# The Need for Machine-Processable Agreements in Health Data Management +
## 5 A Policy Language for Multi-Privilege Consent
### Conflict Detection
## 7 Discussion, Conclusions and Future work

Data sharing in a clinical setting can be complex, involve multiple stakeholders, and require the need to assimilate and reconcile a range of desires and intentions alongside regulatory and disciplinary boundaries. The work presented here has explored these issues through a worked through case study of the expression and sharing of genetic test information. The requirements are derived from a co-design exercise that has sought to model the processes and data that exist where patients have genetic tests carried out and are then subsequently advised by clinicians that they should share this data with family members. We advocated the need for new languages that offer machine-processable data sharing and privacy agreements. We constructed and deployed a prototype application that has allowed us to examine the processes of sharing familial data and to study user and clinician policies, preferences

Figure 4: Conflicts can arise when a test result that is applicable to other family members occurs, and the patient is unwilling to discuss those results with the related members. Consultants can identify when familial-ownership of the data, because of disease inheritance patterns exist. Figure 5: If the consultant and patient cannot resolve conflicts in access, an anonymized process that sends a letter to affected parties family doctor requesting additional testing can be generated and sent. and consent. Using this system, and literature on patient-family data sharing, we have gathered a set of real health-based use cases. In Section 2, we presented seven illustrative use cases that highlight the challenges and complexities that can exist in the sharing of genetic test results. From these use cases we were able to distill requirements that underpin our ontological modelling of the system, our construction of the policy language, and our implementation of the conflict resolution system. An underpinning requirement is for the policy system to understand the language in which clinicians describe their sharing desires and policies. In Section 4 we presented a conceptual ontology to describe the actors, data types and processes that require capturing as part of a permissions-based genetic data sharing system. Patients will wish to express their family tree information using more traditional genealogical family tree structures however clinicians will typically be working with pedigree family trees, that are more applicable to understanding the transference of genetic mutations through lineage. As such, our conceptual ontology and associated rules need to provide mappings between both these forms of family tree constructs, but also understand the particular language of discourse used in discussing sharing desires, in terms such as first degree relatives or cousins. The information being shared is modelled within our ontology and our policy language caters for different versions of test results as well as notions of anonymity. As our use cases point to the need to share results anonymously, this places requirements on any eventual system to both have sharing mechanisms that are anonymous, but also for the system to understand where the contents of test results might be appropriately anonymized or not. With the underlying data structures expressed, we then presented our ontology-based policy language in Section 5. This language allows for multiple owners, fine-grained identification of resources and sharing requirements, as well as a range of complex classes of individuals and data sharing policies derived from our use cases and co-design process. The goal here is not to prohibit the expression of policies that conflict, but rather to capture when such conflicts occur. Having articulated the challenges and requirements of data sharing agreements this then informs the design of our framework and building blocks. The analysis of this policy language against other languages with respect to meeting use case needs, detecting conflicts, dealing with conflicts in a real world setting, and instigation.