Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Privacy protections to encourage use of health-relevant digital data in a learning health system

Abstract

The National Academy of Medicine has long advocated for a “learning healthcare system” that produces constantly updated reference data during the care process. Moving toward a rapid learning system to solve intractable problems in health demands a balance between protecting patients and making data available to improve health and health care. Public concerns in the U.S. about privacy and the potential for unethical or harmful uses of this data, if not proactively addressed, could upset this balance. New federal laws prioritize sharing health data, including with patient digital tools. U.S. health privacy laws do not cover data collected by many consumer digital technologies and have not been updated to address concerns about the entry of large technology companies into health care. Further, there is increasing recognition that many classes of data not traditionally considered to be healthcare-related, for example consumer credit histories, are indeed predictive of health status and outcomes. We propose a multi-pronged approach to protecting health-relevant data while promoting and supporting beneficial uses and disclosures to improve health and health care for individuals and populations. Such protections should apply to entities collecting health-relevant data regardless of whether they are covered by federal health privacy laws. We focus largely on privacy but also address protections against harms as a critical component of a comprehensive approach to governing health-relevant data. U.S. policymakers and regulators should consider these recommendations in crafting privacy bills and rules. However, our recommendations also can inform best practices even in the absence of new federal requirements.

Introduction

The National Academy of Medicine has long advocated for a “learning healthcare system” that produces constantly updated reference data during the care process1. Moving toward a rapid learning system to solve intractable problems in health demands a balance between protecting patients and making data available to improve health and health care. Since much of what impacts an individual’s health and wellbeing occurs outside of a doctor’s office or hospital2, a rapid learning health system also requires data generated outside of traditional healthcare.

This paper comprehensively explores the growing U.S. health data landscape and the privacy risks and innovation obstacles raised by under-regulation of this data. We propose a multi-pronged approach to protecting “health relevant” data while promoting and supporting beneficial uses and disclosures essential to improving health and health care. A roadmap of the key points explored in this paper can be found at Box 1.

Defining health data

Though traditionally, the term “health data” has referred to information produced and stored by healthcare provider organizations, vast amounts of health-relevant data are collected from individuals and entities elsewhere, both passively and actively. Much of data beyond Category 1 in Box 2 is outside of the scope of comprehensive health privacy laws in the U.S.

Recently, concerns about whether existing privacy laws provide sufficient protections for health-relevant data have motivated Congress and state legislatures to propose legislation to fill the gaps3. These concerns have been exacerbated by revelations about how the largest information technology companies collect, use, and share personal data4,5 and these companies’ increasing activities in health care6.

The tension between protecting privacy while promoting more widespread access to health-relevant data is not new. Data produced by the healthcare system (Category 1) has been difficult to access and marshal for health reform, to protect public health, to underpin discoveries, or to expand the evidence base for health and wellness interventions7. Yet recent new federal initiatives aimed at increasing access to Category 1 data—particularly with respect to sharing this data with consumer-facing applications—were met with fierce resistance as privacy concerns were raised8.

At the same time, nontraditional health-relevant data (Categories 2–4), often equally revealing of health status, are in widespread commercial use and, in the hands of commercial companies, largely unregulated—yet often less accessible by providers, patients and public health for improving individual and population health9. The following examples illustrate this tension. There is increasing recognition that social determinants of health (sometimes discernable from data in Categories 3–4) can be highly influential in health and wellness and the costs of both2,10. On the one hand, these data are sensitive because of stigma, health, and financial implications associated with having limited resources. On the other hand, they could be used to improve the health of individuals by identifying those most in need of supports and services. However, in the absence of controls companies could use these data perversely, for example, to avoid locating in neighborhoods perceived to be more costly, or to avoid insuring populations with the highest risks from social determinants. Other types of data in Categories 3 and 4 may not on the surface appear to be health-relevant but could be used to make powerful inferences about an individual’s or a population’s health, e.g. homeownership and job status are predictive of medication adherence11. Such information either could be used to target resources toward improving adherence or to penalize individuals unlikely to take their medications.

To date, U.S. laws governing health data and new legislative proposals tend to focus more on privacy by limiting or controlling access to health-relevant data than on assuring its availability for uses that could improve individual and population health. Lacking are multifaceted policy solutions incorporating protections for health-relevant data while stimulating and encouraging responsible uses for transforming healthcare into a more data-driven enterprise. Necessary protections for health-relevant data also must go beyond a pure privacy focus and extend to preventing or penalizing uses that could harm individuals and populations. Here, we address both privacy protections but also potential data-related harms as a critical component of a comprehensive approach to governing health-relevant data.

U.S. Federal privacy protections for health-relevant personal data

The limitations of HIPAA

The privacy, security, and breach notification regulations promulgated under the Health Insurance Portability and Accountability Act of 1996 (HIPAA)12 and the Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH)13 provide a comprehensive set of protections—but only for data within the health care system. Like many U.S. laws, HIPAA is a sectoral law, covering only certain types of entities. For the most part, HIPAA does not extend to organizations and businesses outside of the traditional health care ecosystem. (See Supplementary Discussion for a brief overview of HIPAA and other laws governing data in the health care system.) Though many may think of HIPAA as only applying to Category 1 data, once health-relevant data—in any of the four categories in Box 2—are collected within an entity covered by HIPAA, those data will be covered by HIPAA’s protections. Specifically, all information that is identifiable “protected health information” is covered14, and this includes information that may not look like health data (such as in Category 4) but is used in a way that makes it “related to health and health care.” However, since HIPAA’s coverage is about “who” holds the data, but not what type of data, much of the health-relevant data collected today is collected by entities outside of HIPAA’s coverage bubble and thus resides outside of HIPAA’s protections15,16,17.

An increasingly important example of information leaving HIPAA’s coverage is when a consumer uses a third party health application (app) to obtain Category 1 data for personal use. Health apps used by consumers are frequently hosted by third parties and may share data further, with little transparency to users. Most are not covered by HIPAA. An analysis of 10 apps (two of them intended to enable women to track menstrual cycles and predict ovulation times) found they transmitted data on user activities in the app to 70 different third parties involved in advertising and profiling, without explicit consent from the users18. Another study examining 14 health and nutrition apps, including apps tracking medication use, migraines, and sleep, and some helping to manage diabetes, found that all but one (the Apple Health App) shared data with third parties without full transparency to the user19. A cross-sectional study of 36 apps for depression and smoking-cessation researchers found that 29 transmitted data to services provided by Facebook or Google, but only 12 accurately disclosed this in a privacy policy20.

Privacy concerns over consumer apps nearly halted new federal initiatives that require health care providers and health plans to share more health information with patients. As directed by the 21st Century Cures Act21, the federal Office of the National Coordinator for Health IT (ONC) recently finalized rules prohibiting “information blocking,” which includes the failure to share health information with individuals or health apps chosen by those individuals22. ONC’s rules also require certified electronic health records (EHRs) used by health care providers to offer open, standard application programming interfaces (APIs), specifically the SMART on FHIR API23 and the SMART/HL7 Bulk Data API24,25, to facilitate seamless digital data sharing of electronic health record data, including with individuals and their chosen health apps24,25. Demonstrating a similar commitment to greater data sharing, particularly with individuals, the Centers for Medicare and Medicaid Services (CMS) now requires health plans under its purview to share claims data with subscribers and hospitals to send alerts to physicians when their patients have been hospitalized26.

When CMS and ONC initially proposed these rules and sought public comment, they were sharply criticized by health care provider organizations and by a major vendor of provider EHR systems for promoting the sharing of sensitive clinical and claims data—data in Category 1 - with consumer-facing apps without adequately addressing privacy concerns27. Although neither CMS nor ONC has authority to regulate consumer-facing tools, the critics capitalized on a significant gap in U.S. health privacy protections28.

FTC jurisdiction and other protections

The new federal rules on interoperability and information blocking facilitate patient-mediated data flows, sending EHR data (Category 1) across an API, leaving a HIPAA-covered entity and entering a consumer-controlled app. As the data traverse the API, the regulatory authority instantaneously shifts from the HHS Office for Civil Rights (OCR), which enforces HIPAA, to the Federal Trade Commission (FTC)29. The FTC has the most enforcement power over privacy in the U.S. through Section 5(a) of the FTC Act (FTCA), which broadly prohibits “unfair or deceptive acts or practices in or affecting commerce”30. The FTCA applies to most entities engaged in commerce, including developers and marketers of mobile health technologies, social media sites, and technology companies. Generally, the FTC’s Section 5 authority does not extend to nonprofit entities or insurance companies (https://www.ftc.gov/news-events/media-resources/what-ftc-does), and there are exceptions related to banks, savings and loans, federal credit unions, and common carriers such as airlines31.

