The Australian E-Health Research Centre has strong research and development capability in clinical data capture, integration, visualisation and analysis. Where data already exist, our HDI technology will continue to provide a useful data integration capability. To provide HDI with more semantically meaningful data integration capabilities, we developing new tools, the Snapper Platform, that more accurately capture the semantic meaning of clinical data using standardised clinical terminologies (e.g. SNOMED-CT). Typically the captured clinical data is then stored in an information model, of which there are are several emerging standards around the world, including openEHR and the NHS Logical Record Architecture. Our involvement in clinical trials, such as AIBL, along with our work with Queensland Health collaborators, provides opportunities to test these technologies in "real world" settings.
Clinical Data Acquisition, Integration, Visualisation and Analysis
Clinical trails typically collect health information about trial volunteers over a number of years in order to correlate observations across the cohort in order to uncover new insights into disease progression or drug performance. The Health Information Systems project, in collaboration with our colleagues in Biomedical Imaging, are acting as data managers and part of the bio-statistical team for trials such as the Preventative Health National Research Flagship's Australian Imaging, Biomarker and Lifestyle (AIBL) Flagship Study of Ageing study.
Breakthroughs in early diagnosis tools and interventions require the exploitation of many data sets including those generated by clinical trials, previously developed or obtained by CSIRO and numerous other public data sets. The complexity, interconnectedness and heterogeneity of the data sets demands specialist skills and tools in its acquisition, integration, management, visualisation and analysis. The Health Information Systems project draws on AEHRC's Health Data capability to integrate, semantically enrich and interlink these diverse data sets to support analysis.
Our approach to clinical data acquisition is driven by a number of challenges. One challenge is to respect the rich semantic meaning of clinical data by building tools that fully capture its inherent complexity. For example, cognitive decline captured by neuropsychological test results over time can be related to anatomical changes in the brain such as cortical thickness. Another challenge is that the tools need to support being "bolted on" to the existing health information technology infrastructure at the medical facilities treating patients in order to ensure that the data is easily available for clinical treatment and research in the future. Yet another challenge is to deal with the distributed nature of the Australian health care system, where patient information is spread across clinical disciplines, organisational boundaries and in some cases jurisdictions.
Electronic Health Record Visualisation
Due to the complexity inherent in the data, visualisation is increasingly seen as a key requirement for contemporary biomedical research. For the same reasons, the visualisation of health records in the clinical setting, including the comparison of a particular patient's health against clinical guidelines or a cohort, is becoming a key requirement for clinical systems. The project will continue its early Patient Journey Browser work by enabling the visualisation of the journey as captured using the clinical terminologies and electronic health records described above. This work provides researchers with the ability to visualise the journey of Study participants through the trial.
Bio-Statistical Analysis
In addition to managing trial data sets, the Health Information Systems project also conducts and contributes to a number of bio-statistical analysis activities.
A Representative Data Set For Australian Cognitive Performance
Compared with neuroimaging techniques, neuropsychological tests are a cost effective and widely available clinical tool for detecting cognitive decline associated with Alzheimer's disease. Australian neuropsychologists currently assess individual patient cognitive performance using normative data gathered from low numbers of overseas subjects. Anecdotal evidence suggests that these normative data are not an ideal reference for Australian patients and the entire process is "fiddly" and time consuming.
The AIBL Study is globally significant due to its large scale: some 1112 volunteers are involved in the trial, including over 600 health control subjects who have completed a large battery of neuropsychological tests. The Study's scale provides researchers with a unique opportunity to create a normative data set that more accurately reflects Australian cognitive performance norms. Access to such a data set would be useful for clinicians to compare their patient's cognitive performance relative to healthy older Australians. For example, a more accurate understanding of cognitive performance will assist clinicians in developing strategies for disease sufferers and their families.
