Multilevel Privacy Assurance Evaluation of Healthcare Metadata

Abstract

Healthcare providers are legally bound to ensure the privacy preservation of healthcare metadata. Usually, privacy concerning research focuses on providing technical and inter-/intra-organizational solutions in a fragmented manner. In this wake, an overarching evaluation of the fundamental (technical, organizational, and third-party) privacy-preserving measures in healthcare metadata handling is missing. Thus, this research work provides a multilevel privacy assurance evaluation of privacy-preserving measures of the Dutch healthcare metadata landscape. The normative and empirical evaluation comprises the content analysis and process mining discovery and conformance checking techniques using real-world healthcare datasets. For clarity, we illustrate our evaluation findings using conceptual modeling frameworks, namely e3-value modeling and REA ontology. The conceptual modeling frameworks highlight the financial aspect of metadata share with a clear description of vital stakeholders, their mutual interactions, and respective exchange of information resources. The frameworks are further verified using experts’ opinions. Based on our empirical and normative evaluations, we provide the multilevel privacy assurance evaluation with a level of privacy increase and decrease. Furthermore, we verify that the privacy utility trade-off is crucial in shaping privacy increase/decrease because data utility in healthcare is vital for efficient, effective healthcare services and the financial facilitation of healthcare enterprises.

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

“…These studies also highlight the three major categories of data sources: structured, semi-structured, and unstructured, within the taxonomy. Moreover, previous research consistently underscores the significant role played by factors like uncertainty [31,33,37], imprecision [34,52,58,59], vagueness [58,60], incompleteness [61,63,65], and complexity [36,53,72] in shaping decision-making within HOs. The aim of considering these factors and data sources is to improve sustainable decision-making processes in the healthcare sector by addressing the challenges arising from incomplete information and leveraging the immense potential of big data.…”

“…UK Singh [51] The impact of imperfect information on the health insurance choice, health outcomes, and medical expenditures of the elderly. USA Sohail [52] Multilevel privacy assurance evaluation of healthcare metadata. The Netherlands Yang [53] Incomplete information management using an improved belief entropy in Dempster-Shafer evidence theory.…”