From Chaos to Control: Adapting Frameworks for Data Governance in Healthcare Crisis Response

Healthcare organisations face challenges managing data in crisis response, yet the current literature does not explain how data governance (DG) frameworks may adapt or evolve in such an environment. Over the years, organisations, including healthcare, have been developing DG frameworks to create an environment conducive to sound data management. These frameworks identify and establish rules, policies, processes, and decision-making authority, determining who manages an organisation's data and how they carry out their duties. The frameworks address specific decision domains such as privacy, data quality, master data management, and metadata management. To successfully meet the objectives, these frameworks must align with the organisation's environment. However, the extant DG literature assumes organisations operate in a stable, evolutionary environment, with only gradual, incremental changes. Yet, evidence shows that challenges such as balancing protecting individuals’ privacy and sharing data emerge when managing data in crisis response.

Therefore, using the punctuated equilibrium (PE) theory as the initial theoretical lens and a case study research design, this study sought to understand how healthcare organisations adapt or evolve their DG frameworks in crisis response. Based on COVID-19 experience, data were collected through semi-structured interviews from six healthcare sites, including the Ministry of Health and five DHBs in New Zealand. An extensive document analysis was also conducted to corroborate data collected from interviews.

By employing thematic analysis, this study identified three themes. These themes revolve around data sharing and protecting individuals’ privacy. The first theme indicates that adapting DG frameworks in crisis response takes a collaborative approach where multiple and diverse stakeholders are involved in decision-making. In this complex environment, healthcare organisations may adapt their structures to address the increased focus on privacy. Again, data-sharing policies and procedures may be adapted to facilitate seamless data sharing among organisations. The second theme reveals that DG frameworks in crisis response are constantly changing. Since the crisis environment constantly changes, so do the structures, procedures, rules, and processes. Effective continuous adaptation relies on establishing feedback mechanisms that are useful in providing real-time information on appropriate adaptation strategies based on the prevailing circumstances of the crisis. The third theme underscores the resilience of the frameworks. Adaptation strategies employed by the Ministry revealed that compliance and privacy risk management remain the primary focus of DG frameworks in healthcare. This study uses the Complex Adaptive Systems (CAS) theory lens to interpret the research findings. Using the theory’s perspective, DG in crisis response exhibits characteristics akin to CAS and is not static but self-organises and adapts to emerging circumstances influenced by COVID-19.

This study significantly contributes to DG practice and literature by explaining how healthcare organisations may adapt their frameworks in complex, dynamic and uncertain environments presented by the crisis. The study provides practical insights on privacy compliance and risk management in crisis response, which is essential for policymakers and regulatory bodies. One of the primary contributions is developing an adaptive data governance framework to guide organisations in crisis response. The framework consists of four essential stages, each with important considerations, emphasising the importance of identifying stakeholders, seeking feedback, and continuously adapting the approaches to address the rapidly changing environment. Additionally, the study makes a significant methodological contribution. By incorporating vignettes, the study demonstrates innovative data presentation methods in qualitative research, offering valuable insights for future studies. Traditionally, vignettes have been used as a tool developed before data collection. However, this study has shown that vignettes can be a powerful post hoc sensemaking tool.