In the context of privacy, the FTC has translated its unfair and deceptive trade practices authority by, for example, requiring companies covered by the FTCA to honor their commitments set forth in privacy policies and service and to adopt reasonable security safeguards32. Further, the Commission has brought numerous cases against businesses covered by the FTCA for failing to protect consumers from companies’ deceptive and unfair practices with regard to their health data and failing to have reasonable and appropriate data security practices regarding that data33.

Entities not covered by the FTCA (for example, nonprofit entities and insurance companies) may be regulated regarding privacy and security only if covered by another federal law (HIPAA, for example) or by state law. Ironically, this means that in terms of federal privacy protections, an app offered by a nonprofit company outside of the health care system (for example, offered by a patient advocacy organization) might offer the least accountability to consumers. Of note, the FTC also administers breach notification requirements enacted by Congress in the Health Information Technology for Economic and Clinical Health Act (HITECH) (see Supplementary Discussion) and applicable to “personal health records,” which are health records maintained by or primarily for individuals34, and “related apps”13.

The FTCA is broadly applicable to most companies collecting health-relevant data, and the FTC has taken enforcement action against developers of mobile health apps35. However, there is a perception that these protections—because they are not established in comprehensive regulations similar to the HIPAA rules—are not sufficient to protect health data36. Notwithstanding the breadth of FTC’s authority, its recent settlement with Facebook regarding a number of alleged violations of the FTCA has generated doubts about whether the FTC is equipped to take on takes this enforcement role outside of HIPAA’s boundaries37,38. Concerns have also been raised that the FTC currently lacks sufficient resources to enforce privacy protections for health-relevant data at scale.

Other federal statutes extend some privacy protections for personal data, which could include health-relevant data, in particular contexts39 (See Supplementary Table 1 for a brief summary of some federal laws that extend protections for personal data). State privacy laws protecting health and personal data often are more protective than federal law40,41. For example, HIPAA does not preempt state laws that are more protective of privacy42. To help resolve privacy concerns, a number of organizations have proposed voluntary privacy frameworks for health data. Voluntary commitments made by companies subject to the FTCA can be enforced by the FTC (See Box 3 for a summary of these efforts).

Finally, international laws also can affect protections for U.S residents if global companies governed by international laws decide to apply those heightened protections to all of their customers. For example, the Global Data Protection Regulation (GDPR), which went into effect in May of 2018, covers all data “controllers” and “processors” in the European Union (EU). It also includes entities not located in the EU but who offer goods and services to EU residents or monitor the behavior of EU data subjects within the EU43. Commitments U.S. companies make to their U.S. customers to comply with the GDPR can be enforced by the FTC.

Reevaluating HIPAA

The presumption has been that, at least with respect to Category 1 data, the U.S. has sufficient protections in HIPAA, but that presumption appears to be fading. Some have questioned whether HIPAA is still protective in an increasingly digital era44. The more the public learns about what HIPAA allows, the less satisfied they are with the “protections” afforded by the law. For example, entities covered by HIPAA frequently sell data that are de-identified per HIPAA standards but still can be linked to create health profiles of individuals45.

In particular, HIPAA’s sufficiency is being questioned when it comes to sharing health-relevant data with large technology companies. After years of periodic experimentation, major technology companies are retooling business models to address need in health care (https://www.cbinsights.com/research/apple-healthcare-strategy-apps/). In many cases, these initiatives involve contracting with health care system actors to improve how these actors deliver health care46,47,48,49. Technology companies have the potential to bring needed resources and innovation into health and health care50. However, the tech companies’ history of ubiquitous data collection and tracking of consumers has generated public backlash51. Facebook52, Google53, and Twitter (https://help.twitter.com/en/information-and-ads#10-08-2019) have had substantial lapses in protecting personal information, generating public doubt that these companies can yet be trusted to responsibly handle health-relevant data54. A whistleblower’s alarm55 over an arrangement between Google and Ascension Health to facilitate data analytics for Ascension caused an uproar, triggering investigations by the U.S. Department of Health and Human Services (HHS) to assure the arrangement complied with HIPAA regulations56. In the arrangement, Google is a vendor to Ascension and covered under HIPAA as a business associate. Consequently, Google must abide by the same rules that govern health care providers and health plans, plus any additional Ascension may have imposed as part of the business associate agreement. Nevertheless, this arrangement sparked a public conversation, suggesting public dissatisfaction with such data arrangements even when they are in compliance with HIPAA.

COVID-19: the perfect storm

COVID-19 may perfectly illustrate the conundrum between protecting health information and ensuring its availability to meet the challenges posed by a significant global pandemic. U.S. lawmakers have used enforcement discretion to relax existing health privacy laws to stimulate more widespread reporting of relevant COVID-19-related data to federal and state public health authorities57. Public health experts have published best practices to enable existing health information exchange networks—built to facilitate digital data sharing among health care providers for treatment purposes—to be rapidly leveraged for public health reporting58.

But efforts from major technology companies to assist in fighting the pandemic have been met with skepticism. Verily’s establishment of community testing sites—and an online site to screen people for eligibility for a test—was initially met with criticism from privacy advocates59. Public health experts (https://apps.npr.org/documents/document.html?id=6877567-Bipartisan-Public-Health-Leaders-Letter-on) are calling for robust contact tracing to combat COVID-19 and help states and localities begin to safely re-open public spaces60. China and South Korea have mandated public use of contact tracing technologies, with few privacy controls61; other countries are also adopting contact tracing technologies62. However, in the U.S., states and localities have been slow to adopt technology solutions that would voluntarily be used by consumers to facilitate contact tracing, both due to privacy concerns and uncertainty regarding whether technology is an effective replacement for the customary human-to-human contact involved in contact tracing61. Google and Apple—typically fierce competitors—joined together to enable apps to use Bluetooth proximity data to facilitate privacy-preserving contact tracing63. However, questions have arisen both about whether such information can be collected in a way that responds to privacy concerns64 and whether privacy controls will create unnecessary barriers to deploying these technologies in an optimal way to fight the pandemic65. Others have expressed concerns regarding the equitable collection and use of this information given disparities in use of smart phones and access to broadband64.

The collection and use by public health authorities of geolocation data for purposes of COVID-19 response illustrates the need for objective review of health-relevant data sharing. The collection of geolocation data has long been controversial66. Sharing such data with governments raises concerns about how those data could be used to harm individuals (such as by stigmatizing or unjustly penalizing those who are determined, based solely on this data, to be ill or at risk to themselves or others). Further, public health authorities typically are not “covered entities” under HIPAA, and laws governing how local authorities can access, use, and disclose data may vary by state and locality67 (Of note: the U.S. Centers for Disease Control and Prevention is covered by the federal Privacy Act of 1974 (see Supplementary Table 1)).

Current federal data privacy bills

Several bills have been introduced in the 116th Congress to fill gaps in U.S. privacy law (See Supplementary Table 2 for a sample list of those bills). For the most part, the federal bills adopt one or more of the following approaches:

  • Requirements to provide individuals with clear notice about how their personal information is collected, used, and disclosed;

  • Requirements to provide individuals with choices (either opt-in or opt-out) for the collection, use, and disclosure of their personal information.

  • Broad definitions of personal data, with stricter standards for data to be considered to be de-identified (and therefore no longer covered).

  • Establishment of individual rights concerning data, including the right to know whether a company possesses your data, the right to request corrections, the right to obtain copies, and the right to have data deleted.

  • Increased authority to, and resources for, the FTC to enforce new privacy mandates; and

  • Exemptions from new law for entities already covered by HIPAA.

These bills incorporate many of the customary provisions found in privacy laws but have the following key limitations, particularly for regulating the privacy of health-relevant data:

Too much reliance on notice and consent to protect privacy

The predominant model for protecting privacy involves companies giving individuals notice of, and rights to consent to, uses and disclosures of their data. These “commitments” regarding data are typically found in Privacy Policies and Terms of Service, and consumers are required to acknowledge that they have read and agree to these documents before they are permitted to use an app or a service.

But this model of notice and consent is widely recognized by privacy scholars as being inadequate on its own to protect privacy, particularly with respect to online transactions68,69,70,71. Privacy notices and terms of service are famously too long and hard to understand and are frequently missing or inadequate72. In an age of “big data,” it is often difficult to predict at the time of data collection all future uses69. Individuals too often agree to terms of service without reading them73. Companies design technology in ways that “maximize the collection, use, and disclosure of personal information,” challenging the notion that individuals truly can make informed choices online even when they are trying to do so74. Reliance on notice and consent also shifts the burden for protecting privacy to the individual, instead of holding institutions and data holders accountable for acting transparently and responsibly with individuals’ data70. Further, companies can change their consent policies, and consumers may not be aware of these changes or have little choice but to agree to them to continue using a service70. Relying on individual consent to protect privacy also fails to account for others whose interests are often implicated in health data, as some health data (such as genetic information) reveals information about family members75.

