PatientsChartingCourse

BOX 3-2  Continued

complexity and revealing new insights about important healthcare issues. For example, The County Health Map, created by Ben Fry, allows users to explore County Health Rankings data using key indicators. The resulting maps unveil hot spots and highlight the factors that influence the health of a community. The maps are designed to allow users to see both broad U.S. patterns as well as individual county metrics.

Pillbox—Pillbox, from the National Library of Medicine (NLM) and FDA, has transformed FDA’s drug label data into a platform for innovation. Working with patient, clinical, and HIT communities, the Pillbox team broke through barriers to the creation of applications based on FDA’s drug label data. Opening Pillbox’s data, images,­ and search function enabled the building of innovative applications that improve the health of citizens. Applications currently in development include voiceactivated and instant-messaging pill identification systems, as well as iPhone medication identification and information applications. A group of programmers at the Great American Hackathon used Pillbox’s data to begin development of a Facebook game, writing hundreds of lines of open source code in the process. This fall, as part of the Community Health Data Initiative, the Pillbox team will repeat this process with other government health data sets.

County Health Rankings—The County Health Rankings—a collaboration between the University of Wisconsin and Robert Wood Johnson Foundation (RWJF)—uses a model to describe the overall health of a community, as well as the factors that affect health. This annual “check-up” for each county in the United States allows everyone to compare the health of where they live to that of their neighbors or other counties in their state. Following the February 2010 release of the interactive website (www.countyhealthrankings­.org), the Rankings have garnered significant attention throughout the nation. The use of summary measures of health and ranking within states engages the news media and policy makers, leading to community-wide collaborations to “dig deeper” and better understand the problems, as well as to seek solutions.

Quick Health Data Online (HHS Office on Women’s Health)—Quick Health Data Online is an interactive database that provides stateand county-level data for all 50 states, the District of Columbia, and U.S. territories and possessions. Data are available by gender, race, and ethnicity and come from a variety of national and state sources. The system is organized into eleven main categories, including demographics, mortality, natality, reproductive health, violence, prevention, disease, and mental health. Within each main category, there are numerous

number who will buy homes in the next 5 years. Such an application also has the potential to create a new business case for investing in health at the community level, as it transforms health improvement from something that is a good thing to do into something that also affects property values, a principal revenue source for communities.

This innovation—just one of the many possibilities with increased availability of data—underscores the need for public participation and collaboration to develop creative applications that serve local needs. Because creating applications requires users to gain familiarity with the data, such work also helps HHS identify opportunities to improve the quality and utility of the data to support learning and health system and health improvement. The ecosystem approach therefore solves two problems. First, data that were previously collected but not used are being applied in a timely way to generate a discernable social return and a means to improve data quality. Second, because of these returns, a business case is developed for HHS to produce and deliver more data as a vehicle to engender social change.

At a follow-up meeting hosted by the IOM on June 3, 2010, the 11 volunteer teams showcased their results to an audience that included individuals interested in joining the work under way or starting new projects, organizations that could supply data beyond those captured by HHS, and community leaders who could use the applications to improve health.

As a result of these initial discussions, the potential impact of these data has dramatically expanded. For example, Clay Johnson of the ­Sunlight Foundation has proposed launching a great American hack-a-thon for health, in which advocacy organizations and nonprofits would compete to develop innovative ideas for applications that leverage community health data to advance their mission. The best ideas would then be handed over to hundreds of hackers in 30 different cities who would volunteer for a weekend out of the year to build these applications. Other participants, such as Ingenix, are considering contributing data to help make some of the proposed applications more robust.

Concluding Thoughts

The spirit of commonwealth and of “all hands on deck” emerging from efforts to foster open government is essential to a data ecosystem and to a learning health system. Initial results of HHS’s Open Government Plan also illustrate the important catalytic role government can play in health system and health improvement. Instead of developing a centralized system to control data access and use, HHS can supply data to all and help consolidate and catalog ecosystem information so that data use can be driven by and serve the needs of the community.

