Drivers of change – inefficient silos of discovery and experimentation
Manchester BRC draws upon expertise in informatics and data sciences to link patients, clinicians, and researchers in new ways, to generate new insights from health data.
These new insights are needed particularly where traditional disease classifications combine patients with different patterns of disease and different care needs into a single group. The BRC’s precision medicine approach seeks to identify patient needs more precisely and to personalise care accordingly. This requires granular and accurate data on disease, symptoms, and outcomes. Fortunately, more sources of data are becoming available – from smartwatch and other wearable device activity data, to new blood tests. However, turning the data into useful information for biomedical discovery, and for better healthcare, is highly complex, requiring different disciplines to come together to make best use of the data.
In addition, with an ageing population, where more people are living with more than one long-term condition, we need to better understand how different conditions (and healthcare interventions) interact with one another.
This need for bigger picture research requires not only joined up data but also combined research. Biomedical research has traditionally been conducted in separate groups studying specific diseases and biological mechanisms: our BRC works collaboratively, linking research themes with the cross-cutting informatics and data sciences team. For example, a computational model used to find patients with distinctive patterns of inflammation in skin disease would be made available quickly to those needing to do similar statistical analyses in lung and joint disease research. Our BRC will eventually bring researchers from different physical ‘labs’ into a common virtual lab, where it is easier to share data, methods and expertise.