GDPR and new state privacy laws, such as the California Consumer Privacy Act (CCPA), tend to rely on consent (either opt-in or opt-out) for collection and use of data, particularly by commercial companies76. Nonetheless, these laws do not appear to have substantially limited the ubiquitous collection and use of personal data in commerce77.

Overvaluing de-identification or pseudonymization as a privacy measure

Most existing privacy laws and proposed federal bills cover only identifiable information. Consequently, information that has been de-identified, anonymized, or pseudonymized is outside of regulation. Although techniques to reduce identifiability of information lessen privacy risks, they do not reduce the risk to zero. Too often, there is no legal accountability for unauthorized re-identification. For example, HIPAA’s de-identification standard requires data to be at “very low” (not zero) risk of re-identification. Consequently, some risk of re-identification remains, but regulators cannot hold recipients of de-identified data accountable for unauthorized re-identification78.

More recent privacy laws, such as GDPR and CCPA, appear to have more robust standards for how data qualify as “de-identified” or pseudonymized and no longer subject to regulation. For example, under the CCPA, data that can be linked to a particular person or household, such as through an IP address or advertising identifier, is considered to be covered even if the particular individual is not identified79. But because the CCPA is new, it is unclear whether these definitions will be effective at giving consumers more control over robust collection and commercialization of personal data. Further, medical researchers depend on the ability to collect and analyze de-identified data. Amendments have been proposed to the CCPA to assure that the CCPA’s more stringent definition of de-identified data does not create obstacles to the collection and use of information for health and medical research purposes80.

Absence of provisions to assure availability of health data for a learning health system

Responsible collection and analysis of health-relevant data are critical to addressing deficiencies in U.S. health and health care. De-siloed data combinable for delivery, research, and public health are needed for coordinated care81, genomic diagnosis82, including accurate diagnoses across genetic ancestries83, comparative effectiveness research84, post-marketing surveillance85, data-driven accrual to clinical trials86, rare disease research (https://www.rarediseasesnetwork.org/researchers/nih-data-sharing), public health surveillance87, early disease detection88, development of digital biomarkers to manage patients care at home89 or to combat a pandemic90, and advancing discovery91. Sometimes inclusion of entire populations is necessary to ensure generalizability of conclusions across diverse patients and to avoid the nonrandom statistical biases that would emerge from opt-in models.

The National Academy of Medicine (then the Institute of Medicine) first proposed a Learning Healthcare System framework in 20071, but progress has been slow, in part due to difficulty in accessing and sharing health-relevant data. Data to improve health and health care needs to include data sources outside of HIPAA, as much of what happens to influence an individual’s health and wellbeing occurs outside of the doctor’s office or hospital92. However, most of the proposed bills focus disproportionately on protecting personal data and do little to promote its availability. This shortcoming may be of little import for data not used for health purposes, but it has significant implications for health-relevant data. Ultimately, the U.S. will need a long-term, national solution that both addresses privacy and data availability. Survey data reveal that individuals practice “privacy-protective” behaviors such as not seeking health care or hiding the truth about health conditions if they don’t trust that their information will be kept confidential93.

Recommended protections for health-relevant data to fuel a learning health system

Determining whether there are sufficient protections for data based on whether an entity is or is not covered by HIPAA arguably is no longer the appropriate benchmark. The lack of strong, consistent protections for health data that respond to 21st-century risks could have the “long term effect of reducing the uptake of new innovative technologies” and undermining the promise of digital medicine18. At the same time, focusing just on privacy without assuring needed data flows fails to address the compelling need for data to address significant health needs94, including the need to address significant disparities in health outcomes based on race and gender. Creation of a learning health system may require a “moral priority on learning,” with active contributions of data from both health care professionals and from patients95.

The dual needs in health to both protect individuals and assure data availability to improve individual and population health call for comprehensive policies governing all entities collecting and using health-relevant information whether covered by HIPAA or not. Policymakers need not reinvent the wheel and can draw from HIPAA’s framework, as well as FTC recommendations. Specifically, in 2012, the FTC issued a report, Protecting Consumer Privacy in an Era of Rapid Change (hereinafter “FTC Report”)96. The report established recommendations for privacy “best practices” to be adopted by all commercial companies, except smaller companies and those not sharing sensitive data with third parties96. Though the report was not focused on health-relevant data and is now eight years old, the best practice recommendations nonetheless provide some noteworthy approaches for establishing enforceable rules and norms for this data.

U.S. policymakers should consider these recommendations in crafting privacy bills. But given the glacial pace of federal legislation, the recommendations below also can inform best practices adopted by companies in the absence of any new federal requirements. These best practices, if publicly attested to by companies already covered by the FTCA, can be enforced by the FTC (such as if a commercial company publicly commits to a data limitation (for example, not sharing data except with consent) but doesn’t actually follow that practice)96.

Establish rules for health-relevant data rather than relying just on consent

Although HIPAA has its deficiencies, its overall comprehensive approach has value in considering how to govern health-relevant data, even when collected and used outside of the health care system. For example, HIPAA’s regulations include a role for individual consent but do not push all of the obligations for protecting privacy to the individual, instead creating enforceable boundaries for when and how identifiable information can be used and shared. From its inception, HIPAA’s regulatory framework has recognized that health data must be protected and also made available for treatment, to secure payment, to enable health care institutions and medical practices to conduct operations, for public health, and research purposes.

On the other hand, HIPAA’s drafters established a comprehensive list of required and permitted uses and disclosures to enable data flows typical in a functioning health care system (see Supplementary Discussion); that list would not necessarily effectively govern data collected, used, and shared by commercial companies outside of healthcare. Lawmakers will need to establish a list of permitted collections, uses, and disclosures that more directly address the privacy risks in the commercial space. Also, there are few, if any, prohibitions on what an entity covered by HIPAA can do with data, as uses or disclosures not expressly permitted can still occur with the written authorization of the individual. To effectively govern commercial companies’ behavior with health-relevant data, lawmakers will need to prohibit uses and disclosures where the privacy risks are significant in comparison to the benefits.

Impose collection, use, and disclosure limitations

In the FTC Report, the Commission recommended that “companies should limit data collection to that which is consistent with the context of a particular transaction or the consumer’s relationship with the business, or as required or specifically authorized by law.”96 In other words, data collection should be limited to what a consumer might expect, given the context. “Fair Information Practice Principles,” the foundation for information privacy law, include collection limitations as a critical component of protecting data97.

However, the concept of “collection limitations” may seem antithetical to the robust health data enterprise that contributes to a learning health system. HIPAA’s regulations contain few limits on whether entities may collect health information, choosing instead to comprehensively regulate how that information can be used and disclosed once an entity covered by HIPAA has it. When HHS first drafted the HIPAA regulations, it may have made sense to disregard collection limitations. HHS was setting ground rules for how a defined set of entities within the health care system could handle data. But as health care entities increasingly collect data on socioeconomic determinants, in some cases beyond what patients might expect98, policymakers may want to consider whether collection limitations should be imposed in HIPAA (for example, requiring such collection to be directly connected to addressing individual or population health).

Further, for commercial companies, whose business models revolve around monetization of personal information, some limits on the collection of health-relevant data make sense. For example, the collection of health-relevant data could be prohibited unless the data collection is consistent with consumer expectations and intended to benefit the individual or population health. For example, a bill drafted (but not yet introduced) by Senator Sherrod Brown (D-OH) would prohibit the collection of personal data unless it is “strictly necessary” to provide the good or service sought by the consumer77.

Use and disclosures of health-relevant data similarly should be limited to what the consumer would reasonably expect, given the context70. This maxim also should govern the repurposing of information. For example, technology and telecommunications companies routinely collect geolocation data (https://www.gravitatedesign.com/blog/what-is-geolocation/). Today, governments across the world are seeking or already collecting these data for COVID-19 response activities. These data were not collected for this purpose initially, and consumers likely did not expect it to be used for this purpose. The FTC Report recommends consumer consent be obtained before collecting, using, or disclosing information in ways not consistent with the context of consumer’s relationship to the company96. Given the limitations of relying on consent for protecting privacy, such an approach feels ripe for abuse. Individual consent should be required, but some additional gating criteria may be needed to rein in companies’ tendencies to pursue uses and disclosures of data that enhance the bottom line, do not contribute to improving individual and/or population health, and where the impact of risks to privacy created by those activities is chiefly borne by the consumer.

Assure beneficial uses of health-relevant data

Federal policies should also assure that data are available to be used ethically to address health system improvements. Of note, one of the federal bills pending before Congress - the Data Care Act99 would impose duties of “care, loyalty, and confidentiality” on online service providers that collect individual identifying information, detailing specific requirements and prohibitions under each of the three categories (See Supplementary Discussion). This approach is appealing for governing health-relevant data—but the categories are so broadly worded that it is unclear it would result in beneficial uses of these data consistently across all data holders. Though allowable under HIPAA, the sale of “de-identified” data by covered entities is another flashpoint in an expanding debate100 which suggests that policies governing health-relevant data should address de-identified as well as identifiable data.

