Y. Tony Yang, ScD

Sharing massive amounts of medical data is critical to precision medicine. The California Department of Public Health recently started to partner with certain hospitals in the state to better understand cancer trends by collecting and securely sending standardized cancer data directly to the California Cancer Registry. This initiative is the first of its kind in the United States. This has afforded the cancer registry the opportunity to perform real-time surveillance on data reported via participating hospitals, and researchers can use advanced methods to analyze these data. Other states are likely to follow California’s lead. However, there are barriers to increased data-sharing efforts. How these barriers can be addressed to facilitate data sharing while protecting individual privacy, reducing the risk of data misuse, and enhancing public trust becomes critical as precision medicine moves forward.

The California Department of Public Health recently started to partner with hospitals to better understand cancer trends by collecting and sending standardized cancer data directly to the California Cancer Registry.

This has afforded the registry the opportunity to perform real-time surveillance on data reported via participating hospitals, and researchers can use advanced methods to analyze these massive amounts of data.

There are barriers to increased data-sharing efforts. How these barriers can be addressed to facilitate data sharing while protecting individual privacy, reducing the risk of data misuse, and enhancing public trust becomes critical as precision medicine moves forward.

The growing use of big data and the ability to share data across entities and fields have been called the “biggest medical innovation in recent years” and hold great promise for the future of population health and precision medicine.

 Big data are characterized by “volume, variety, and velocity,” or the ability to store massive amounts of data, to analyze differently formatted data structures, and to do so quickly and efficiently.

 Big data can be used to deliver personalized medicine, help identify population health patterns in communities, and predict and track disease.

 California recently introduced a real-time cancer tracker, which allows pathologists and oncologists to upload current patient data into a central database.

 Researchers and providers will be able to use the database to recommend treatments for patients based on real-time information and outcomes.

 This model is promising, but there are barriers that may impede the system’s adoption elsewhere.

In 2014, the California Department of Public Health (CDPH) and the College of American Pathologists (CAP) began a pilot project to track cancer data in real time in a central database that will be accessible to providers across the state.

 One of the reasons California’s model is so novel is because of how the information is uploaded. The CDPH and CAP worked together to develop a standardized checklist into which physicians and providers enter patient data.

 Currently, pathologists write brief paragraphs about their observations and outcomes and send them to the California Cancer Registry. A researcher then reads the paragraphs and enters the information into the system.

A real-time registry offers many benefits. If enough hospitals adopt the same format, physicians will be able to use all of the standardized data to help determine treatment plans for their patients, including recommending clinical trials. The new format will also allow cancer centers to better conduct internal evaluations to detect problems with their laboratories or pathology departments. Currently, only a few of the hundreds of hospitals in California are utilizing the new format.

 As of today, there remain many barriers that impede speedy adoption.

 Most hospitals use an electronic health record (EHR) system, which, in theory, should give researchers immediate access to big data, but barriers exist. First, these systems vary and may not be compatible with other systems.

 Second, clinical trial and other research data are not standardized. Researchers work around these limitations by extracting, transforming, and loading data from EHRs into a “data lake,” which is a massive, easily accessible, centralized repository of large volumes of structured and unstructured data.

 Researchers can then create 1 large data schema, but this can be very expensive in time and resources, requiring intensive and customized data coding.

Also, some consumers may feel uncomfortable with their data being stored and accessed by multiple users, even when deidentified.

 There are also questions about whether anonymization truly deidentifies data. One study found, for example, that 40% of the participants in a deidentified DNA study were reidentifiable.

 Data sharing is regulated by federal and state laws. The content of the data, their identifiability, and the context of use determine how these laws apply.

 The Public Health Service Act limits the use of identifiable data and requires the secretary of HHS to ensure that data are protected from inappropriate use.

 The Health Insurance Portability and Accountability Act of 1996 (HIPAA) regulates what types of identifiable data can be disclosed by healthcare plans and providers.

 The HIPAA Privacy Rule, which applies to identifiable health information only, protects such data from being improperly disclosed by health plans, providers, clearinghouses, and business associates.

