records to browsing histories, the data generated by individuals fuels innovation, informs policies, and drives profits for corporations worldwide. But as discussions around the monetization of personal data intensify, one question looms large: Who truly owns this data, and who benefits from its use? #DataOwnership #DigitalEconomy

Key Ethical Principles

1. Ownership: Individuals generate the data; they should retain ownership and the ability to control how it’s used.
2. Consent: Explicit, informed consent should be mandatory before any data is shared or sold, even in anonymized forms.
3. Equity: If the data has value to corporations or organizations, the individuals providing it should share in that value.

??Complexity: When Others Contribute to Data. For instance, when a person enters a doctor’s office and the physician evaluates and contributes information and informed medical opinion into a medical record documented in Epic whose data is it? The current model often sweeps this all into the institution. Is that where the ownership resides? What if an individual company owns it? 亚马逊 This question highlights the nuanced nature of data ownership in collaborative environments and suggests a more equitable framework. In healthcare, there are multiple small practices and organizations all of whom collect data on the individuals they treat. This means your data is exposed, over and over again, to misuse and leakage.

These principles are foundational to a fair and transparent data economy. Yet, the current landscape reveals significant shortcomings and complexity which evolved as the technology came on line and often without significant scrutiny or transparency. #DataEthics #Transparency

The Problem with the Current Approach

1. Anonymization Concerns: Even anonymized data can often be re-identified, particularly in large, rich datasets. What risk does this expose, when data is being "scraped" and combined with thousands? Perhaps less concern, however when it is used to tune the content to your personal characteristics?
2. Lack of Individual Benefit: People whose data is sold rarely see any direct financial or other benefit from its use save to enrich others. Does healthcare engender a more nuanced approach, since the data is more sensitive, than preferences for marketing purposes?
3. Erosion of Trust: Selling data without adequate transparency and compensation risks alienating the public and undermining trust in institutions. Generally trust of people around health has been eroding, is this a part of that evolving problem?

Consider the NHS’s proposal to sell anonymized health data. While the intent may be to advance medical research and innovation, this approach disregards the rights and interests of the individuals whose data forms the backbone of these datasets. This is not just a privacy issue; it’s a matter of equity and justice. #HealthData #PrivacyRights

A Proposal for Paid, Consent-Based Data Sharing

A system where individuals are compensated for the use of their data could address many of these concerns. Here’s how it could work:

1. Explicit Consent: Every individual is given clear, accessible information about how their data will be used and asked to approve or deny its use for each specific purpose. #Consent
2. Compensation Framework: Each "data episode" (e.g., a hospital visit, test result, or prescription) would have a small, fixed reimbursement (e.g., ￡0.05–￡0.10), paid to the individual. #Compensation
3. Value Transparency: Individuals could see how much their data has been accessed and used over time and understand the cumulative benefit. #Value #Transparency
4. Richer, Full-Range Data: When people are paid and involved, they are more likely to agree to share comprehensive, detailed data. Although we often think of "big data" as the be all and end all of data, and it is sufficient for some big questions, it actually has not been often achieved. The largest data sets in medicine (pharmaceutical trials) are usually only 5,000 people. In fact, with health progressing to individual treatments, large data may be less useful than personalized data, i.e. smaller and more specific learnings from unique groups of individuals. #FairDataUse #DataTransparency

The Gift of Data for Public Good

In addition to monetary compensation, individuals could have the option to "gift" their data for specific purposes, such as advancing research for rare diseases, supporting nonprofit organizations, or contributing to public health initiatives. This approach would:

• Empower individuals to direct their data toward causes they care about.
• Strengthen the role of nonprofits in leveraging data for societal benefit.
• Foster a sense of shared responsibility and collective impact.

By allowing people to choose how their data is used—whether for personal gain or altruistic purposes—this model balances individual rights with societal benefits. #PublicHealth #DataForGood

Benefits of This Approach

This approach empowers individuals by placing them at the center of decisions about their data. By giving people direct control over how their information is used, this model acknowledges their role as both the creators and stewards of their personal data. It also engenders responsibility for care taking the data. In doing so, it fosters a sense of ownership and agency that is often missing in the current data economy. #Empowerment #DataRights

Beyond empowerment, this framework creates data value equity. A portion of the profits generated from the use of personal data is returned to the individuals who provided it. This redistribution not only aligns with principles of fairness but also ensures that people benefit directly from the economic value their data generates. #EquityInData

Engaging individuals in this way enhances data quality. When people are compensated and involved, they are more likely to provide comprehensive and accurate information. This richer dataset may be more valuable and impactful for research, innovation, and policy-making. #DataQuality #Innovation

Transparency and fair compensation also build public trust. By establishing clear and ethical processes, institutions can demonstrate their commitment to respecting individual rights, fostering confidence among stakeholders and the public. #TrustInData

How to Present This Proposal

To garner support for this proposal, it must be framed around fairness. The emphasis should be on equity and the moral imperative that individuals deserve to benefit directly from the use of their personal information. This reframing positions the approach as a rights-based solution to a systemic issue. #DataEquity #Fairness

???Public engagement should be highlighted as a cornerstone of this model. Demonstrating how these systems would enhance trust in institutions like the NHS England can help gain public and political buy-in. This begs the question of how Medicare Centers for Medicare & Medicaid Services handle this same issue? Are they already sharing your data with other parties? Has that been transparent? Showcasing the tangible economic impact—such as richer datasets and better health outcomes—further strengthens the case for this approach while illustrating how profits can be fairly distributed. #PublicEngagement #EconomicImpact

Collaboration with advocacy groups is essential. Partnering with organizations focused on digital rights, data privacy, and healthcare equity can amplify the proposal’s reach and credibility, ensuring it resonates with both policymakers and the public. #Advocacy #DigitalRights

??Challenges to Address

Implementing this system will undoubtedly require overcoming several challenges. First, the costs associated with developing and maintaining a consent and reimbursement framework could be significant. Institutions must be prepared to invest in the necessary infrastructure to ensure smooth operation. #ImplementationCosts

Corporate pushback is another potential obstacle. Companies that have historically enjoyed free or cheap access to data may resist these changes. Advocates for this model must be ready to address these concerns and make a compelling case for why this approach benefits all stakeholders in the long run. #CorporateResponsibility

Finally, regulatory complexity cannot be ignored. Aligning this proposal with existing laws, such as GDPR in the UK, EU, and US will require careful planning and legal expertise. However, these hurdles are not insurmountable, especially with a well-structured plan and strong advocacy. #DataRegulations #GDPR

As a medical consumer, where do you stand on the issue of data value equity in healthcare? #ArchitectingFutureHealth