Complexity science provides a lens to explore the complex system of public policy related to data governance. Public policy generally demonstrates the common sense and common conscience of the citizens and is applied to matters of public health, safety, and welfare (West’s Encyclopedia of American Law, 2008). Subsequently, public policy varies with changing social duties, economic needs, public opinion, and moral objectives of the people. Public policy influences the implementation, execution, and interpretation of legislation (West's Encyclopedia of American Law, 2008).
Mitchell (2009) defined a complex system as a “system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing and adaptation via learning or evolution” (p. 200). Public policy could be considered a complex system because of its co-evolutionary nature and self-organizing behavior in the presence of strong government actors (Morçöl, 2010). Most public policy systems do not occur in isolation; instead, they co-exist with other systems, such as legal, social, economic systems, and are influenced by external and internal factors, including health data sharing preferences of consumers. Morçöl (2005, 2010) described the use of complexity theory in public policy as the conceptualization of relationships between micro processes (e.g., individual choices and behaviors) and macro policy processes (e.g., actions taken by the government).
Caldwell and Mays (2012) described this policy framework as taking into consideration the pace, direction and impact of organizational innovation and change by studying the interconnections between meanings across different organizational levels. Using complexity theory to analyze both understanding and action at multiple levels influences public policy development and allows for clear interpretation of public policy (Caldwell & Mays, 2012). Information privacy, data security, and consumer consent management processes originate in public policy. Subsequently, legal regulation flows from public policy, forming the framework for data governance rules (Code of Fair Information Practices, 1973; U.S. Department of Health & Human Services, 2013; HITECH, 2014).
Data governance is the core function of a data management framework (Mosley & Mosley, 2008). Data governance is a relatively new term in health care. Data governance is defined as the exercise of authority, control, and shared decision-making (planning, monitoring, and enforcement) of the management of data assets (Mosley & Mosley, 2008). Data governance in health care includes multiple complex systems: public policy, legal regulation (state and federal), technology infrastructure, health care delivery, and consumer behavior, beliefs, or perceptions (Moon, 2017). However, ensuring the consumer voice is present in public policy related to health data governance and the management of health data sharing preferences remains problematic.
Existing public policy and legal regulation recognizes the individual as the primary decision-maker in the release of information transaction (HITECH, 2014; U.S. Department of Health & Human Services, 2013). Federal public policy has more recently involved consumer engagement campaigns to organize consumer feedback pertaining to topics on consumer data management, such as how to view, download, and transmit that information (ONC, 2015).
Research findings support consumer desire for granular control of their health care data (Caine et al., 2015; Dhopeshwarkar et al., 2012; Park et al., 2013; Patel et al., 2011; Schwartz et al., 2015). However, efforts have been hindered by outdated public policy, variation in interpretation of legal standards, and legal regulation that is difficult to operationalize in an electronic information management environment (Daniel et al., 2014). Consequently, public policy implementation in health care continues to be an issue, despite heavy incentives to digitize health data and create a learning health ecosystem.
Lisa Moon. PhD Dissertation. University of MN. Consumer Data Sharing Preferences. 2017.
@phdmoon