 The HIPAA Security Rule requires covered entities to establish structures to protect data from being breached or inappropriately disclosed.

 In addition, tax considerations restrict nonprofit organizations from entering into certain types of data-sharing arrangements.

 Many governmental agencies also have their own privacy and data-sharing policies.

The ability to collect public health data, as well as to disclose and share such data, varies by state.

 Some states have privacy protections that are stronger than those required under HIPAA

 or specific protections for certain diseases, such as HIV.

 Other state laws are comprehensive and apply generally to system participants who collect, acquire, use, or disclose information within the state.

 Federal and state antitrust laws may also regulate contractual information exchange and sharing arrangements to promote competition.

 Providers may have to consider state licensing requirements.

 Some states create legal obligations and requirements on government entities regarding government data, and others utilize broader privacy laws.

 Mandatory data-reporting laws also vary across states.

 Such variations in state laws may present barriers to information sharing.

 Institutionalized barriers must also be overcome. Of particular significance are patients’ privacy concerns and reluctance to share data.

 Many consumers are concerned that their data may be used by third parties, even though they frequently and voluntarily upload identifiable data on unsecure websites and social media.

 Consumers also fear that their data will be used in unethical or harmful ways,

 such as to discriminate against minorities.

 Patients want data to be shared if it benefits them, but they want to protect their data and keep them to themselves if they do not have a vested interest in sharing the data.

 In order to bring consumers on board, the government, health systems, and research institutions will have to prove that they can be trusted with sensitive health information and explain the exact purpose of data collection.

In addition, health systems are worried that their competitors will be able to use their data against them when competing for customers.

 Providers fear that if their health statistics are publicly available, they will lose patients or be sanctioned if they do not measure up in performance.

 The cost of implementation of data management and sharing software is also a barrier. Companies are reluctant to implement large data systems requiring high front-end costs if they do not see a clear financial benefit.

 The government has access to an enormous amount of healthcare data. In 2013, the government accounted for 64% of total healthcare spending in the United States, and this figure is expected to rise to over 67% by 2024.

 This presents a major opportunity for the federal government to lead the way toward innovative data collaboration. CMS already shares Medicare claims data with “qualified entities” in order to evaluate provider performance.

 Value-based purchasing and performance-based payment also encourage the study of claims data to detect patterns and reward providers of high-quality care.

 Legislative or regulatory fixes may help assure the public that their health data will not be compromised. Policy makers should swiftly punish the organizations and people responsible for data breaches to foster an environment of accountability.

 Safeguards should ensure that healthcare data can be used only by entities with sufficient technical capabilities to maintain security.

 The Affordable Care Act has also enacted standards for the collection of certain kinds of especially sensitive health data, such as race and ethnicity.

The government can clarify restrictions around data sharing without requiring legislation. Agencies can issue guidance and clarify language around existing statutes and regulations, including issuing guidance that coordinates agencies in order to clarify the ways in which all of the potential problem areas interact.

States can also remove barriers to data sharing by providing incentives to share data by working to connect public health agencies to providers, mandating the reporting of data and minimizing barriers that could limit reporting or data sharing, and sharing best practices so that other states can apply lessons learned to their own systems.

 One way to address privacy concerns is to create data stewardship guidelines.

 At the federal level, the Federal Trade Commission and the Organisation for Economic Co-operation and Development have created guidelines for researchers to use data in a fair and secure way.

 The Markle Foundation has created the Connecting for Health Common Framework for Private and Secure Health Information Exchange (Common Framework) that institutions can use as technical guidance when creating sharable data systems.

 The Common Framework is based on US Fair Information Practice Principles, which stress transparency, individual participation, purpose specification, use limitation, data security, and institutional accountability.

Policy makers should engage the patient community and explain the benefits of data collection in a concrete and tangible way.

 To such end, Jan Liphardt, PhD, of Stanford University, has proposed a patient-driven cancer database.

 The site will respect patient privacy by anonymizing data and following patient directives on what the data can be used for.

 The initial phase will only ask patients to answer 5 basic questions, such as “what is your diagnosis?” and “did your cancer metastasize?” Eventually, however, the team hopes to synthesize the data so that patients can help chart their own treatment plans by looking at what the data set shows for similar patients.