Companies collecting, using or disclosing health-relevant data (identifiable and de-identified) could be required to establish independent data ethics review boards. Such boards would evaluate proposed data projects for legal and ethical implications as well as the potential to improve health or the health care system [101]. Such boards could be similar to Institutional Review Boards (IRBs), which provide ethical review of proposals for research on human subjects under the federal Common Rule102. However, data ethics review boards should focus more on privacy than interventional risk and include members with substantial privacy expertise. Today, the Common Rule does not require IRB review of research using data that are not identifiable and provides exemptions (including rapid review by one or two members of the IRB) for research using identifiable data103. Further, data ethics review boards could evaluate uses and disclosures beyond just those for research.

This approach provides no magic bullet. Notably, IRBs have not been a panacea for assuring the ethical conduct of human subjects research. The proposed data ethics review boards similarly would need to be established with safeguards against industry capture and conflicts of interest and should not be viewed as a comprehensive solution. For such boards to be effective, they must have independence from the company and ideally include outsiders, such as consumers and experts. Facebook recently announced the establishment of an independent Oversight Board to achieve “fair decision-making” concerning the removal of unacceptable content on the site. Among the Board’s authorities are to “instruct” Facebook to allow or remove content and “interpret” Facebook’s Community Standards and other policies “in light of Facebook’s articulated values”104. Of note, Facebook relied on a provision in Delaware law, the Purpose Trust Statute, to assure the Board was independent of Facebook105. However, notwithstanding this unique (and expensive) endeavor by Facebook, there is little evidence that this Board has made a difference in assuring better decision-making with respect to content on the site106. Similarly, Google dismantled its ethics board intended to “guide responsible development of AI [artificial intelligence]” at Google shortly after it was established due to controversy over its membership107.

In another example, GDPR requires a Data Protection Impact Assessment, and in some cases regulatory review, for certain high-risk processing activities, such as health data processed in large numbers43. Similarly, U.S. federal agencies are required to conduct Privacy Impact Assessments “for all new or substantially changed technology” that collects, maintains, or disseminates personally-identifying information (https://www.archives.gov/privacy/privacy-impact-assessments). Such assessments could be valuable if subject to independent, objective review and not merely check-the-box exercises.

“Data trusts” or “civic trusts” also have been proposed as legal mechanisms for assuring that companies use and disclose consumers’ personal data for the benefit of consumers, even after a change in company strategy or sale of the company108. Consumer data trusts have been defined as “intermediaries that aggregate consumers’ interests and represent them vis-à-vis data-using organizations”109. By aggregating consumer interests, consumer trusts would have bargaining power to negotiate better terms of data use and disclosure than could be achieved by any individual consumer109. Existing laws giving individuals the right to copies of their information (for example, HIPAA, CCPA, and GDPR) could help facilitate the establishment of these trusts, as individuals could direct copies of their personal data to be held and managed therein. Common to the different versions of data trusts is the use of trust law to help assure that commitments regarding how data can be accessed, used, and disclosed are honored. But trusts also can be established to protect private interests; consequently, the ability of a data trust to assure only responsible uses of data depends on what terms and conditions are established for use and disclosure of the data, and who establishes those rules.

Another option is to require companies collecting or processing health or health-relevant data to adhere to additional oversight and requirements. Ontario province in Canada permits data custodians to disclose personal health information for health system improvement purposes. However, custodians may disclose information only to entities approved by the Privacy Commissioner as having adequate practices and procedures to protect privacy and maintain confidentiality110. In a variation on that theme, health data collection and processing could be limited only to entities that demonstrate (through periodic audits) that they meet ethical, privacy, and security standards. Companies collecting health-relevant data also could be required to segment or “firewall” their health business from other aspects of the company.

Health system entities customarily rely on “data use agreements” to bind recipients of health data to contractual commitments regarding the use of that data. Such agreements often include prohibitions on further uses and disclosures and, in the case of de-identified information, commitments not to re-identify individuals in the dataset. HIPAA requires a data use agreement when a “limited data set” (data stripped of 16 common identifiers) is used or disclosed for routine health care operations, public health, or research111. Data use agreements also may be voluntarily adopted when sharing even de-identified data, as an additional measure of protection adopted by the disclosing entity. However, such agreements are not a scalable solution to protecting health-relevant data. They depend on the parties to the contract to agree to responsible terms (often difficult where the data recipient has greater bargaining power), and those terms can only be enforced by the parties to the contract. While such contracts can be protective, they can also be vehicles for protecting data as a proprietary asset, which can limit the availability of data even for potentially beneficial uses.

Increase transparency and choice around health data uses and disclosures

Although notice and consent should not be the cornerstone for privacy, individuals still want and should have notice of, and some choice about, collection, use, and disclosure of health-relevant information (https://ogury.com/blog/how-consumers-really-feel-about-their-privacy-and-data/). The FTC Report called for “simplified choice,” with clearer, shorter, and more standardized privacy notices, in circumstances where the data collection, use, and sharing is beyond what consumers would ordinarily expect or involves sensitive data96. For example, companies can improve notice and choice through layered notice and use of visuals to improve comprehension112,113.

But even if consent is not sought for a particular use or disclosure, either because it is within consumer expectations or is mandated or authorized by law, companies should still be required to be transparent about data uses and disclosures (https://bankingjournal.aba.com/2019/06/study-consumers-increasingly-concerned-with-data-security-privacy/). As demonstrated by the public reaction to Google’s arrangements with Ascension Health System, greater transparency of health data uses and disclosures appears important to engendering public trust in digital medicine technologies.

Even within traditional health care, there is a need for greater communication about health-relevant data uses and disclosures. The National Research Council report on Precision Medicine emphasizes that it is patients who “uniquely understand the potential value of a social contract in which patients both contribute personal clinical data and benefit from the knowledge gained through the collaboration”114. In consenting to care and treatment, physicians, hospitals, and health systems should consider entering a compact with patients such that data and biospecimens captured as a byproduct of the care delivery system can be aggregated and linked to be used in a learning health system. Both consent to treat documents and the notice of privacy practices provided to patients should explicitly outline the compact82.

Strengthen remedies for harms

Ideally, protections for health-relevant data should go beyond addressing privacy and also address the potential for harm. Historically, in the U.S. policymakers have separated addressing discrimination—such as through the enactment of provisions in the Affordable Care Act prohibiting discrimination in health insurance based on health status or history—and privacy. However, in many respects privacy and nondiscrimination can collectively help create public trust in the collection, use, and sharing of health-relevant data115. The Genetic Information Nondiscrimination Act (GINA) is one model of a combined privacy and antidiscrimination law116. For example, GINA prohibits employers from collecting genetic information (protecting privacy) and prohibits employers from discriminating based on genetic information. Because health-relevant data can be collected and used for benefit and harm (compare using information to target scarce health care resources toward individuals and populations most in need with using that same information to avoid enrolling individuals and populations likely to be more costly and difficult to treat), it is critically important that policies not just focus on controls on health-relevant data and also address and minimize the opportunities for information to be used in ways that harm individuals and populations.

What may be needed are stronger protections against discrimination—for example, discrimination in health and health-related insurance (for example, disability insurance) and protections against harmful employer uses of health-relevant information. The Affordable Care Act, which prohibits discrimination in the provision of health insurance (except with respect to information on smoking status) is perceived to be on shaky ground due to persistent opposition from a number of Republican policymakers (https://www.bbc.com/news/world-us-canada-24370967), and there are no federal protections for disability and life insurance. Similarly, the Americans with Disabilities Act provides employment protections for individuals with disabilities—but these protections do not extend to health information collected about persons who do not meet the definition of an individual with a disability117.

In protecting against potential “harms” of data use, policymakers also need to consider the chilling effect that law enforcement access to data will have on the willingness of individuals—particularly marginalized populations—to have their data collected and used for “learning” purposes. Data for Black Lives is “committed to the mission of using data science to create concrete and measurable change in the lives of Black people,” recognizing that data has “tremendous potential to empower communities of color”—while at the same time data is too often “wielded as an instrument of oppression, reinforcing inequality and perpetuating injustice.” (https://d4bl.org/about.html) Privacy laws often contain exemptions for access to data by law enforcement. For example, HIPAA allows law enforcement access to data based on an “administrative request” if the information sought is “relevant and material to a legitimate law enforcement inquiry,” limited in scope, and requiring identifiable (vs. de-identified) information118. HIPAA also allows entities to release certain medical information to law enforcement—including name, address, blood type, and physical characteristics—to identify or locate a suspect, fugitive, witness or missing person119. For entities not covered by HIPAA, company commitments may provide the only assurance that law enforcement access to data will be required to meet probable cause standards, as determined by a neutral authority (such as a court), absent stronger protections in applicable state law. Police recently caught the Golden State Killer by matching DNA found at a crime scene with DNA in a free online genetic database used by one of his relatives. Although this information helped police to resolve 12 murders and at least 45 rapes committed in California between 1976 to 1986, the potential for law enforcement access to online health-relevant databases may deter individuals from using these tools120.

