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cerns about causal interpretations remain. Randomized studies embedded in routine care that assess patient outcomes by means of electronic medical record databases are cost-effective and reduce residual imbalances in patient characteristics An audio interview with Dr. Schneeweiss at the start of a is available at NEJM.org study. The Patient Centered Outcomes Research Institute recently launched a major initiative to build a nationwide network of health care systems that will use their infrastructure for such pragmatic randomized trials. Ultimately, a key to success in learning from big health care data will be to remain focused on our ultimate goal: gaining ac-

Learning from Big Health Care Data

tionable insights into the best ways to treat the patients in the care system that generated the data. If we work backward from this goal, agreeing on the right analytic methods and the necessary data will be manageable steps, and together we’ll be able to negotiate the critical issues of data privacy and standardization. Dr. Schneeweiss reports serving as a consultant to WHISCON and to Aetion, a software manufacturer in which he also owns shares. No other potential conflict of interest relevant to this article was reported. Disclosure forms provided by the author are available with the full text of this article at NEJM.org. From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston.

1. The learning health care system in America. Washington, DC: Institute of Medicine, 2012 (http://www.iom.edu/Activities/Quality/ LearningHealthCare.aspx). 2. Choudhry NK, Anderson GM, Laupacis A, Ross-Degnan D, Normand SL, Soumerai SB. Impact of adverse events on prescribing warfarin in patients with atrial fibrillation: matched pair analysis. BMJ 2006;332:141-5. 3. Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 2005;58:323-37. 4. Faden RR, Kass NE, Goodman SN, Pronovost P, Tunis S, Beauchamp TL. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics. Hastings Cent Rep 2013;43:Spec Rep:S16-S27. 5. Gabriel SE, Normand SL. Getting the methods right — the foundation of patientcentered outcomes research. N Engl J Med 2012;367:787-90. DOI: 10.1056/NEJMp1401111 Copyright © 2014 Massachusetts Medical Society.

Fostering Responsible Data Sharing through Standards Rebecca Kush, Ph.D., and Michel Goldman, M.D., Ph.D.

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hildren with muscular dystrophy and their families make sacrifices to engage in clinical research studies, providing valuable data they expect will contribute to the discovery of a cure, although they know it may not be found in time to help them. This message was emphasized at a recent meeting organized by the Institute of Medicine, where clinical investigators and study sponsors were implored to share research data to fulfill their moral obligation to maximize the chances that patients’ contributions translate into therapeutic advances. The urgent need to build collaborative networks dedicated to data-sharing principles was also underlined at a recent summit on dementia held by the Group of Eight industrialized countries. In fact, the failures encountered in targeting beta-amyloid for the

treatment of Alzheimer’s disease have led several companies to begin collaboratively developing innovative study designs requiring extensive data sharing. Unfortunately, the diverse ways in which data are collected and reported in clinical studies make it difficult or impossible to query across data sets, pool and share data, or integrate data for analyses of multiple trials to gain new scientific insights. Yet these problems can be resolved through the use of standard data formats, and the best outcomes would be achieved if data standards were adhered to from the start — within the electronic health record (EHR). In 1999, the Mars space orbiter exploded when incoming data were misinterpreted: they were assumed to be in SI units when they were actually in U.S. customary units. Without the relevant

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metadata, such confusion is inevitable. Units and other metadata are critical in medical research as well. Standard data and metadata formats are required for efficient aggregation of patient-level data, trustworthy statistical analyses, and accurately informed clinical decisions. When such standards are not implemented by all parties at the outset of research studies, precious information is lost or, at the very least, time-consuming manual mapping or computer programming is needed to render data comparable. Data related to cognitive defects in Alzheimer’s disease are a classic example. Although there is an Alzheimer’s Disease Assessment Scale for testing cognition, various study sponsors use this questionnaire in various ways, preventing accurate comparisons among studies or among patients 2163

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Fostering Responsible Data Sharing

Three Possible Scales for Scoring the Severity of an Adverse Event in Three Different Studies. Score

Study A

Study B

0

Absent



Missing or unknown

1

Mild

Mild

Very mild

2

Moderate

Moderate

Mild

3

Severe

Severe

Moderate

4

Very severe

Not assessed or missing

Serious

5

Not assessed



Very serious

6





Life-threatening

9

Unknown





unless data are later translated into a common format — a process that is time-consuming and requires post hoc medical interpretation. To harmonize and standardize data collected during clinical studies, researchers must agree not only on the vocabulary or terminology but also on the precise definition of each word. Such agreement requires active involvement and consensus building among multiple stakeholders. The Coalition Against Major Diseases, set up by the Critical Path Institute (C-Path), brought together biopharmaceutical companies, patient-advocacy groups, representatives of the Food and Drug Administration and the National Institutes of Health, and the Clinical Data Interchange Standards Consortium (CDISC) to utilize data generated by numerous company-sponsored clinical studies on Alzheimer’s disease (including trials with negative results). The development of a consensus-based standard, which has now been published (CDISC Alzheimer’s disease standard version 2.0), enabled a database to be built that includes information on thousands of patients with the same disease, providing 2164

