How effectively society as a whole is able to capitalise on big data in health care will depend, in part, on how well these data are integrated and communicated to clinicians and the public. It will be important to have digital health information collected in a format that clinicians and consumers can easily interpret and query. Much has been written about the shortcomings of electronic health record systems.
Currently, much of health information is organised around systems—eg, cardiovascular, gastrointestinal, and neurological. However, we expect that health information can also be organised according to underlying human molecular biology, and this might lead to novel health management approaches. For example, much as molecular information is grouped into networks based on interaction (physical or other) and co-expression data, digital health data might be grouped into entirely new interactive entities that might enable more efficient diagnoses and help to optimise disease management strategies (figure). The organisation of health information into interactive clusters and other novel methods for stratifying health data will complement existing approaches and potentially lead to improvements in health care. Tumour agnostic therapies in oncology represent early evidence of how the health-care paradigm might shift as health data management matures. We expect that there will be an important role for artificial intelligence in health data organisation and interpretation, with the caveat that clinical applications will need to be subject to rigorous validation procedures.