Health centers and their networks are experiencing a growing need to make a myriad of decisions around managing and obtaining value from data, such as minimizing cost and complexity, managing risk, and ensuring adherence to regulations and legal requirements. As organizations mature in their data science teams and projects, formal mechanisms are needed to oversee these analytic processes. These mechanisms are referred to as data governance, which is the decision making authority over data-related matters. Data governance becomes more complex as more organizations participate, especially among diverse stakeholders. A data governance framework for example may need to account for the needs of health plans, which are different than a health care provider.
New use cases, including public health, population health, and research are also built into and expanded within a governance framework. While data governance exists in single health care organizations and integrated delivery networks, data governance requirements become more complex as information exchange grows beyond state lines and incorporates new types of health care organizations.
As health information sharing becomes more common due to federal legislation and health care reform, data governance models must be adopted and should be flexible to adapt to these changes. Although health centers and networks belonging to the same enterprise, or are closely affiliated, generally agree on data governance, the primary challenge in their governance framework is how to place the needs of the data/information exchange and sharing above tangent or unrelated interests of each externally participating organization. In this session, attendees will be able to explain the concept of data governance and identify nuances such as organizational structures for managing data, rules and policies, data decision rights, methods for accountability, and methods enforcement for data-related processes.