In developing its 2012 report, the FTC expressly rejected calls for a “harm-based” model of privacy that focuses only on protecting consumers from harms like “physical security, economic injury, and unwanted intrusions into their daily lives.”96 FTC’s contention was that such a model would fail to recognize “a wider range of privacy-related concerns, including reputational harm or the fear of being monitored.”96 Feelings of risk and anxiety also are among the harms suffered by individuals whose data are breached121. Rules-based privacy regimes like HIPAA instead create enforceable expectations regarding how health data must be handled without regard to whether or not individuals or populations suffer any cognizable harm when organizations don’t follow the rules. In addition to addressing discrimination harms, policymakers should also consider addressing more traditional privacy harms (for example, breaches of heath information). In enforcing HIPAA, OCR considers whether a HIPAA violation harmed individuals in determining the level of civil monetary penalty it will pursue122. Through HITECH, Congress amended the HIPAA Privacy Rule to require HHS to establish a mechanism to enable individuals “harmed” by HIPAA violations to receive a portion of any civil monetary penalties or settlements imposed or reached by HHS. However, HHS has yet to act on this measure13.

Harm should not be the linchpin of privacy regulation; but addressing harm should be a component, particularly for health-relevant data given its sensitivity. One interesting example is a privacy tax on data collectors and processors that could fund a no-fault compensation program for privacy harms123. Companies also could be required to establish funds to compensate harms, with broad recognition of the types of privacy harms that can occur due to unauthorized or unethical uses or disclosures of data123.

Maintain incentives to use and disclose data which are less identifiable—but refrain from treating these data as zero risk

In general, information at low or very low risk of re-identification is typically not subject to privacy laws and consequently is not regulated. Such an approach leaves privacy risk on the table; however, relaxing regulations on data at very low risk of re-identification provides incentives for entities to collect, use, and disclose data with fewer privacy risks. Experience with HIPAA’s rules for de-identification suggests that if the law sets clear and achievable standards for de-identification, entities will leverage de-identified data for public health, research, and business analytics. On the other hand, anger and frustration over commercialization of HIPAA de-identified health data appears to be increasing—and some entities are responding to those concerns124. For example, one renowned medical center has recently adopted an ethical framework for sharing even de-identified data and biospecimens with external entities, including commercial companies125.

Regulation of health-relevant data should provide incentives for the use and disclosure of that data in less identifiable forms. However, given that this data will still retain some residual risk of re-identification, this data should be subject to some regulation. For example, civil monetary penalties should be imposed for unauthorized re-identification of de-identified data and criminal penalties for intentional re-identification. But merely controlling for risk of re-identification will not be sufficient to garner consumer trust in how companies handle their health-relevant data, even if it is “de-identified.” Companies should be required to be transparent about the uses and disclosures of de-identified data and to identify the general methodologies used for de-identification. Because consent is not sufficiently protective of privacy, uses and disclosures of de-identified data also could be subject to ethics board review.

Conclusion

To fully realize the potential of digital data and digital medicine, and to advance U.S health and healthcare toward becoming a rapid learning system, the U.S. will need comprehensive privacy and security protections for health data regardless of where the data are collected or maintained. At the same time, such health protections must also encourage responsible uses and disclosures. The COVID-19 pandemic shines a spotlight on this problem—but COVID-19 is far from the only health issue that more robust access to data could help address. And in the aftermath of COVID-19, as health threats ease, re-equilibrating around access to health care data will be an essential conversation. To date privacy bills introduced to date focus more on protecting health-relevant data than on assuring its appropriate use. Also, proposed measures for protecting data rely too much on notice and consent and de-identification of data as protections.

What is needed is a multi-pronged approach that implements strong privacy protections but also includes accountability even for uses of so-called “de-identified” or anonymized data and addresses the potential for harm to individuals and populations. Such measures should also facilitate assure the availability of health-relevant data for societal benefit and to support a learning healthcare system Such a transformation could not be more important and urgent, as U.S. healthcare adapts to the impact of a global pandemic, struggles to care for an aging population, faces a diminishing primary care workforce, and continues to have the highest expenditures in the world despite often having poorer health outcomes. Innovative and pervasive use of data must underpin any substantial transformation.

Data availability

This paper is not original research involving data collection. Hence, there is no research data to make available.

References

  1. 1.

    The Learning Healthcare System: Workshop Summary (The National Academies Press, Washington, DC, 2007).

  2. 2.

    Gottlieb, L., Sandel, M. & Adler, N. Collecting and applying data on social determinants of health in health care settings. JAMA Intern Med. 173, 1017–1020 (2013).

    Article  Google Scholar 

  3. 3.

    Kuraitis, V. & McGraw, D. Health Data Outside Hipaa: The Wild West of Unprotected Personal Data. The Healthcare Blog. https://thehealthcareblog.com/blog/2019/08/12/health-data-outside-hipaa-the-wild-west-of-unprotected-personal-data/. Accessed 12 Aug 2019.

  4. 4.

    NY Times. The Privacy Project. https://www.nytimes.com/interactive/2019/opinion/internet-privacy-project.html (Series of articles, June 2019 to Feb. 2020)

  5. 5.

    What They Know. Wall St J. https://www.wsj.com/news/types/what-they-know (series of articles, 27 Feb 2011 to 23 Dec. 2012).

  6. 6.

    Cohen, I. G. & Mello, M. M. Big data, big tech, and protecting patient privacy. JAMA 322, 1141–1142 (2019).

    Article  Google Scholar 

  7. 7.

    U.S. Department of Health and Human Services. Report to Congress.- Report on Health Information Blocking. https://www.healthit.gov/sites/default/files/reports/info_blocking_040915.pdf. Accessed 20 Apr 2015.

  8. 8.

    Finley, D. About 60 Health Systems are Siding with Epic Systems Against HHS Proposed Data-sharing Rules. https://www.businessinsider.com/60-health-systems-epic-systems-hhs-2020-2. Accessed 06 Feb 2020.

  9. 9.

    Weber, G. M., Mandl, K. D. & Kohane, I. S. Finding the Missing Link for Big Biomedical Data. JAMA 311, 2479–2480 (2014).

    CAS  PubMed  Google Scholar 

  10. 10.

    Chess, E. Step Aside, Biomarkers. Look to the Bank Account for Early Signs Of Dementia. https://www.statnews.com/2019/12/05/dementia-early-warning-check-bank-accounts-not-biomarkers/. Accessed 05 Dec 2019.

  11. 11.

    Parker-Pope, T. Keeping Score On How You Take your Medicine. The NY Times. https://well.blogs.nytimes.com/2011/06/20/keeping-score-on-how-you-take-your-medicine/. Accessed 20 Jun 2011.

  12. 12.

    Health Insurance Portability and Accountability Act. Public Law No. 104-191, 110 Stat. 1938 (1996).

  13. 13.

    Health Information Technology for Economic and Clinical Health Act (HITECH). Public Law No. 111-5, 123 Stat. 226 (Feb. 17, 2009).

  14. 14.

    Code of Federal Regulations title 45, § 160.103 (definition of health information).

  15. 15.

    Price, W. N. & Cohen, I. G., Privacy in the Age of Medical Big Data. Nat. Med. 25, 37–43 (2019).

  16. 16.

    National Committee on Vital and Health Statistics (NCVHS). Health Information Privacy Beyond HIPAA: A Framework for Use and Protection (A Report for Policy Makers. https://ncvhs.hhs.gov/wp-content/uploads/2019/07/Report-Framework-for-Health-Information-Privacy.pdf. Accessed 18 Jun 2019.

  17. 17.

    U.S. Department of Health & Human Services. Examining Oversight of the Privacy & Security of Health Data Collected by Entities Not Regulated by HIPAA. https://www.healthit.gov/sites/default/files/non-covered_entities_report_june_17_2016.pdf. Accessed 17 Jun 2016.

  18. 18.

    Forbrukerradet. Out of Control: How Consumers are Exploited by the Online Advertising Industry. https://fil.forbrukerradet.no/wp-content/uploads/2020/01/2020-01-14-out-of-control-final-version.pdf. Accessed 14 Jan 2020.

  19. 19.

    Test-Achats. Nutrition And Health Applications Do Not Respect Privacy. https://www.test-achats.be/action/espace-presse/communiques-de-presse/2020/food-and-health-apps#. Accessed 23 Jan 2020.

