harmonization of core concepts with other exchange models, development of implementation specifications, reference implementation, and incorporation into certification and testing initiatives. Dr. Fridsma emphasizes the need to leverage existing work, coordinate capacity, and integrate successful initiatives into the framework.
Rebecca Kush from the Clinical Data Interchange Standards Consortium shared the Institute of Electrical and Electronics Engineers’ definition of interoperability—“the ability of two or more systems or components to exchange information and to use the information that has been exchanged” (IEEE, 1990). Building on this, she suggests that one approach to defining interoperability within the digital infrastructure of the learning health system might be the exchange and aggregation of information upon which trustworthy healthcare decisions can be made. Dr. Kush cites existing enablers that will contribute to this goal, including the Coalition Against Major Diseases’s Alzheimer’s initiative to share and pool clinical trial data across pharmaceutical companies. Furthermore, she posits that a standardized core dataset of electronic health record information that could be repurposed for research, safety monitoring, quality reporting, and population health would help facilitate an interoperable digital health infrastructure. Dr. Kush shares several examples of existing standards initiatives that could be leveraged as a foundation for the learning health system, for example, increasing adverse drug event (ADE) reporting through the implementation of the ADE Spontaneous Triggered Events Recording trial.
Echoing the notion of health care as a complex adaptive system, Jonathan Silverstein, formerly of the University of Chicago (now at NorthShore University Health System), asserts that current technological failures of the healthcare system are a result of incompatibility between the technology employed and the nature of the system. He suggests that what is needed is secure data liquidity supported by a functional architecture that enables ever-expanding secure uses of health data. Dr. Silverstein proposes that this can be achieved by employing provable electronic policy enforcement in regard to access, provenance, and logging, as well through scalable data transport mechanisms and transformations that make data unambiguous and computable. He predicts that the increasing scale and complexity of medicine and biology will lead to more collaborative endeavors and sharing of resources—both data and technical. Consequently, approaches to sharing technical resources through federated hosted services such as grids and clouds—which provide scalable ways to leverage existing distributed data, transport standards, and individual expertise—promise to be a crucial part of the digital infrastructure.
Drawing on his experiences with the Indiana Network for Patient Care, Shaun Grannis of the Regenstrief Institute shares his thoughts on what will be needed to mitigate data heterogeneity in a learning health system.