Edinburgh Napier University

  This project extends the e-Health Cloud-based Platform, and integrates with assisted living. The project integrates Edinburgh Napier University, Microsoft and HoIP, and has created a novel governance policy infrastructure using a circle-of-trust relationship for assisted living, which then links to formal, role-based security for primary and secondary health care. In this way a completely secure and robust data infrastructure has been created which allows data to be translated between the main pathways that patients take through a health care infrastructure. This system thus aims to overcome the non-integrated approach to patient care, where data is not used effectively between GPs, the hospitals, and within assisted living. The work is funding by the TSB and EPSRC, and aims to increasing human and digital trust for Internet-based systems.


As with any safety critical industry, there is a strong requirement for trust in the capture, storage and consumption of health care data. Errors in any part of this process can reduce human trust in the infrastructure. Unfortunately many systems do not radiate the rights of access to data throughout the complete infrastructure, and thus there can often be weaknesses in the transfer of rights to access in transferring between systems. Another issue in health care relates to the integration of the access rights between differing domains, such as for the formal health care infrastructure, and the informal carer infrastructure. While there can often be well-defined roles for access to data within a formal infrastructure, very little exists for informal care. The key aim of this project is to create a completely integrated infrastructure, where identity and role is used to define the rights to data capture and store, and onto the consumption of services are exposed to differing domains, which are strictly consumed using an integrated security policy. Figure 1 outlines the infrastructure (see attachments), where data is captured from the patient environment, and marked up with the required context (such as the patient ID, capturer ID, location, device type, captured units, and so on). This context information allows the data to be used in many different ways, such as tracking a certain device around the health care environment, or to determine the blood pressure for a range of patients. This data is then stored in its original captured form within patient data buckets, using the encryption keys of the capture service and the patient. Access to the buckets is then carefully controlled by a security policy, and are exposed through carefully managed services, which require an identity ticket verifying the role and identity of user consuming the service. Figure 1 shows an example of an EWS (Early Warning Score) which aggregates a number of clinical assessments such as blood pressure and heart rate. The service then, if the user has the correct rights to access the service for the patient, delivers an abstraction of the interface, thus supporting a wide range of devices, and customising the user interface based on the rights of the user.


The core infrastructure has high levels of security and trust, where a security policy controls every action, and there will be three well defined, and open, interfaces to allow existing health care infrastructures to integrate with the e-Health Cloud. Once identity has been verified, using a federated trust infrastructure, a ticket is issued which verifies the identity, and is then used to access a service, based on their rights. The data is then carefully managed within a domain and no direct access can be made to it, apart from through carefully managed services. A SPoC (Single Point of Contact), as illustrated in Figure 2 is then used to control the flow of information between domains, using well defined policies, and rights are based on role and identity. A key challenge will thus be in integrated existing infrastructures, such as HealthVault with a new e-Health Cloud, while still integrating security. This will be achieved through a policy translation engine, which converts the enhanced policy definition into HealthVault.


A key element of the system is the integration of patient simulation agents, that will mimic real-life clinical data, such as for heart rate and blood pressure, and which have defined patient profiles to provide likely changes in measured parameters. For example this would simulate an increase in blood pressure at give times for a patient who has been modelled at being a risk of a cardiac arrest. This simulator will provide the data to test a large scale infrastructure, with millions of patients providing data, and also will allow health care professionals the opportunity to test the system and thus build up trust, using simulated patient profiles.