  20. 20.

    Huckvale, K., Torous, J., & Larsen, M. Assessment of the Data Sharing and Privacy Practices of Smartphone Apps For Depression And Smoking Cessation. JAMA Network Open. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2730782 (2019).

  21. 21.

    Public Law 114-255. 130 Stat 1033 Sections 4003–4004.

  22. 22.

    Federal Register vol. 85 25642-25961. Accessed 01 May 2020.

  23. 23.

    Mandl, K. D., Mandel, J. C. & Kohane, I. S. Driving Innovation in Health Systems Through an Apps-Based Information Economy. Cell Syst. 1, 8–13 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Mandl, K. D. et al. Push Button Population Health: The SMART/HL7 Bulk Data Access Application Programming Interface. npj Digit. Med. 3, 151 (2020).

    Article  Google Scholar 

  25. 25.

    Mandl, K. D. & Kohane, I. S. A 21st Century Health IT System: Creating a Real-World Information Economy. N. Engl. J. Med. 376, 1905–1907.

  26. 26.

    Federal Register vol. 85 no. 85. 25510-25640. Accessed 01 May 2020.

  27. 27.

    Roth, M. Special Report: Epic Uproar Exposes Conflict Between Data Privacy and Innovation. Health Leaders Media. https://www.healthleadersmedia.com/innovation/special-report-epic-uproar-exposes-conflict-between-data-privacy-and-innovation. Accessed 11 Feb 2020.

  28. 28.

    Mandl, K. D. & Kohane, I. S. Epic’s call to block a proposed data rule is wrong for many reasons. Stat News. https://www.statnews.com/2020/01/27/epic-block-proposed-data-rule/. Accessed 20 Jan 2020.

  29. 29.

    Mandl, K. D. & Kohane, I. S. Data Citizenship under the 21st Century Cures Act. N. Engl. J. Med 382, 1781–1783 (2020). March 11.

    Article  Google Scholar 

  30. 30.

    U.S. Code title 15, §45.

  31. 31.

    US. Code title 15, §45(a)(2).

  32. 32.

    Solove, D. J. & Hartzog, W. The FTC and the New Common Law of Privacy. Col. Law Rev. 114, 583–676 (2014).

    Google Scholar 

  33. 33.

    U.S. Department of Health and Human Services. Examining Oversight of the Privacy & Security of Health Data Collected by Entities Not Regulated by HIPAA. https://www.healthit.gov/sites/default/files/non-covered_entities_report_june_17_2016.pdf. Accessed 17 Jun 2016.

  34. 34.

    Mandl, K. D. & Kohane, I. S. Time for a Patient-Driven Health Information Economy? N. Engl. J. Med. 374, 205–208 (2016). January 21.

    Article  Google Scholar 

  35. 35.

    Wagner, J. K. The Federal Trade Commission and Consumer Protections for Mobile Health Apps. J. Law, Med. Ethics 48, 103–114 (2020). April 28.

    Article  Google Scholar 

  36. 36.

    Terry, N. Assessing the thin regulation of consumer-facing health technologies. J. Law, Med. Ethics 48, 94–102 (2020).

    Article  Google Scholar 

  37. 37.

    Coldewey, D. 9 Reasons the Facebook FTC Settlement is a Joke. Techcrunch. https://techcrunch.com/2019/07/24/9-reasons-the-facebook-ftc-settlement-is-a-joke/. Accessed 24 Jul 2019.

  38. 38.

    Olen, H. Why Facebook’s $5 Billion Settlement With the Ftc Won’t Change A Thing. Wash Post. https://www.washingtonpost.com/opinions/2019/07/25/why-facebooks-billion-settlement-with-ftc-wont-change-thing/. Accessed 25 Jul 2019.

  39. 39.

    Congressional Research Service. Data Protection Law: An Overview. https://fas.org/sgp/crs/misc/R45631.pdf. Accessed 25 Mar 2019.

  40. 40.

    U.S. Department of Health and Human Services. Privacy and Security Solutions for Interoperable Health Information ExchangeReport on State Law Requirements for Patient Permission to Disclose Health Information. https://www.healthit.gov/sites/default/files/disclosure-report-1.pdf. Accessed Aug 2009.

  41. 41.

    Baum, S. Navigating State Patient Data Privacy Laws Will Only Get More Challenging. MedCity News. https://medcitynews.com/2018/11/navigating-state-patient-data-privacy-laws-will-only-get-more-challenging/. Accessed 13 Nov 2018.

  42. 42.

    Craig, D. What You Need To Know About Hipaa And Your State’s Laws. https://blog.sprucehealth.com/need-know-hipaa-states-laws/. Accessed 10 Oct 2016.

  43. 43.

    EU, General Data Protection Regulation (GDPR) OJ 2016 L119/1.

  44. 44.

    Butler, M. Is HIPAA outdated? While coverage gaps and growing breaches raise industry concern, others argue HIPAA is still effective. J. Ahima. 88, 52 (2017).

    Google Scholar 

  45. 45.

    Tanner, A. Our Bodies, Our Data: How companies make billions selling our medical records (Beacon Press, Boston, 2017).

  46. 46.

    Robbins, R. Contract offers unprecedented look at Google deal to obtain patient data from the University of California. Stat News. https://www.statnews.com/2020/02/26/patient-data-contract-google-university-of-california/. Accessed 26 Feb 2020.

  47. 47.

    Rosenbaum, L. Google Health Exec Defends Controversial Partnership With Ascension: ‘We’re Super Proud of it.’ Forbes. https://www.forbes.com/sites/leahrosenbaum/2020/01/14/google-health-exec-defends-controversial-partnership-with-ascension-were-super-proud-of-it/#69dd0116a3be. Accessed 14 Jan 2020.

  48. 48.

    Landi, H. Providence St. Joseph Health, Microsoft form strategic alliance to leverage cloud, AI technology. Fierce Healthcare. https://www.fiercehealthcare.com/tech/providence-st-joseph-health-microsoft-form-strategic-alliance-to-leverage-cloud-ai-technology. Accessed 08 Jan 2019.

  49. 49.

    Jahns, I. For the benefit of all’” Mayo partners with Amazon, Microsoft and others in the fight against COVID-19. MedCityBeat. https://www.medcitybeat.com/news-blog/2020/for-the-benefit-of-all-mayo-partners-with-amazon-microsoft-and-others-in-fight-against-covid-19. Accessed 26 Mar 2020.

  50. 50.

    Wachter, R. & Cassel, C. Sharing Health Data with Digital Giants: Overcoming Obstacles and Reaping Benefits While Protecting Patients. JAMA 323, 507–508 (2020).

    Article  Google Scholar 

  51. 51.

    Smith, E. The Techlash Against Amazon, Facebook And Google – And What They Can Do Economist. https://www.economist.com/briefing/2018/01/20/the-techlash-against-amazon-facebook-and-google-and-what-they-can-do. Accessed 20 Jan 2018.

  52. 52.

    Davis, J. Facebook Accused Of Exposing User Health Data In Complaint to FTC. Health IT News. https://healthitsecurity.com/news/facebook-accused-of-exposing-user-health-data-in-ftc-complaint. Accessed 20 Feb 2019.

  53. 53.

    Nakashima, R. A. P. Exclusive: Google tracks your movements, like it or not. Associated Press. https://apnews.com/828aefab64d4411bac257a07c1af0ecb. Accessed 13 Aug 2018.

  54. 54.

    Zuboff, S. Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (PublicAffairs, New York, 2019).

  55. 55.

    Anonymous. I’M The Google Whistleblower. The Medical Data Of Millions Of Americans Is At Risk. The Guardian. https://www.theguardian.com/commentisfree/2019/nov/14/im-the-google-whistleblower-the-medical-data-of-millions-of-americans-is-at-risk. Accessed 14 Nov 2019.

  56. 56.

    Garcia, A. Google’s ‘Project Nightingale’ center of Federal inquiry. https://www.cnn.com/2019/11/12/tech/google-project-nightingale-federal-inquiry/index.html. Accessed 15 Nov 2019.

  57. 57.

    U.S. Department of Health and Human Services. HIPAA and COVID-19. https://www.hhs.gov/hipaa/for-professionals/special-topics/hipaa-covid19/index.html. Accessed 12 Jun 2020.

  58. 58.

    Mostashari, F. & McClellan, M. Data Interoperability and Exchange to Support COVID-19 Containment. https://healthpolicy.duke.edu/sites/default/files/atoms/files/data_interoperability_and_exchange_to_support_covid-19_containment_final.pdf. Accessed 01 May 2020.

  59. 59.

    Gebhart, G. Verily’s COVID-19 Screening Website Leaves Privacy Questions Unanswered. https://www.eff.org/deeplinks/2020/03/verilys-covid-19-screening-website-leaves-privacy-questions-unanswered. Accessed 25 Mar 2020.