Opening access to health-related data from the public and private sectors could be transformative for improving the health of Americans and the effectiveness of the healthcare system. However, people of all talents, capabilities, and resources must come together to realize this potential. HHS needs guidance from the public about what data it should capture and release and how the data can be used to advance social missions. As good as the HHS data are now, they will become much better over time with public input. The dialogue should be about how HHS and the public, in the spirit of open government, can collaborate on an ongoing basis to achieve the promise of a learning health system.

ENSURING DATA INTEGRITY: ADDRESSING PRIVACY

PROTECTION AND PROPRIETARY CONCERNS

Don E. Detmer, M.D., M.A.

University of Virginia

In the American context, a worthy society is exemplified by a healthy citizenry pursuing happiness through the beneficence of sound governance and positive expressions of personal freedom. Key to those positive expressions of personal freedoms is the exploration of “the illimitable expressions of the human mind.”4 The nation’s origins were suffused with the optimism and courage characteristic of the Enlightenment. Discovery and support of progress across all dimensions of human activity were central themes, as characterized by efforts as diverse as the pursuit of copyright law and the rapid spread of smallpox vaccination (Sheldon, 2010). As Ralph Waldo Emerson noted, however, “The first wealth is health.”5

These early emphases on discovery and improvements in health and health care were underscored 150 years later when, in the midst of a great world war, Franklin Delano Roosevelt dedicated the NIH for the discovery of cures for the maladies plaguing humanity. Over the years, this national investment has benefited the care of Americans and people everywhere, while also leading to major improvements in the health of domesticated and even wild animals.

The Blue Ridge Academic Health Group has advocated for some years for a value-driven healthcare system (Blue Ridge Academic Health Group, 1998, 2004) that continually improves the quality of care and practices ­evidence-based management of illnesses. To secure such a system requires

4  See http://wiki.monticello.org/mediawiki/index.php/Illimitable_freedom_of_the_human_ mind_(Quotation) (accessed October 11, 2010).

5  See http://www.notable-quotes.com/e/emerson_ralph_waldo.html (accessed October 11, 2010).

that the entire enterprise focus on learning and become a learning health system (IOM, 2007). Requirements for a learning health system include

(1) widespread access to patient information to better understand the comparative effectiveness of treatments and foster ongoing research into less understood conditions; (2) policies and procedures that do not waste scarce resources, including money and the valuable time of health professionals;

(3)active engagement of patients in their care to improve their understanding of and commitment to treatments of known effectiveness; and

(4)ongoing, credible systems that preserve privacy and manage patient information securely to achieve data integrity for both healthcare operations and research of all types (e.g., biomedical, public health, and health services research).

Recently, Secretary of Health and Human Services Kathleen Sebelius noted: “The key to benefiting from change will be in ensuring that healthcare decision makers are guided by science and research” (Sebelius, 2010). With the passage of the ACA, access to basic healthcare services was affirmed as worthy of the investment in terms of national health status and social productivity. The absence of prior legislation providing all citizens with access to basic healthcare services gave rise to fears that unauthorized distribution of personal health data would lead to healthcare uninsurability. With the passage of ARRA, the Genetic Information Nondiscrimination­ Act of 2008, and the ACA, we have now entered a new era where this concern is moot.

Secretary Sebelius wants decisions to be based on evidence and research. To this end, public policy must support a learning health system. Further, citizens should be actively engaged in the support of legitimate healthcare research and institutional review boards (IRBs). The nation now has stringent policies and regulations, including both civil and criminal policies, relating to the use and protection of person-specific health information. Indeed, in 2009, ARRA added so many new protections that many experts consider this law to have created a second incarnation of the privacy provisions of the Health Insurance Portability and Accountability Act (HIPAA) of 1997. Even before ARRA was passed, multiple expert groups involved in the nation’s research enterprise had examined the impact of HIPAA on a range of biomedical, public health, and health services research. An IOM report reviewed all such studies and offered the conclusion that improvements to HIPAA were needed to facilitate better privacy protections (some of which are included in ARRA), as well as better access to data for research (IOM, 2009).

Another component of ARRA is a mandate that the government assess the comparative effectiveness of treatments to develop greater understanding of which treatments are most effective. This mandate specifically calls for health services research that will require access to person-specific health-

care data. The time has come to put before the American people the opportunity to support the discovery of new treatments and help ensure that only those resources needed to support research and protect data integrity and security are expended. At the same time, the research enterprise should be constructed so as to offer the greatest levels of patient safety and highest quality of care for standard practices.