 A model like this represents a path forward for patient engagement and trust, the foundation upon which the future of big data must be built.

 Center for Health Policy and Media Engagement, George Washington University School of Nursing, and Department of Health Policy and Management, George Washington University Milken Institute School of Public Health (YTY), Washington, DC; Department of Health Services Policy and Management, University of South Carolina (BC), Columbia, SC.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (YTY); drafting of the manuscript (YTY); critical revision of the manuscript for important intellectual content (BC); and supervision (YTY).

 Y. Tony Yang, ScD, 1919 Pennsylvania Ave NW, Ste 500, Washington, DC 20006. Email: ytyang@gwu.edu.

1. Roski J, Bo-Linn GW, Andrews TA. Creating value in health care through big data: opportunities and policy implications. 

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3. California Department of Public Health (CDPH) Partners for breakthrough for sharing cancer data [news release]. Sacramento, CA: California Department of Public Health; July 27, 2015. cdph.ca.gov/Programs/OPA/Pages/NR15-051.aspx. Accessed December 28, 2017.

4. Bernstein AB, Sweeney MH; Centers for Disease Control and Prevention. Public health surveillance data: legal, policy, ethical, regulatory, and practical issues. 

5. Rosenbaum SJ, Painter MW. Assessing legal implications of using health data to improve health care quality and eliminate health care disparities. George Washington University website. hsrc.himmelfarb.gwu.edu/sphhs_policy_facpubs/253/. Published 2005. Accessed December 28, 2017.

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11. Himmelstein DU, Woolhandler S. The current and projected taxpayer shares of US health costs. 

. 2016;106(3):449-452. doi: 10.2105/AJPH.2015.302997.

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13. Markle Common Framework. Markle website. markle.org/markle-common-framework-connecting-professionals. Accessed December 28, 2017.

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Articles

Facilitating real-time data sharing while protecting individual privacy, reducing the risk of data misuse, and enhancing public trust becomes critical as precision medicine moves forward.

Precision Medicine and Sharing Medical Data in Real Time: Opportunities and Barriers

On March 1, 2016, the Supreme Court issued a 6-2 decision in 

, holding that the federal Employee Retirement Income Security Act (ERISA)—a comprehensive federal law that regulates a variety of employee benefits, including health plans established by employers&mdash;preempts a Vermont law requiring self-funded employer health insurers to submit certain types of health data to state databases.

 Vermont had passed the law to collect and aggregate state-level health data, with the goal of understanding and addressing rising healthcare costs.

 As increasing numbers of employees receive coverage from self-funded employers, their healthcare data have become important to ensure transparency and to craft state-level healthcare policy. Although some laud the Supreme Court decision as a win for medical privacy, the case highlights the challenges in managing conflicting state and federal laws, and balancing patient privacy interests against the state’s interests in healthcare reform.

Background: Vermont’s Law, its Opposition, and the Court’s Holding

Enacted in 2005, the Vermont law aimed to improve healthcare quality by collecting various health-related data, including “medical claims data, pharmacy claims data, member eligibility data, provider data and other information.”

 In particular, the law required self-funded insurance providers to submit, upon request, healthcare information on prices and annual registration forms, and to report claims at specified intervals.

 In the recent past, a growing number of states have established databases that collect health insurance claims information from all healthcare payers into a statewide information repository. By January 2016, Vermont and 17 other states had enacted “all-payer claims databases,” while more than a dozen others considered such a law or program.

Liberty Mutual Insurance Company, which administers self-insured health plans for over 80,000 individuals nationwide,

 objected to the Vermont law, claiming its disclosure requirements violate its fiduciary duties under ERISA.

 Vermont had issued a subpoena to demand that Liberty Mutual release medical and pharmacy claim files pursuant to Vermont law, and the company responded by filing suit—claiming that ERISA preempted Vermont’s data-reporting requirement for self-insured plans. The district court sided with Vermont, but the Second Circuit Court of Appeals reversed the lower court’s decision and ruled in favor of Liberty Mutual.

