Clinical Data as the Basic Staple of Health Learning Creating and Protecting a Public Good: Workshop Summary (2010) / Chapter Skim
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4 Healthcare Data: Public Good or Private Property?
4 Healthcare Data: Public Good or Private Property?
Pages 137-170
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From page 137... ...
One of the goals of Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good was to evaluate the nature of goods, both public and private, in the healthcare data marketplace and to propose concepts, opportunities, and guidance for improving access to and sharing of medical data. This chapter reviews perspectives on clinical data; effects of the medical care data marketplace on research priorities, gaps, and possibilities; characteristics of a public good or utility -- and on which dimensions healthcare data compare; distinctions that can be made within data types or sources; barriers to broader sharing of and access to medical data; and the conceptual advances, guidance, or policy needed.
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From page 138... ...
The potential to support evidence-based medicine through the wide variety of prescription drug and medical databases continues to grow because these data can offer greater insight into the practices of care delivery and safety surveillance. Current data sources have been constructed to serve as potential resources for research and commercial endeavors.
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From page 139... ...
This ideology concerns first and foremost the role of markets in our polity, and assumptions about what markets do well and do not do well. Decisions about the management of health information will involve politics at many levels and they will, consequently, involve ideology.
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In accordance with a Roundtable theme, we must correct the market failure for expanding electronic health records. A good is nonexcludable if, even if wholly owned and paid for, its use and benefit by others cannot be prevented.
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These are a little more relevant to our discussion of healthcare data. A quasi-public good is one whose production or consumption generates or might generate effects on third parties.
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From page 142... ...
In the course of this workshop's discussions of large private clinical databases, some of the examples used are of groups that have made public the data they collect available to third parties essentially free of charge, usually out of altruism. That is wonderful, of course, but as a society we have not organized ourselves around altruism as a guarantee of any particular outcome.
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From page 143... ...
; linked enrollment; medical and drug claims databases from commercial health plans and large self-insured employers; and combinations of such databases assembled and made available in the form of commercial databases by data aggregators such as Ingenix, Medstat, and Pharmetrics. In addition, there are a variety of government databases, including state Medicaid files (Medicaid Statistical Information System [MSIS]
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From page 144... ...
Although not necessarily available to outside researchers, electronic medical record (EMR) databases exist for several health plans.
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From page 145... ...
We end up using the other types of data for research, but that was not their original purpose. Although voluminous for their service types, the large inpatient databases such as HCUP and the NIS, as well as the prescription drug databases from organizations such as IMS and Wolters-Kluwer, typically are not linked to other data types such as outpatient medical claims.
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From page 146... ...
For example, how do you effectively combine information from different health plans, where one plan has fee-for-service dollar amounts attached to each service provided and another plan is fully capitated and only the service encounters are recorded? Similar issues arise in the pooling of medical records data across multiple sources when these sources use different medical record systems.
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From page 147... ...
The discussion thus far has focused on deidentified databases. However, in certain situations, such as in large health plans or physician practices, there is also the possibility to access protected health information (PHI)
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From page 148... ...
Large retrospective claims databases can be particularly useful for safety signal detection. However, because of a variety of issues about the reliability of diagnostic coding in such databases, it is desirable to have access to medical records for the patients represented in the data.
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From page 149... ...
In terms of the trade-offs between a pooled mega-database and pulling data from different data aggregators, the need is growing for a mega-database that would pull data from different health plans, the Department of Veterans Affairs, Medicare, and so forth. Then we would need to standardize the data, and create a public good that will be available for research, for cost-effectiveness studies, and for real-world drug safety to be able to understand guideline compliance of physician practices and other issues.
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From page 150... ...
Even the construction of a pooled database built from similar data streams (e.g., commercial health plans) is a huge task.
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From page 151... ...
that are designed to reduce or eliminate negative externalities suffered by data subjects. This paper identifies the major clusters of legal rules that create barriers to clinical data morphing into a public good: property or inalienability rules, federal–state disconnects, and evolving data protection models.
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From page 152... ...
Similarly, exchanges between data stewards that facilitate nonrival consumption of information properties (including interinstitution sharing of records data for outcomes research and the sale of clinical data for marketing purposes) or novel public goods exceptions to nonexcludability regimes may impose negative externalities on the data subjects.
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From page 153... ...
Such consequences range from the technical (e.g., regulatory safe harbors notwithstanding, stark and antikickback barriers to market transactions between providers to accelerate the adoption of e-prescribing and electronic health records, or EHRs2) to the conceptual (e.g., the Health Insurance Portability and Accountability Act's, or HIPAA's, compliancebased, provider-centric data protection model that tends to confirm the proprietary, or private goods, nature of clinical data by encouraging providers to wall off the data as "theirs" rather than treat it as held in trust for their patients or the public)
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From page 154... ...
may retain proprietary rights in that technology and so to an extent the records built on that platform (Harty-Golder, 2007)
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. Notwithstanding, there is authority that medical records software is patentable (Micro Chem., Inc.
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From page 156... ...
. The stewards of clinical data (again, in part motivated by data protection laws)
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From page 157... ...
For example, a recently defeated New Hampshire bill (House kills medical privacy bill, 2008) would have increased data protection considerably beyond HIPAA protection standards by restricting data use to the point of care, thereby potentially outlawing many marketing and research uses (Guay, 2008; U.S.
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From page 158... ...
projects (Miller and Miller, 2007) , state actors may now perceive that the Office of the National Coordinator for Health Information Technology (ONCHIT)
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From page 159... ...
Data Protection The need to protect the privacy of health information is broadly accepted, yet the mechanisms for its assurance continue to be controversial. Pre-HIT protection for medical records was formally achieved with a patchwork of state statutory and common-law rules.
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. This HIPAA data protection model has several important (and unsatisfactory)
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. Personal Health Records and Consumer-Directed Health Care Perhaps the greatest flaw of the HIPAA data protection model is how quickly it has been rendered wanting by new technologies.
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. Jacqueline Lipton has argued more broadly that proprietary rights in "information property," while necessary to provide incentives and protect private property, must be balanced by broad new duties placed on rights holders, such as obligations of accuracy, confidentiality, and "an obligation to facilitate scientific, technical, and educational uses of information."16 Agreement on how to operationalize such an approach has been elusive.
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From page 163... ...
. Data Protection Models The HIPAA privacy and health records debates have been marked by a serious disconnect between data custodians and government policy makers on one side and privacy advocates on the other.
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From page 164... ...
The United States is not alone in confronting this tension between data protection and public utility. For example, a recent report by the New Zealand Law Commission noted, "there remains an outstanding issue as to whether there is a strong enough public mandate for the use of personal health information without consent for research in the public good." Similarly, a 2007 Canadian study noted very high (89 percent)
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From page 165... ...
The difficult question, however, is how to implement this more robust data protection model. In its Stewardship Framework report (NCVHS, 2007)
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From page 166... ...
The federal legislative logjam on matters such as genetic discrimination, HIT funding, and effective data protection must be cleared to reduce the barriers posed by an escalating number of state "solutions." However, the larger and more substantive barriers are as much a function of underlying policies as their legal transcription. Information properties in data and an inability to agree on an effective data protection model create immensely difficult barriers.
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From page 167... ...
2002a. Other requirements related to uses and disclosures of protected health information.
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2008. Failure to maintain proper medical records; altering medical records; making false report; failure to file or obstructing required report; failure to allow inspec tion and copying of medical records; failure to report other person in iolation of chapter or regulations.
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2008. Legislation around retention of medical records.
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year=2008&bill=6241 (accessed February 24, 2009)
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