The State of Data Sharing at the U.S. Department of Health and Human Services

"The potential for machine learning, artificial intelligence, and other tools to address some of the most complex challenges, to promote discovery, and to augment the possibilities of applied human intelligence has become increasingly accepted.1 And yet, the difficulty of the journey of creating value from data is underestimated when the sole focus is on an ultimate output rather than the foundational elements necessary to embed analytics as a core function of organizational performance. As Fortune 500 companies are recognizing this imperative of analytics, their primary driver is revenue. Federal and state governments are on a similar transformational journey of using data to advance mission delivery. The move to make more data publicly available, under the banner of the open data movement, is seen as essential to making government itself more open. Public access to open data also enables data consumers including entrepreneurs, innovators, and researchers to use data to generate new products and services, build businesses, and create jobs. Indeed, the cultural shift of closed to open government data has resulted in more than two thousand datasets published to HealthData.gov for the public to discover and use.2 While the value proposition for open data has taken root in the marketplace, government agencies must likewise use its data as a strategic asset.

Across the twenty-nine distinct agencies of the United States Department of Health and Human Services (HHS), data essential to understanding the nation’s health are collected every day. 3,4 Whether surveillance, survey, or claims data, HHS expends an enormous amount of financial resources to report on the state of the health of the population it serves. These data, however, are largely kept in silos with a lack of organizational awareness of what data are collected across the Department and how to request access. Each agency operates within its own statutory authority and each dataset can be governed by a particular set of regulations. As such, each discrete analysis of the data often gets reviewed for legal purposes and leads to data sharing occurring largely on a project-by-project basis. The individuals involved negotiate the nature and extent of data sharing arrangements often based on past experience and personal relationships. The process can lack transparency, transferability, accountability, and consistency. ..."