 The Second Circuit placed a heavy emphasis on the privacy risks associated with this type of reporting requirement and found that such privacy risks would burden beneficiaries of the plans.

The Supreme Court subsequently granted certiorari, and, with little difficulty, found that ERISA did preempt the Vermont law.

 The Court noted that the only relevant question is whether Congress intended ERISA to preempt state legislation, such as Vermont’s. Because the Vermont law both “govern[ed]…a central matter of plan administration” and “interfere[ed] with nationally uniform plan administration,” the Court found that the law was of the very type that Congress intended ERISA to preempt.

 To allow states to have their own reporting requirements would “impos[e] novel, inconsistent, and burdensome reporting requirements on plans.”

Unaddressed Controversy: Should Privacy or Data Drive Healthcare Policy?

Unlike the Second Circuit’s ruling, the Supreme Court’s opinions (majority, concurring, and dissenting) made little of the privacy aspect, noting only that Liberty Mutual challenged the law to protect the privacy of individual medical records.

 None of the justices, however, expressed any issue with government collection of individual healthcare information.

 The majority opinion even suggested that the Secretary of Labor could require ERISA plans to report individual claims data to a national or state database, indicating that the Court saw no privacy issues at stake, so long as statutes and policies meet the requirements of ERISA.

Although the Court barely addressed the issue, many have recognized an inherent conflict between protecting medical privacy and collecting healthcare information to shape effective policy. Eighteen states have created such databases to determine the nature and distribution of healthcare costs, in hopes of implementing policies and regulations that could reduce costs.

 As Justice Ruth Bader Ginsberg noted in her dissent in 

, “Stopping states from collecting claims data from self-insured employer healthcare plans would thus hugely undermine the reporting regimes on which Vermont and other States depend to maintain and improve the quality, and hold down the cost, of healthcare services.”

, states can no longer mandate data reporting from the majority of employees nationwide, given that about 63% of covered workers are enrolled in a plan that is partially or entirely self-funded.

Likewise, the American Hospital Association noted that it is essential that self-funded insurers be included in all-payer databases if those databases are to realize their potential.

 The American Hospital Association and the Association of American Medical Colleges agreed that hospitals have only the data for the patients they treat, but information from an individual’s broad healthcare experience is needed to inform clinical, payment, and public health policy.

 Complete data is necessary to conduct useful analyses of the healthcare system, across its entire spectrum, to make meaningful changes. Striking down these laws may also reduce the transparency necessary to encourage insurers participating in the healthcare marketplace and improve overall state health.

Opponents of the law, however, fear potential data breaches in the large-scale transmission and storage of personal health data, and suggest that any savings and increased efficiency will come at a high cost. Thus, said Twila Brase, president of Citizens’ Council for Health Freedom, in reaction to the ruling, “Today is a good day for medical privacy in America.”

 The ruling means that fewer patient medical records are at risk of privacy violation.

 Many, including the Association of American Physicians and Surgeons (AAPS) believe that patient privacy cannot adequately be protected in large government databases.

 Large, centralized databases are vulnerable to hacking, and are targets for cyber-attacks, putting the private medical information of millions of Americans at risk of public disclosure.

The Second Circuit explicitly acknowledged that the laws “require[…] ERISA plans to record, in specified format, massive amounts of claims information and to report that information to third parties, creating significant (and obvious) privacy risks and financial burdens.”

 Moreover, de-identification may do little to protect individual privacy; researchers at Harvard demonstrated that de-identified private medical information can be re-identified.

 The potential public disclosure of this private information, through breach or re-identification, could adversely impact careers and family lives, causing a disincentive against seeking care for stigmatized medical conditions. The AAPS believes that ultimately, the medical profession, rather than “big brother” states, is the appropriate actor to spearhead healthcare cost reductions.

Implications for the Future of Healthcare Data Collection After 

 hampers efforts to track the quality and cost of care, it implies that, at least for now, workers in America covered under self-insured plans are protected from the mandatory release of their private medical records to state governments. Advocates of privacy rights are not alone in celebrating the decision.

 Many employers and health insurers see the decision as a significant victory for self-funded insurers who otherwise would have to comply with conflicting patchworks of state laws and regulations.