To address these needs, Congress should introduce and pass a law that will facilitate the achievement of a number of important objectives with respect to biomedical, public health, and health services research. This law should include a number of features (Box 3-3). First, it should enable Americans to have a unique personal health identifier, which will facilitate the integrity of research data. The identifier should also be allowed to be used for authentication of routine care unless the individual wishes to formally opt out of such usage. Second, the law should mandate that all research studies meeting HIPAA requirements and having been approved by an IRB have access to nonanonymized health record data for legitimate research purposes. Further, citizens should explicitly be given the opportunity to opt out of the accessibility of any personal genetic information for research that meets HIPAA requirements and has been approved by an IRB to use nonanonymized health record data for legitimate research purposes. Any breaches of data security relating to research use would fall within

BOX 3-3

Components for a “Health Research and Safe Care Act of 2011”

Citizens have the opportunity to

•opt out of selection of a unique personal health identifier for use in research databases (including an option for the identifier to be used for routine personal health care);

•opt out of consent to share their personal health data for IRB-approved research that complies fully with HIPAA security regulations and requirements without any data anonymization; and

•opt out of consent to share their personal genetic data, if available, for IRBapproved research that complies fully with HIPAA security regulations and requirements without any data anonymization.

In addition:

•use of anonymized data would continue to be allowed without explicit personal consent; and

•public–private partnerships would be able to allow researchers to identify potential research participants, who might then be approached to take part in clinical studies for which consent is needed.

the current boundaries of HIPAA and ARRA as defined above. Finally, a public–private collaboration should be established to develop a process by which citizens interested in becoming involved in a research protocol that may apply to them as a result of preliminary data screening could be located and offered information on potential inclusion in the study.

Some further explanations are appropriate to better understand the logic behind the above policy proposal. The above proposal flows from the 2008 policy conference of the American Medical Informatics Association (AMIA). AMIA held a conference on “Informatics, Evidence-based Care, and Research: Implications for National Policy” (Bloomrosen and Detmer, 2010). A diverse group including federal regulators, citizens, ­researchers, and informaticians, concluded that national policy must be revised to secure a learning health system, improve and ensure data integrity, attend to data security and privacy, and facilitate research. The group concluded that only through a refocusing of public policy with respect to data access could these objectives be met. It concluded that a learning health system requires broad access to personal health data. Further, citizen support for legitimate biomedical and health research should be facilitated because today the entire burden of engaging citizens in research falls not on society but on researchers and IRBs, thereby creating significant disincentives for caregivers­ and researchers to pursue many promising lines of inquiry. In short, at this time, federal policy adversely impacts legitimate biomedical and health research by restricting data access, and there is no evidence that this is the will of most Americans.

The perspective of clinicians is important to data integrity as well. With the drive to make EHRs the norm for clinical practice settings, it is important to motivate the clinical community to be highly committed to data integrity. Payment for “meaningful use” holds promise to at least encourage recording data to enhance payment rather than spending the time to capture the exact clinical situation. There is a real possibility that data quality will benefit from clinicians learning that their data will be reviewed for both research purposes and for assessing their clinical practice behavior for quality assurance and maintenance of licensure. EHRs have the potential to reduce the quality of clinical notes as well as improve care through better data access, and it is wise not to overlook the role that support for professionalism can play in a contractual regulatory world.

At the broader policy level, instead of supporting research, current policy focuses overwhelmingly on privacy protection of personal health information, and such federally enforced protections continue to mount with neither the ability nor support by citizens to alter this policy course. Further, with Americans strongly in favor of legitimate biomedical, public health, and health services research, no policy exists to encourage the system to

Recently, Massachusetts has used an opt-in option for gaining informed consent to share personal data among regional caregivers using a unique health identifier. Upward of 90 percent of patients opt in to data sharing, but the monetary costs to maintain this approach are significant, and substantial time is required by clinicians as well. Meanwhile, in Minnesota, a data sharing system has been in play for some years. It too uses an opt-in option, and experience with this system mirrors that in Massachusetts (e.g., monetary costs and clinician and other administrative costs are substantial). Finally, Utah has had an opt-out program for children’s health data whose original focus was on minimizing underand overimmunization­. Roughly 3 percent of parents opt their children out, and the system is much less expensive than those of Massachusetts and Minnesota. Public acceptance is thus shown by these real-world experiences to approach or exceed 90 percent for sharing of data for care purposes.