Study C

sufficient data in a standard format to permit complex data modeling related to new therapeutic targets. The standard allows information on mild cognitive impairment, family history, and gene expression, as well as other key information such as demographic data, to be reported in an agreed-on format. C-Path now has consortia for Parkinson’s disease, multiple sclerosis, and other diseases, with CDISC standards published or emerging. In Europe, substantial resources are also being invested in the development of clinical data standards, primarily through the Innovative Medicines Initiative (IMI). The need to report key data on adverse events is another reason for using standard data formats. If three study sponsors collect data on the severity of an adverse event using different scoring systems (see table), it’s nearly impossible to compare the data in an accurate and trustworthy way. A computer program cannot replace the expert clinical opinion needed for interpreting such information; again, data should be collected in a standard format from the start. Several disparate adverse-event standards have now been harmonized in the collab-

orative Biomedical Research Integrated Domain Group (BRIDG) model. The biopharmaceutical industry is increasingly committed to global standardization as an essential objective of noncompetitive research efforts developed in partnership with public organizations, in Europe through the IMI and more broadly through the Coalition for Accelerating Standards and Therapies (CFAST). These joint efforts conducted with CDISC aim to accelerate the development of innovative therapies by reducing the time and resources required for study conduct and increasing the quality and facilitating integration of research data. For instance, the CDISC diabetes standard will include clear definitions of hypoglycemic events. Agreement on the use of terms such as “severe hypoglycemia,” “symptomatic hypoglycemia,” and “pseudohypoglycemia” at the start of studies will save months of costly back-end data mapping. Unambiguous definitions will represent an important achievement for the assessment of effectiveness and adverse events caused by new diabetes drugs. The CDISC standards from CFAST projects will be maintained by means of a dedicated tool, the Shared Health and Clinical Research Electronic Library. Now, as EHRs are increasingly being adopted, interoperability issues must be solved if we want to take advantage of modern health reporting systems and endto-end electronic data streams in order to promote responsible data sharing and a learning health system in which clinical care data can readily be used for research and research results can rapidly inform clinical decisions. Security, privacy, confidentiality,

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and informed-consent issues are being studied carefully by many parties, and solutions are in progress. However, global consensus on a minimum standard data set for EHRs, akin to the consensus-based minimum standard data set for global clinical research studies (Clinical Data Acquisition Standards Harmonization [CDASH]), could drive significant progress. A potential solution lies in a suite of CDISCinspired profiles from Integrating the Healthcare Enterprise that connect the Continuity of Care Document from EHRs with

Fostering Responsible Data Sharing

CDASH; since these standards lie at the intersection of health care and research, they could drive innovation by enabling EHRs to readily support standards-based research studies. The question is no longer whether but rather how clinical research data should be shared. EHR use is becoming routine, and responsible data sharing is being defined, with principles established and policies set globally. The precise format of the data to be shared cannot be an afterthought. In an era of increased transparency and inte-

grative analyses of data from multiple origins, data standards are essential to ensure accuracy, reproducibility, and scientific integrity. Their use will help in fostering innovation — and thereby in honoring the sacrifices of research participants everywhere. Disclosure forms provided by the authors are available with the full text of this article at NEJM.org. From the Clinical Data Interchange Standards Consortium, Austin, TX (R.K.); and the Innovative Medicines Initiative, Brussels (M.G.). DOI: 10.1056/NEJMp1401444 Copyright © 2014 Massachusetts Medical Society.

Mini-Sentinel and Regulatory Science — Big Data Rendered Fit and Functional Bruce M. Psaty, M.D., Ph.D., and Alasdair M. Breckenridge, M.D.

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n medicine, “big data” come in many forms. With the financial incentives provided by Medicare and Medicaid for the “meaningful use” of electronic health records (EHRs), the quantity of electronic medical data has expanded rapidly. Simultaneously, genomewide association studies funded by the National Heart, Lung, and Blood Institute have produced data sets with millions of genetic variants for each participant, encouraged the development of consortia with hundreds of thousands of study participants, and resulted in discoveries about the genetic origins of human health and disease. Despite the expanding numbers of people with dense electronic medical data, however, the research products from claims data and EHRs have been few and modest, in part because these medical big data are not of research quality but rather are the electronic side effects of the

functions of billing and clinical care. If, as Nate Silver suggests in The Signal and the Noise, “Most of [the increasing quantity of information] is just noise, and the noise is increasing faster than the signal,” the challenge for medical scientists is finding ways to create systems or study settings that can contribute meaningfully to the health of the public. One model is the Mini-Sentinel. A pilot project of the Sentinel Initiative of the Food and Drug Administration (FDA), the MiniSentinel has created a nationwide system that uses electronic data to evaluate the safety of marketed drugs, devices, and biologics. To be eligible to become a Mini-Sentinel data partner, health care systems were required to have complete longitudinal claims data, including computerized pharmacy data, for defined populations during known periods, as well as the ability to retrieve full-text medi-

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cal records. Unlike fee-for-service providers, these closed systems of care are unlikely to miss key exposures or events in longitudinal studies. Each Mini-Sentinel data partner develops and maintains its own data, formatted according to the common data model, and only a carefully selected fraction of all available data is extracted for use from claims and EHR databases. Harmonization of data elements has nonetheless been a formidable task: among the data partners, for instance, 68 different terms were used to describe the units for platelet counts. In the Mini-Sentinel, patient privacy is fully protected, protocols are posted for public comment, and investigators are free of conflicts of interest with regulated industries. Patient-reported outcomes, though not currently included, are likely to become available as EHRs expand. Extensive validation 2165

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