# The Need for Machine-Processable Agreements in Health Data Management +
## 1 Introduction
myKinMatters, shown in Figure 1 offers patients a secure way of sharing their genetic test results electronically with their relatives and has a variety of functions i.e., supports the patient with creating a family tree; allows the patient to take their family tree to their clinician who can mark on the app which relatives could benefit from being given the information; allows the patient to upload their genetic test result document along with any other documents they wish to share; send documents electronically to relative's email or mobile phone; and track which relatives have opened the documents. We co-designed myKinMatters with clinicians and health professionals at a University Hospital Trust, and have deployed the application with test users in simulated scenarios as part of intervention trials. Through our analysis we have identified five major requirements that are particular to health data sharing: (i) the need to define complex, previously undefined, classes of individuals to share data with, e.g., \"share data with all females on the patient's mother's side\", (ii) data sharing dependencies, for example, a patient wants to share with their siblings only if their mother learns first, or in contrast, only if their siblings are not going to share it further, (iii) data sharing instigation, e.g., the clinician might suggest or advise that the patient should share the data with someone in their family and we would like to record this action, as well as whether the patient actually shares the data or not, (iv) multi-party privileges on data, e.g., control of genetic data might not belong to one individual in particular but to an entire family, or a clinician might have an obligation to override a patient's preference, and (v) areas where permission to share may be different where the data is anonymized. Our first requirement points us to use an ontology-based approach, ontologies being shared vocabularies, machine-processable conceptualisations of a domain of interest ([[https://www.w3.org/standards/semanticweb/ontology](https://www.w3.org/standards/semanticweb/ontology)]([https://www.w3.org/standards/semanticweb/ontology](https://www.w3.org/standards/semanticweb/ontology))). For example, our approach allows us to employ a pedigree genealogical ontology such as [9], which in combination with familial relationship inference rules, e.g., \"all female siblings of a mother are maternal aunts\", enables us to offer complex sharing customisation options to patients and clinicians. We perform a comparative analysis that shows that our identified requirements are not captured by existing approaches or languages, and so in this paper we investigate the use of an ontology-based data sharing framework, designed in modular way so as to support our identified challenges as well as future unforeseen requirements. Some aspects of medical data sharing prove deeper and more challenging than others, such as the support of multi-party privileges on data which can lead to conflicting statements, for which we also present a conflict detection algorithm and present a conflict resolution intervention that will work with humans (clinicians and patients) to reach a solution. Our contributions are the following:  
1. We discuss the case of health data ownership and point out the intricacies and complexities of this matter that are particular to health data. 2. We construct and deploy an application for sharing familial data to study user and clinician policies, preferences and consent. Using this system, and literature on patient-family data sharing, we gather a set of real health-based use cases that reveal challenges and requirements of data sharing agreements (Section 2). This informs the design of the building blocks of our framework. 3.