With respect to such options and biomedical research, Daniel Masys of Vanderbilt University offered to the author the following personal communication relating to deidentified data:

Vanderbilt has an opt-out model for building a biobank of DNA samples from blood left over from clinical testing linked to de-identified electronic medical record (EMR) data that currently has more than 63,000 samples in it, growing at about 800 per week. The predicted opt-out rate before the project started, based on focus groups and a 5000 person patient survey was five percent. The observed opt-out rate over the past two years, across about one million consent-for-treatment signing events, is about 3.8 percent. Surveys of those who did opt out suggest an “anti-science” phenotype that is suspicious of all organizations, a belief that commercial profit motives are behind any veneer of science altruism. Most are personally convinced to a degree that no additional information would change their minds. These data seem to be a useful validation of the estimated size of an often vocal minority who figure so prominently in all of the public debates about research, uses of clinical data and electronic data systems. . . . This is not to say that such views are not valid and they clearly must be accommodated in discussions of architecture and policy, but they often seem to occupy a disproportionate amount of public dialog and pedagogy. (Masys, 2009)

If the recommended opt-out option were signed into law, one might predict that, based on available data and with sensible administration, well over 75 percent of the American public would “sign up.” This would benefit research immensely and save billions of dollars and thousands of hours as well.

In conclusion, two quotations from James Madison relate directly to this public policy issue and the challenge facing those seeking to help

Americans find more cures sooner through the passage of legislation such as that suggested here. Madison noted: “A people who mean to be their own Government, must arm themselves with the power to which knowledge gives.”7 Progress comes only when information flows. Madison observed in a letter to Benjamin Rush that he was no stranger to the clash of deeply felt values and how the intensity of belief can warp perception. He stated: “Nothing is so contagious as opinion, especially on questions which, being susceptible of very different glosses, beget in the mind a distrust of itself.”8 One cannot but reflect on this quotation in the context of the continuing health data privacy wars of the past two decades. Within the Beltway, it is commonly taken as fact that patients are more concerned about the privacy of their data than anything else and that trust in health care and EHRs is based overwhelmingly on assurance of this privacy. What facts exist reveal that this is so much finely glossed opinion. According to Mechanic and Meyer (Mechanic and Meyer, 2000), trust in health care is based on three main pillars and a fourth that sometimes is not even mentioned. First and foremost is the ability of the clinician to communicate and connect with the patient. Second is the perceived competence of the clinician. Third is whether the clinician is willing to advocate for the patient’s benefit if necessary. If it is mentioned at all, maintaining privacy and confidentiality of data is fourth.

Americans should be given the option to become full and open participants in a learning health system. To do otherwise is to prevent them from being either Americans in the finest sense of that word or adults with respect to their care and concern for their fellow beings. The right of Americans to be left alone must not be allowed to become a right to be unknown.

REFERENCES

Allcott, H., and S. Mullainathan. 2010. Behavior and energy policy. Science 327:1204-1205. Bloomrosen, M., and D. Detmer. 2010. Informatics, evidence-based care, and research; implications for national policy: A report of the American Medical Informatics Association’s health policy conference. Journal of the American Medical Informatics Association

17:115-123.

Blue Ridge Academic Health Group. 1998. Academic health centers: Good health is good business. http://whsc.emory.edu/blueridge/_pdf/blue_ridge_report2.pdf (accessed October 11, 2010).

———. 2004. Converging on consensus: Planning the future of health and health care. http:// whsc.emory.edu/blueridge/_pdf/blue_ridge_report_8_2004oct.pdf (accessed October 11, 2010).

7  See http://presspubs.uchicago.edu/founders/documents/v1ch18s35.html (accessed October 11, 2010).

8  See http://www.revolutionary-war-and-beyond.com/james-madison-quotes-6.html (accessed October 11, 2010).