# A Hyperledger Fabric-Based System Framework for Healthcare Data Management
## 3 Existing Frameworks
MedChain combined blockchain, digest chain, and P2P network to overcome efficiency issues in sharing various types of healthcare data which addressed privacy issues through data generation: disassociate the actual data with the data owner [34]. Healthchain suggested that doctors should also have the right to control their diagnostic data [35]. These studies show that in the current health IT systems, data security and privacy are mainly achieved through multiple encryption technologies. Blockchain technology should not be regarded as \"the master key\" to change all healthcare data management methods and solve patient privacy concerns. In 2020, a series of studies considered adopting privacy blockchain, including the Hyperledger Fabric framework, to implement scalable healthcare data management applications. PREHEALTH, a privacy-preserving EHR management solution uses Hyperledger Fabric and Identity Mixer to ensure acceptable scalability and data privacy [36]. [PERSON], [PERSON], and [PERSON] proposed a Hyperledger Fabric-based EHR sharing system and its related test environment that was based on Hyperledger composer [37]. The Hyperledger blockchain was suggested as an approach for Intensive Medicine, especially ICU data management, to avoid equipment failure [38]. Another approach to enhance security is the so-called blockchain tree that connects three blockchains together to store different types of data separately [39]. The above studies address privacy issues in healthcare data mainly through using the blockchain to give patients access control and monitor data usage. These models take advantage of the openness, traceability, and tamper proofing of the data on the blockchain, and overcome the shortcomings of traditional doctor-driven data storage and sharing. On the premise of solving the security of data storage and transmission, if the healthcare data access permission can be completely controlled by the patient, it could effectively protect privacy by preventing violated access. However, there are several shortcomings in the above research:  
* Healthcare data is a valuable asset, but its ownership is complex. The law does not specify data ownership and its economic value. For example, a doctors' diagnosis and treatment plan, the flow of people in clinics, the economic profits of private hospitals, etc., can all be reflected in the healthcare data and the value that is generated by these data should not belong to patients only. In other words, the \"intelligence of property\" problems of healthcare information hinders the patient-driven models. * The patient-driven data management model does not fit the current situation. The limitation is that most patients do not have the expertise to control what kind of information is shared with whom and when, which could result in reduced efficiency of healthcare services provision. The proposed solutions have not benefited the improvement of healthcare services. In traditional face-face treatments, doctors communicate with patients or refer to medical examinations to understand medical history and condition. Letting the doctor browse a patients' healthcare history data is not necessarily more efficient. On the other hand, for healthcare research institutions, requiring access permissions for each type of data from each patient increases the difficulty of obtaining data. It is also costly for patients to decide on each data access request. * The current frameworks have not reflected the legal and industry regulations on data retention. In Australia, the minimum timeframe for keeping medical records is seven years for an adult and until 25 for anyone under 18 [10].