An operating model design of medical agile noninvasive data governance

Abstract

Data governance is defined as general data management practices and redundant data structures; it sets direction and controls to ensure compliance with rules, policies, and regulations. However, large organizations with large amounts of data and complex structures require more focus on people and interactions, based on the agile concept. It entrusts a co-design and decision-making team with no invasion of the original role to support the organization’s business in operations. The data should be embedded in the analysis group to sprint the data analysis cycles. This research focused on an operating model, the medical agile noninvasive data governance, which aimed to formally assign roles and responsibilities to groups with specific expertise. The resulting streamlined workflow benefited from various planning strategies for further healthcare services; thus, the healthcare organization in Thailand chose this as a case study. This research emphasized the roles and responsibilities throughout the organization to show a more accurate imple-mentation process of the prototype. Implementation and evaluation were categorized into two levels: organizational and operational. An in-depth organizational-level interview evaluated the resulting the responsible-accountable-supportive-consulted-informed matrix for the policy establishment process. The operational-level assessment made the function of the concept visible through a role-playing representation of data operations. A questionnaire of role-playing roles was used for assessment in terms of agility, connectivity, redundancy, reducing roles, and process responsibilities. The highest level of overall satisfaction was 4.65 on a 5-point rating scale. After comparing the results of existing frameworks or studies, the researchers found that this prototype design was complete, with coverage of both roles and responsibilities of each level according to the organizational structure. It could streamline work processes and lead to analytics to connect with valuable, accurate, and transparent targeted outcomes for organizations.