• Modern health care challenges, such as chronic disease, require comprehensive, longitudinal information to support team care.
• Blindfolded record linkage, such as using hashes, offer many advantages to better link data between sources while maintaining privacy.
In order to make optimal use of the digital health data utility, novel and innovative approaches will have to be developed. These innovations include learning from large sets of data while dealing with the risk associated with physical aggregation, coping with incomplete standardization of data, and linking data from diverse sources without the use of universal identifiers. Richard Elmore, Coordinator of Query Health at the Office of the National Coordinator for Health Information Technology, and Richard Platt, Chair of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute, discussed the specific case of distributed data queries. Christopher Chute, Professor of Medical Informatics at the Mayo Clinic, elaborated on challenges and opportunities associated with data harmonization and normalization. Vik Kheterpal, Principal at CareEvolution, focused on data linkage between sources.
In their discussion of distributed queries, Richard Elmore and Richard Platt covered the broad definition and qualities of such queries, and provided specific examples of these queries in action. Distributed queries allow querying of data from multiple partners without having to physically aggregate data in one central repository; a query is sent to all partners, and each participant runs this query internally and returns summary results individually. Some example use cases for distributed population queries include population measures related to disease outbreaks, postmarket surveillance, prevention, quality, and performance. The advantages of this model, Elmore emphasized, are myriad. A distributed query approach allows data partners to maintain HIPAA-mandated, contractual control of their protected health information (PHI), and it facilitates data validity by ensuring that results are returned by local content experts, those most familiar with and understanding of the data and their interpretation. The