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Precision Medicine and Sharing Medical Data in Real Time: Opportunities and Barriers
Y. Tony Yang, ScD, and Brian Chen, PhD, JD
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Precision Medicine and Sharing Medical Data in Real Time: Opportunities and Barriers

Y. Tony Yang, ScD, and Brian Chen, PhD, JD
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.
A Path Forward

Overcoming technical barriers. 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 2024.11 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.12 Value-based purchasing and performance-based payment also encourage the study of claims data to detect patterns and reward providers of high-quality care.

Possible legal solutions. 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.2 Safeguards should ensure that healthcare data can be used only by entities with sufficient technical capabilities to maintain security.2 The Affordable Care Act has also enacted standards for the collection of certain kinds of especially sensitive health data, such as race and ethnicity.10

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.5

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.10

Changing institutionalized barriers. One way to address privacy concerns is to create data stewardship guidelines.1 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.1 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.13 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.14

Policy makers should engage the patient community and explain the benefits of data collection in a concrete and tangible way.2 To such end, Jan Liphardt, PhD, of Stanford University, has proposed a patient-driven cancer database.15 The site will respect patient privacy by anonymizing data and following patient directives on what the data can be used for.15 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.15 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.15

Author Affiliations: 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.

Source of Funding: None.

Author Disclosures: 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.

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

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

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2. Heitmueller A, Henderson S, Warburton W, Elmagarmid A, Pentland AS, Darzi A. Developing public policy to advance the use of big data in health care. Health Aff (Millwood). 2014;33(9):1523-1530. doi: 10.1377/hlthaff.2014.0771.

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.

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6. Public Health Service Act, 42 USC §300kk(e).

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9. CDC/ATSDR policy on releasing and sharing data. CDC website. cdc.gov/maso/policy/releasingdata.pdf. Published April 16, 2003. Updated September 7, 2005. Accessed December 28, 2017.

10. Partnership for Public Health Law. Legal issues related to sharing of clinical health data with public health agencies. Association of State and Territorial Health Officials website. astho.org/Public-Policy/Public-Health-Law/Legal-Issues-Related-to-Sharing-Clinical-Health-Data-with-Public-Health-Agencies. Published April 2016. Accessed December 28, 2017.

11. Himmelstein DU, Woolhandler S. The current and projected taxpayer shares of US health costs. Am J Public Health. 2016;106(3):449-452. doi: 10.2105/AJPH.2015.302997.

12. Qualified entity program. CMS website. cms.gov/Research-Statistics-Data-and-Systems/Monitoring-Programs/QEMedicareData/index.html?redirect=/QEMedicareData. Updated October 13, 2017. Accessed December 28, 2017.

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

14. National strategy for trusted identities in cyberspace. National Institute of Standards and Technology website. nist.gov/sites/default/files/documents/2016/12/08/nsticstrategy.pdf. Published April 2011. Accessed December 28, 2017.

15. Huber J. Introducing CancerBase: a way to share personal medical data to help cancer research. Scope website. scopeblog.stanford.edu/2016/08/01/introducing-cancerbase-a-way-to-share-personal-medical-data-to-help-cancer-research. Published August 1, 2016. Accessed December 28, 2017. 
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