Big Data and knowledge engineering for health
An informatics divide exists between research and healthcare on several levels, limiting the optimum use of health data in research, and the deployment of new knowledge in clinical practice. Increasingly, ‘Big Data’ is relevant to this disconnect.
To bridge this troublesome chasm it will be insufficient to simply try to reconcile these two realms. Instead, we need dramatic steps towards a new, distinct and currently poorly funded set of activities in ‘healthcare knowledge engineering’. This will enable research and healthcare to become bi-directionally connected in terms of data and knowledge flow and use. Ideally, research data and knowledge will be processed to identify clinically relevant advances, and these will be distilled, repurposed, and delivered into healthcare use. Simultaneously, experiences, procedures and outcomes from the practice of medicine will be made suitably available for exploitation by the research community to aid in the understanding of disease. Critically, the system will then begin to self-optimise - in that real world outcomes for patients treated by decisions that were based on latest research knowledge will be fed back to the research world, thereby enabling the latest state of knowledge to be refined and improved for use in treatment decisions for subsequent patients.