# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 2 Materials and Methods  
The'refined Process Mining (PM) framework' [42] laid out the Process Mining spectrum and inspired us to follow similar steps in executing this research work. The framework inspired our research approach because the refined PM framework incorporates the controlflow aspect of \"How?\", the organizational aspect of \"Who?\", and the metadata aspect of \"What?\" of the business processes. The framework also highlights the usages and contributions for analyzing the premortem (currently opened cases) and post mortem (already completed historic cases) in event logs. Event logs are the datasets that are extracted directly from an organization's IS for data-driven evaluations. Furthermore, the idea of normative (i.e., de jure) and empirical (i.e., de facto) business models is also described in this framework, where goals are cartography, auditing, and navigation. Cartography is the extraction and improvement of de facto empirical process models deduced from event logs. Auditing considers the de jure (should-be state that is assumed to be correct) business model and compares it against the de facto business process model for improvements' sake. This sometimes improves the de jure model if the empirical model is performing better than expectations. Navigation improves the future based on normative and de facto evaluations with future recommendations.  
Process Mining spectrum covers the who, how, and why of a case study (in this case, the healthcare metadata share in the dutch healthcare landscape). However, the normative evaluation of the documents requires text mining of official documents, which are usually not publicly available. For this reason, the normative evaluation in this research work is based on content analysis using study of the literature, official websites, and publicly available official documents. The used methodology with data analytical approach for this research work is shown in Figure 2. For PM-oriented de jure analysis, the PM-based text mining of one of the Dutch diagnostic labs is underway for future navigation of a local diagnostic lab in the Netherlands.  
For empirical evaluation, Process Mining (PM) discovery and conformance checking techniques are used. PM is performed using publicly available, i.e., ProM Lite 1.2, ProM 6.9-6.10 [63] tools, and commercially available Disco [64] tool. PM is performed on publicly available, real-world healthcare event logs [65]. Based on the evaluations, the conceptual modeling frameworks are drawn using Resource Event Agent (REA) ontology [66] and e3-value modeling [67]. Furthermore, the expert's opinion further verified the evaluations and conceptual modeling frameworks. The underlying reasons behind choosing the aforementioned conceptual modeling frameworks were:  
* To highlight the significant stakeholders, their vital interactions with their mutual exchange of information resources while handling healthcare metadata;
* Moreover, the ontologies and the privacy-aware mutual interactions also accentuate the monetary aspect of the transactions. The latter is an essential contribution to this research work.  
The conceptual modeling frameworks are verified with the expert(s) opinion. Finally, an increase/decrease in privacy is demonstrated at each level primarily influenced by the PUT of Dutch healthcare metadata.  
As mentioned earlier, multilevel privacy assurance evaluation is divided into three levels. For each level, the overarching methodology is shown in Figure 2. Moreover, the elaborated methodology for each subsequent level is mentioned under each level, namely individual level, inter- and intra-organizational level, and third party data processing level.