  60. 60.

    Watson, C., Cicero, A., Blumenstock, J., & Fraser, M. A National Plan to Enable Comprehensive COVID-19 Case Finding and Contact Tracing in the U.S. https://www.centerforhealthsecurity.org/our-work/pubs_archive/pubs-pdfs/2020/200410-national-plan-to-contact-tracing.pdf. Accessed 10 Apr 2020.

  61. 61.

    Vogelstein, F. & Knight, W. Health Officials Say ‘No Thanks’ to Contact Tracing Tech. Wired. https://www.wired.com/story/health-officials-no-thanks-contact-tracing-tech/. Accessed 08 May 2020.

  62. 62.

    O’Neill, P. H., Ryan-Mosley, T. & Johnson, B. A Flood Of Coronavirus Apps Are Tracking Us. Now It’s Time To Keep Track Of Them. MIT Tech Rev. https://www.technologyreview.com/2020/05/07/1000961/launching-mittr-covid-tracing-tracker/. Accessed 07 May 2020.

  63. 63.

    Holmes, A. Take A First Look At Apple And Google’s Ambitious New Covid-19 Contact Tracing Technology That Will Send You A Notification If You Were Near Someone Who Has The Coronavirus. Bus Insider. https://www.businessinsider.com/apple-google-covid-19-contact-tracing-smartphone-screenshots-2020-5. Accessed 04 May 2020.

  64. 64.

    Morrison, S. Apple and Google look like problematic heroes in the pandemic. Vox. https://www.vox.com/recode/2020/4/16/21221458/apple-google-contact-tracing-app-coronavirus-covid-privacy. Accessed 16 Apr 2020.

  65. 65.

    Newton, C. Why Countries Keep Bowing To Apple And Google’s Contact Tracing App Requirements. The Verge. https://www.theverge.com/interface/2020/5/8/21250744/apple-google-contact-tracing-england-germany-exposure-notification-india-privacy. Accessed 08 May 2020.

  66. 66.

    Nanos, J. Every Step You Take: How Companies Use Geolocation Data To Target You–And Everyone Around–In Ways You’re Not Even Aware of Boston Globe. https://apps.bostonglobe.com/business/graphics/2018/07/foot-traffic/. Accessed 20 Jun 2020.

  67. 67.

    O'Connor, J. & Matthews, G. Informational Privacy, Public Health, and State Laws. Am. J. Public Health 101, 1845–1850 (2011).

    Article  Google Scholar 

  68. 68.

    Hartzog, W. & Richards, N. It’s Time to Try Something Different On Internet Privacy. Wash Post. https://www.washingtonpost.com/opinions/its-time-to-try-something-different-on-internet-privacy/2018/12/20/bc1d71c0-0315-11e9-9122-82e98f91ee6f_story.html?noredirect=on. Accessed 20 Dec 2018.

  69. 69.

    Cate, F. H. & Mayer-Schönberger, V. Notice and Consent in a World of Big Data. https://www.repository.law.indiana.edu/facpub/2662 (2013).

  70. 70.

    Nissenbaum, H. A. contextual approach to privacy online. Daedalus 140, 32–48 (2011).

    Article  Google Scholar 

  71. 71.

    Pasquale, F. Redescribing health policy: the importance of information policy. Houst. J. Health Law Policy 14, 95–128 (2014).

    Google Scholar 

  72. 72.

    Unyaef, A., Dehling, T., Taylor, P. L. & Mandl, K. D. Availability and quality of mobile health app privacy policies. J. Am. Med Inf. Assoc. 22, 28–33 (2015). April.

    Article  Google Scholar 

  73. 73.

    Berreby, D. Click to Agree With What? No One Reads Terms Of Service, Studies Confirm. The Guardian. https://www.theguardian.com/technology/2017/mar/03/terms-of-service-online-contracts-fine-print. Accessed 03 Mar 2017.

  74. 74.

    Hartzog, W. Privacy’s Blueprint: The Battle to Control the Design of New Technologies (Harvard University Press, Cambridge, 2018).

  75. 75.

    McGuire, A. L. et al. Confidentiality, privacy, and security of genetic and genomic test information in electronic health records: points to consider. Genet. Med. 10, 495–499 (2008).

    Article  Google Scholar 

  76. 76.

    Brumfield, C. 11 New State Privacy And Security Laws Explained: Is Our Business Ready? CSO. https://www.csoonline.com/article/3429608/11-new-state-privacy-and-security-laws-explained-is-your-business-ready.html. Accessed 08 Aug 2019.

  77. 77.

    Fowler, G. Nobody Reads Privacy Policies. This Senator Wants Lawmakers To Stop Pretending We Do. Wash Post. https://www.washingtonpost.com/technology/2020/06/18/data-privacy-law-sherrod-brown/. Accessed 18 Jun 2020.

  78. 78.

    McGraw, D. Building public trust in uses of Health Insurance Portability and Accountability Act de-Identified data. J. Am. Med Inform. Assn 20, 29–34 (2013).

    Article  Google Scholar 

  79. 79.

    Title 1.81.5 of Part 4 of Division 3 of the California Civil Code (Section 1798.100 et seq.) at 1798.140(o).

  80. 80.

    Doddi, D. & Gottlieb, D. California Bill Proposes CCPA Exceptions for HIPAA De-identified Information, Other Health data. JD Supra. https://www.jdsupra.com/legalnews/california-bill-proposes-ccpa-34045/ (Jan 17, 2020).

  81. 81.

    Anderson, A. C. & Chen, J. ACO Affiliated Hospitals increase implementation of care coordination strategies. Med. Care 2019, 300–304 (2019).

    Article  Google Scholar 

  82. 82.

    Mandl, K. D. & Bourgeois, F. T. The Evolution of patient diagnosis: from art to digital data-driven science. JAMA 318, 1859–1860 (2017).

    Article  Google Scholar 

  83. 83.

    Manrai, A. K. et al. Genetic misdiagnoses and the potential for health disparities. N. Eng. J. Med. 375, 655–665 (2016).

    Article  Google Scholar 

  84. 84.

    Collins, F. S., Hudson, K. L., Briggs, J. P. & Lauer, M. S. PCORnet: turning a dream into reality. J. Am. Med. Inform. Ass’n 21, 576–577 (2014).

    Article  Google Scholar 

  85. 85.

    Califf, R. The Patient-Centered Outcomes Research Network: a national infrastructure for comparative effectiveness research. N. C. Med. J. 75, 204–210 (2014).

    PubMed  Google Scholar 

  86. 86.

    Visweswaran, S. et al. Accrual to Clinical Trials (ACT): a clinical and translational science award consortium network. J. Am. Med Inform. Ass’n Open 1, 147–152 (2018).

    Google Scholar 

  87. 87.

    Platt, R. et al. The FDA sentinel initiative–an evolving national resource. N. Engl. J. Med. 379, 2091–2093 (2018).

    Article  Google Scholar 

  88. 88.

    Mandl, K. D. et al. Implementing syndromic surveillance: a practical guide informed by the early experience. J. Am. Med. Inform. Ass’n 11, 141–150 (2004).

    Article  Google Scholar 

  89. 89.

    Coravos, A., Khozin, S. & Mandl, K. Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ Digital Med. 2, 14 (2019).

    Article  Google Scholar 

  90. 90.

    Hale, C. Fitbit Posts Early Findings Showing Its Trackers Can Identify Cases Of Covid-19 Before Symptoms Take Hold. Fierce Biotech. https://www.rarediseasesnetwork.org/researchers/nih-data-sharing. Accessed 19 Aug 2020.

  91. 91.

    Mandl, K. D. & Kohane, I. S. Federalist principles for healthcare data networks. Nat. Biotechnol. 33, 360–363 (2015).

    CAS  Article  Google Scholar 

  92. 92.

    Quinn, M. The future of healthcare is outside the doctor’s office. https://www.governing.com/topics/health-human-services/gov-community-health-workers.html (2017). Accessed 7 Apr 2020.

  93. 93.

    McGraw, D., Dempsey, J. X., Harris, L. & Goldman, J. Privacy as enabler, not an impediment: building trust into healthcare information exchange. Health Aff. 28, 416–427 (2009).

    Article  Google Scholar 

  94. 94.

    Pasquale, F. Grand Bargains for Big Data: The Emerging Law of Health Information. Md. Law Rev. 72, 682–772 (2013).

    Google Scholar 

  95. 95.

    Faden R. R. et al. An Ethics Framework for a Learning Health Care System: A Departure from Traditional Research Ethics and Clinical Ethics. Hastings Ctr Special Report: Ethical Oversight of Learning Health Care Systems S16–S27 (Jan–Feb 2013).

  96. 96.

    FTC Report. Protecting Consumer Privacy In An Era Of Rapid Change: Recommendations For Businesses And Policymakers. https://www.ftc.gov/sites/default/files/documents/reports/federal-trade-commission-report-protecting-consumer-privacy-era-rapid-change-recommendations/120326privacyreport.pdf. Accessed Mar 2012.

  97. 97.

    Gellman, R. Fair Information Practices: A Basic History. https://bobgellman.com/rg-docs/rg-FIPshistory.pdf. Accessed 07 Oct 2019.

  98. 98.

    Millenson, M. Big Data on Social Determinants: Improved Health and Unaddressed Privacy Concerns. https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0161. Accessed 05 Jun 2018.

  99. 99.

    S.3744. Data Care Act of 2018. https://www.congress.gov/bill/115th-congress/senate-bill/3744. Accessed 12 Dec 2018.

  100. 100.

    Farr, C. Hospital Execs Say They Are Getting Flooded With Requests For Your Health Data. CNBC. https://www.cnbc.com/2019/12/18/hospital-execs-say-theyre-flooded-with-requests-for-your-health-data.html. Accessed 18 Dec 2019.

  101. 101.

    Parasidis, E., Pike, E. & McGraw, D. A Belmont Report for Health Data. N. Engl. J. Med 380, 1493–1495 (2019).

    Article  Google Scholar 

  102. 102.

    U.S. Department of Health and Human Services, Office for Human Research Protections. Federal Policy for the Protection of Human Subjects (‘Common Rule’). https://www.hhs.gov/ohrp/regulations-and-policy/regulations/common-rule/index.html. Accessed 18 Mar 2016.

  103. 103.

    Menikoff, J., Kaneshiro, J. & Pritchard, I. The Common Rule, Updated. N. Engl. J. Med. 375, 613–615 (2017).

    Article  Google Scholar 

  104. 104.

    Facebook Oversight Board Charter. https://about.fb.com/wp-content/uploads/2019/09/oversight_board_charter.pdf. Accessed 23 Jun 2020.

  105. 105.

    Thomas, V. C., Duda, J. P. & Maurer, T. G. Independence With A Purpose: Facebook’s Creative Use Of Delaware’s Purpose Trust Statute To Establish Independent Oversight. https://businesslawtoday.org/2019/12/independence-purpose-facebooks-creative-use-delawares-purpose-trust-statute-establish-independent-oversight/. Accessed 17 Dec 2019.

  106. 106.

    Sullivan, M. Facebook Has A Huge Truth Problem. A High-priced ‘Oversight Board’ Won’t Fix It. Wash Post. https://www.washingtonpost.com/lifestyle/media/facebook-has-a-huge-truth-problem-a-high-priced-oversight-board-wont-fix-it/2020/05/14/c5b53cba-95d9-11ea-9f5e-56d8239bf9ad_story.html?utm_campaign=wp_post_most&utm_medium=email&utm_source=newsletter&wpisrc=nl_most. Accessed 14 May 2020.

  107. 107.

    Piper, K. Exclusive: Google Cancels Ai Ethics Board In Response To Outcry. Vox. https://www.vox.com/future-perfect/2019/4/4/18295933/google-cancels-ai-ethics-board. Accessed 04 Apr 2019.

  108. 108.

    McDonald, S. The Civic Trust. Medium. https://medium.com/@McDapper/the-civic-trust-e674f9aeab43. Accessed 04 Aug 2015.

  109. 109.

    Siftung Neue Verantwortung. Designing Data Trusts—Why We Need to Test Consumer Data Trusts Now. https://www.stiftung-nv.de/en/publication/designing-data-trusts-why-we-need-test-consumer-data-trusts-now. Accessed 23 Jun 2020.

  110. 110.

    Information and Privacy Commissioner/Ontario. A Guide to the Personal Health Information Protection Act. https://www.ipc.on.ca/wp-content/uploads/Resources/hguide-e.pdf. Accessed Dec 2004.

  111. 111.

    Code of Federal Regulations, title 45 § 164.514(e)(4).

  112. 112.

    Schaub, F., Balebako, R., Durity, A. L., & Cranor, L. F. A Design Space For Effective Privacy Notices. 2015 Symposium On Usable Privacy and Security (SOUPS). https://www.ftc.gov/system/files/documents/public_comments/2015/10/00038-97832.pdf. Accessed 22–24 Jul 2015,

  113. 113.

    Kay, M. & Terry, M. Textured Agreements: Re-envisioning Electronic Consent. 2010 Symposium On Usable Privacy And Security (SOUPS). http://hci-web.cs.uwaterloo.ca/sites/default/files/soups_2010_textured.pdf. Accessed 14–16 Jul 2010.

  114. 114.

    National Research Council of the National Academies. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease (National Academies Press, Washington, DC, 2011).

  115. 115.

    Roberts, J. L. Protecting Privacy to Prevent Discrimination. Wm. Mary L. Rev. 56, 2097–2174 (2015).

    Google Scholar 

  116. 116.

    Genetic Information Nondiscrimination Act. Pub. L. 110-233, 122 Stat. 881 (21 May 2008).

  117. 117.

    U.S. Department of Justice, Civil Rights Division, Disability Rights Section. A Guide to Disability Rights Laws. https://www.ada.gov/cguide.htm#:~:text=Americans%20with%20Disabilities%20Act%20(ADA,to%20the%20United%20States%20Congress. Accessed Feb 2020.

  118. 118.

    Code of Federal Regulations, title 45 §164.512(f)(1)(C).

  119. 119.

    Code of Federal Regulations, title 45 §164.512(f)(2).

  120. 120.

    Guerini, C. J., Robinson, J. O., Petersen, D., & McGuire, A. L. Should police have access to genetic genealogy databases? Capturing the Golden State Killer and other criminals using a controversial new forensic technique. PLOS Biol. https://doi.org/10.1371/journal.pbio.2006906 (2018).

  121. 121.

    Solove, D. & Citron, D. K. Risk and anxiety: a theory of data-breach harms. Tex. Law Rev. 96, 737–786 (2018).

    Google Scholar 

  122. 122.

    Code of Federal Regulations, title 45 §160.408(b).

  123. 123.

    Edwards, L. Reconstructing consumer privacy protection on-line: a modest proposal. Int. Rev. Law Comput. Technol. 18, 313–344 (2004).

    Article  Google Scholar 

  124. 124.

    McGraw, D. & Petersen, C. From Commercialization to Accountability: Responsible Health Data Collection Use, and Disclosure for the 21st Century. Appl. Clin. Inform. 11, 366–373 (2020).

    Article  Google Scholar 

  125. 125.

    Spector-Bagdady, K., Hutchinson, R., Kaleba, E. O. & Kheterpal, S. Sharing Health Data and Biospecimens with Industry - A Principle-Driven, Practical Approach. N. Engl. J. Med. 382, 2072–2075 (2020).

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Alice Leiter, Senior Counsel, E-Health Initiative, for her assistance with earlier drafts of this article. K.D.M. was supported by cooperative agreement U01TR002623 from the National Center for Advancing Translational Sciences, National Institutes of Health and by the PrecisionLink initiative at Boston Children’s Hospital. D.M. receives a salary from Ciitizen Corporation.

Author information

Affiliations

Authors

Contributions

Both authors contributed equally to the drafting of this article. D.M.’s contributions come from her diverse experiences in health and privacy, including serving: as the head of the HIPAA division of OCR for 2.5 years; as a health privacy advocate at the Center for Democracy & Technology for 6 years; as an attorney for health care systems for 6 years; and, most recently, as a co-founder of a consumer health technology company for 2.5 years. K.D.M.’s contributions stem from his experience designing and deploying information technologies in lock step with regulatory considerations–including personally controlled health records, biosurveillance systems, participatory surveillance systems, EHR and data sharing networks, and widely adopted application programming interfaces for data exchange—as well has his contributions to the 21st Century Cures Act and ensuing rules.

Corresponding author

Correspondence to Deven McGraw.

Ethics declarations

Competing interests

D.M. is employed by and has stock in Ciitizen, a personal health record platform (www.ciitizen.com) that helps individuals collect, use, and disclose their health information to meet their needs. Ciitizen is a Board member of the CARIN Alliance, which advances the ability of individuals to get copies of their health information. K.D.M. chairs the scientific advisory Board for Medal, Inc. Boston Children’s Hospital receives corporate philanthropic support for K.D.M.’s laboratory from SMART Advisory Committee members which include the American Medical Association, the BMJ Group, Eli Lilly and Company, First Databank, Google Cloud, Hospital Corporation of America, Microsoft, Optum, Premier Inc, and Quest Diagnostics.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McGraw, D., Mandl, K.D. Privacy protections to encourage use of health-relevant digital data in a learning health system. npj Digit. Med. 4, 2 (2021). https://doi.org/10.1038/s41746-020-00362-8

Download citation

Further reading

Search

Quick links