# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### Contribution  
Our research work **contributes** in to the following areas:  
1. Individual level: provides clarity regarding privacy definition and its qualitative assessment measures. Policymakers need to standardize the Privacy by Design measures, and entrepreneurs have to apply standardized technical measures to safeguard privacy to avoid hefty compensations effectively. The e3-value models provide the basis to provide the standard metrics for key stakeholders to confirm the transfer of privacy as an objective value object within healthcare. We highlighted the Informed Consent and its loopholes in ascertaining patients' privacy, as in Figure 10;
2. Inter-/intra-organizational level: validated the patients' un-anonymized care metadata shared amongst Dutch caregivers using Process Mining (PM) discovery techniques on a real-world Sepsis dataset. From a patient's perspective, we provide the REA ontology based on the content analysis concerning Dutch care metadata share landscape. IM of REA ontology highlights: the key economic agents, their prime interactions, and collective economic value gain or loss. We explicated privacy as a Materialized Privacy Claim (MPC) using REA;
3. Third-party metadata processing level: validated the usefulness of the plugins as privacy-preserving noise adding plugins for the tool's end-users from both the domains (i.e., the PM and healthcare). The contribution of this research work is to suggest and verify a framework for the evaluation of the PUT of ProM's (noise adding) plugins and their respective noise added Petri Nets. This was also conducted in the context of suggesting similar noise-adding plugins for future ProM versions;
4. Normative and empirical evaluation: provides a normative and empirical evaluation of multilevel privacy-preserving measures and highlights the implication of PUT on the former. Based on findings, the research work provides a partially hypothetical multilevel privacy assurance graph that exhibits the privacy pathway that starts as the highest from an individual's perspective and keeps dropping in favor of inter-/intra-organizational control over PII. The privacy reaches its lowest at third-party data processing level, as shown in Figure 10. The bold font draws attention toward the contribution of this research work and four main branches of multilevel privacy assurance evaluation with recommendations for healthcare providers and respective stakeholders. Conceptualization, S.A.S., F.A.B., M.v.K. ; methodology, S.A.S., F.A.B., M.v.K. ; software, S.A.S. ; validation, S.A.S. ; formal analysis, S.A.S. ; investigation, S.A.S. ; resources, S.A.S., F.A.B. (arranging so that S.A.S. can do the research work); data curation, S.A.S. ; writing: S.A.S., F.A.B., M.v.K. ; writing--review and editing, F.A.B., M.v.K. ; visualization, S.A.S. ; supervision, F.A.B., M.v.K. ; project administration, M.v.K. ; funding acquisition, M.v.K. All authors have read and agreed to the published version of the manuscript. This research was funded by DMB department of EEMCS faculty of University of Twente. Not applicable. All used event logs (datasets) are publicly available. See Sepsis event log [87], Hospital billing event log [88], and Urine infection event log [89]. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: