## 3 Methodology
#### Classification of Papers by Methodology  
We deepened the works classification by specifying their methodological approach: theoretical methods and experimental methods (Figure 3). Furthermore, for each of these sub-classfication, we provided details in form of labels (Table 4). * _Theoretical methods_
* Conceptual model: Papers in this category focus on developing abstractions that help understand key concepts and their relationships within the system. These studies do not provide detailed structural or technical descriptions but emphasize high-level understanding. * Architectural design: Papers under this classification present designs that provide detailed structural and technical information, expanding upon conceptual models to create a comprehensive system blueprint. * _Experimental methods_
* Proof of concept: Papers classified as proof of concept present preliminary experimental work aimed at validating a concept or evaluating its feasibility. These studies are less detailed than prototype implementations but serve as an important step toward establishing feasibility. * Simulation: Papers in this category use computer-based simulations involving mathematical models to evaluate or predict system behavior and characteristics. These studies provide insights into how the system might perform under different conditions. * Prototype: Papers describing prototypes involve working implementations of concepts, complete with technical details of the frameworks and tools used. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Biomedical Data** & **Application Area** &
\begin{tabular}{l} **Reason for Using** \\ **Blockchain** \\ \end{tabular} \\ \hline \(\bullet\) clinical trial records & \(\bullet\) clinical trials & \(\bullet\) access control \\ \(\bullet\) personal records & \(\bullet\) biomedical databases & \(\bullet\) non-repudiation \\ \(\bullet\) medical records & \(\bullet\) health records & \(\bullet\) data versioning \\ \(\bullet\) sensor data & \(\bullet\) medicines supply & \(\bullet\) data integrity \\ \(\bullet\) consent forms & \(\bullet\) medical insurance & \(\bullet\) data auditing \\ \(\bullet\) ambient temperature & \(\bullet\) wearables and embedded & \(\bullet\) data provenance \\ \(\bullet\) transaction records & \(\bullet\) health & \\ \(\bullet\) database queries & & \\ \(\bullet\) financial data & & \\ \(\bullet\) location & & \\ \hline
**Blockchain Technology** & **Maturity of Approach** & \\ \hline \(\bullet\) Bitcoin & \(\bullet\) proposal & \\ \(\bullet\) Ethereum & \(\bullet\) architecture & \\ \(\bullet\) Hyperledger Fabric & \(\bullet\) implementation & \\ \(\bullet\) MultiChain & \(\bullet\) evaluation & \\ \(\bullet\) TenderMint & & \\ \(\bullet\) JUICE & & \\ \(\bullet\) Gcoin & & \\ \(\bullet\) NEM & & \\ \(\bullet\) IOTA & & \\ \(\bullet\) Custom & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of blockchain applications in the biomedical domain [2]. These studies offer practical validation and insights into the feasibility of the proposed approaches. Finally, the type of contribution of each study is identified as the last stage of classification, with the following explanation of the labels. * Algorithm: A custom algorithm tailored to application needs. * Workflow and processes: Details of system workflow and processes. * Simulation analysis: Mathematical analysis of simulated model results. * Experimental model: Experimental setup description, including tools and frameworks, without results. * Prototype case: Case study of an application of a prototype without experimental results. * Performance evaluation: Case study including experimental assessment. * Experimental evaluation: Experimental results were used to assess the performance of algorithms or architectures within a defined framework. * User evaluation and feedback: Feedback from real-life participants based on a case. Figure 3 provides an illustration of the entire 3-stage classification criteria that was utilised.