to leverage this information for improvement, such as the ability to view laboratory results, maintain problem lists, or manage prescription ordering, (Decker et al., 2012).

In addition to increased data collection, more organizations are sharing digital health data. Data collected to meet federal reporting requirements or for administrative purposes are becoming more accessible. Efforts such as Health.Data.gov provide access to government datasets for the development of insights and software applications with the goal of improving health. Within the private sector, at least one pharmaceutical company is actively exploring release of some of its clinical trial data for research by others.2 Data sharing partnerships are also opening up across organizations. The Care Connectivity Consortium, a group of five health systems at the leading edge of using EHRs (Kaiser Permanente, Geisinger Health System, Mayo Clinic, Intermoutain Healthcare, and Group Health Cooperative), have agreed to securely exchange clinical data for care coordination. Sharing is also happening across industries. In the case of AstraZeneca and WellPoint, a payer and a product manufacturer have initiated a study on the clinical and cost effectiveness of treatments for some chronic and common diseases. Finally, efforts to increase patient access to their own data, such as the Blue Button initiative which allows patients to download their health information with the click of a button, have been adopted by organizations such as the Veterans Health Administration and UnitedHealthcare, and included in the criteria for Meaningful Use.

The increased collection and sharing of health data is quickly moving health care into the era of “big data.” This term refers to the huge volume and diversity of data collected in increasingly connected digital technologies. The scale of “big data” has implications for analysis and learning in a way that has been leveraged by other industries, such as intelligence, but is only beginning in health care.

Increasing collection, sharing, and aggregation of data are being matched by advances in methods for learning from these data. Clinical and administrative data can be used for studies to assess the effectiveness of health care interventions; identify product safety issues; detect emerging epidemics; and measure health care utilization and value. Observational methods that use data collected in the course of providing patient care are increasingly appreciated as valuable contributors to generating and testing hypotheses. The rapidly rising costs and extended duration of traditional randomized control trials (RCTs) have contributed to the interests of investigators and funders, among whom there is a growing appreciation of the need to harness big data for innovative streamlined approaches to testing new interventions.

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2 Personal communication, Joel Beetsch, Sanof.



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