# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 1 Introduction
### Research Aim, Purpose, and Main Conclusions  
The aim of this research work is to provide a multilevel privacy assurance evaluation of fundamental privacy-preserving measures for Dutch healthcare metadata and finding privacy increase/decrease at each level. The multilevel privacy assurance evaluation comprises normative and empirical evaluation. The normative evaluation is performed using content analysis. The empirical evaluation is conducted using Process Mining on real-world healthcare event logs. The evaluations' findings are illustrated using, 'Padlock Chain Model' of e3-value modeling and the 'Insurance Model' of Resource Event Agent (REA) ontology. The resulting conceptual modeling frameworks highlight the financial aspect with vital stakeholders, their mutual value transactions of data-oriented resources in a confined time. The purpose of this research work is three-fold: firstly, the exploratory and data-analytical research work of multilevel privacy assurance; secondly, the evaluation findings' illustration using e3-value models and REA ontology conceptual modeling frameworks and their verification using expert opinion; and thirdly, to find multilevel increase/decrease in privacy for Dutch healthcare metadata and its direct dependency upon Privacy UtilityTrade-off (PUT). The research work is helpful for (academia and industry alike) to collectively rectify the verified privacy gaps that deserve attention and immediate fixation before reaching out for extensive technical and organizational privacy-specific intricacies.  
The main conclusions of this research work provide the multilevel privacy assurance evaluation of fundamental privacy-preserving measures of healthcare metadata in the Dutch healthcare landscape. We performed the empirical and normative evaluation and illustrated the findings using e\({}^{3}\)-value modeling and REA ontology. We explored the increase/decrease in privacy at each privacy assurance level. We verify that Privacy Utility Tradeoff plays a vital role in shaping the increase/decrease in privacy for the sake of better data utility. This utility-prone metadata handling primarily ensures uninterrupted healthcare metadata share for patients' efficient/effective healthcare, which ascertains the service-dominant logic of patients' value care. Moreover, this uninterrupted healthcare metadata share predominantly financially facilitates the healthcare enterprises (a fundamental objective of GDPR, 2018). The latter is an essential contribution to this research work.  
In this paper, Section 2, namely Materials and Methods, provides an overarching methodology with a data analytical approach. It also incorporates a separate description of the followed methodology for each level of privacy assurance evaluation. Section 3 constitutes the results and findings based on normative and empirical evaluations. Section 4 discusses and concludes the multilevel privacy assurance evaluation. Section 4 also proposes a multilevel privacy assurance curve with direct implications from the PUT in handling sensitive healthcare metadata in Dutch healthcare. The contribution of the research work comes at the end of Section 4.