 Without the ruling, large self-funded employers that operate in multiple states could face a myriad of different regulations in each state, leading to high administrative costs and conflicts in compliance requirements. The new ruling alleviates that burden by ensuring that self-insured employers all face one uniform set of national regulations. Uniformity in reporting helps with efficient plan administration.

The majority opinion did, however, suggest a way in which states might obtain the information they desire—through the federal government.

 The Secretary of Labor could, for example, promulgate regulations to require self-funded insurers to report healthcare data to the federal government, who could then distribute this information to the states.

 Moreover, the ruling does not prevent self-funded insurers from voluntarily reporting health information to the states.

These technical workarounds to comply with the Court’s decision, however, may overlook the risk of privacy violation. Although many can appreciate the need for centralized healthcare databases and the critical analysis regarding the costs and quality that they enable for policy formulation, the Court’s reticence on privacy issues associated with data collection may suggest that it finds no inherent privacy violation in massive, government-collected databases of private medical information.

 was silent on the privacy issues surrounding health databases and the ruling was merely a technical application of the doctrine of federal preemption, according to which federal law trumps state law when the two are in conflict, this case may pave the way for the federal government to collect health plan data or to approve or delegate to a state the authority to collect such data. As one physician put it, however, “[W]e need to foster a public debate over citizens’ right to medical privacy versus government seizure of private information without consent, because any loss of privacy rights undermines our society, and threatens our freedoms and our way of life.”

 A rigorous analysis of the risks and benefits of government-mandated healthcare databases, and investment in improved technology to protect such data already held by the government, are of utmost importance.

 Department of Health Administration and Policy, George Mason University (YTY), Fairfax, VA; Department of Health Services Policy and Management, University of South Carolina (BC), Columbia, SC. 

 Y. Tony Yang, MS, 1J3, 4400 University Dr, Fairfax, VA  22030. E-mail: ytyang@gmu.edu.

, No. 14-181 (2016). US Supreme Court website. http://www.supremecourt.gov/opinions/15pdf/14-181_5426.pdf. Accessed June 2016.

2. The Vermont statutes online. Title 18: Health; Chapter 221: Health Care Administration; Subchapter 001: Quality, Resource Allocation, And Cost Containment; 

Health care database. State of Vermont Legislature website. http://legislature.vermont.gov/statutes/section/18/221/09410. Accessed June 2016.

3. Interactive state report map. All-Payer Claims Database Council website. https://www.apcdcouncil.org/state/map. Accessed March 24, 2016.

, No. 12-4881-cv. 746 F.3d 497 (2d Cir, 2014). Leagle website. http://www.leagle.com/decision/In%20FCO%2020140204086/LIBERTY%20MUT.%20INS.%20CO.%20v.%20DONEGAN#. Accessed June 2016.

, No. 99—1529. 532 U.S. 141, 148 (2001). https://www.law.cornell.edu/supct/html/99-1529.ZS.html. Accessed June 2016.

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Gobeille v. Liberty Mutual Insurance Company

. National Academy for State Health Policy. http://www.nashp.org/wp-content/uploads/2015/11/Gobeille-Amici-table11.pdf. Accessed March 24, 2016.

8. Woodward C. Brase on database: court ruling ensures privacy. One News Now website. http://www.onenewsnow.com/legal-courts/2016/03/02/brase-on-database-court-ruling-ensures-privacy. Published March 2, 2016. Accessed March 24, 2016.

9. Sweeney L. Policy and law: identifiability of de-identified data http://latanyasweeney.org/work/identifiability.html. Accessed March 24, 2016.

10. Israel S. The illusion of patient privacy and private practice. 

Gobeille v. Liberty Mutual Insurance Company highlights the challenges in managing conflicting state and federal laws, and balancing patient privacy interests against the state’s interests in healthcare reform.

Y. Tony Yang, ScD

Articles

Precision Medicine and Sharing Medical Data in Real Time: Opportunities and Barriers

Medical Record Privacy and State Health Reform After Gobeille v. Liberty Mutual Insurance Co.