p-medicine: A Medical Informatics Platform for Integrated Large Scale Heterogeneous Patient Data

Abstract

Secure access to patient data is becoming of increasing importance, as medical informatics grows in significance, to both assist with population health studies, and patient specific medicine in support of treatment. However, assembling the many different types of data emanating from the clinic is in itself a difficulty, and doing so across national borders compounds the problem. In this paper we present our solution: an easy to use distributed informatics platform embedding a state of the art data warehouse incorporating a secure pseudonymisation system protecting access to personal healthcare data. Using this system, a whole range of patient derived data, from genomics to imaging to clinical records, can be assembled and linked, and then connected with analytics tools that help us to understand the data. Research performed in this environment will have immediate clinical impact for personalised patient healthcare.

Figure 1.

System design overview diagram. All traffic is secured by the authentication and authorization services in the secure framework. In addition, all data pushed into the data warehouse must pass through the pseudonymisation process to assure the privacy of the data stored in the warehouse. Finally, when data to be annotated is uploaded to the data warehouse, it triggers the ontology annotator and the data translator modules.

Figure 2.

The data upload process. Data is exported in a p-medicine “compliant” format before being pushed into the local data warehouse and the central data warehouse via the trusted third party.

Figure 3.

Pseudonymisation overview. Each user receives a pseudonym when data is pushed from the hospital. Then it is sent to the trusted third party (TTP) and it generates an anonymous identifier for this user, which is the one that is stored in the data warehouse. However, the TTP stores the link in case re-identification is needed.

Figure 4.

Structure of the annotations defined for each database. Semantic equivalences are defined with the set of entries, each containing an equivalence of a database view and an HDOT view.

Figure 5.

The data translation process uses database annotations as input. Resulting data conform to a common vocabulary provided by a cancer-related ontology. Provenance information is added to the transformed data.

Figure 6.

Data flow for sync services (left) and push services (right). The sync services retrieve data from the LDW installed in the hospital. The Patient Identity Management Service ensures that pseudonyms can be mapped to patients in ObTiMA. The push services are able to push trial data from ObTiMA either to the LDW or DWH. To translate trial data to a format compliant with HDOT the Data Translation Services are used.