“Human detection and identification is error-prone, inconsistent and inefficient. Perhaps most importantly, it’s not scalable,” says Morgan. “Newer imaging technologies are outstripping human capabilities to analyze the data we can produce.”
Source: Eagle-eyed machine learning algorithm outdoes human experts — ScienceDaily
The point here is that data is being generated faster than we can analyse and interpret it. Big data is not a storage problem, it’s an analysis problem. Yes, we’ve had large sets of data before (think, libraries) but no-one expected a human being to read through, and make sense of, all of it. Now that digital health-related data is being generated by institutions (e.g. CT and MRI scans, EHRs), wearables (e.g. Fitbits, smart contact lenses), embeddables (e.g. wifi enabled pacemakers, insulin pumps) and ingestibles (e.g. bluetooth-enabled smart pills), it’s clear that no single service provider will have the cognitive capacity to analyse and interpret the data flowing from patients at that scale.
As more and more of the data we use in healthcare is digitised, we’ll need algorithmic assistance to filter out and highlight what is important for our specific context (i.e. what does a physio need to know about, rather than what the nurse needs). There will obviously be a role for health professionals in designing and evaluating those algorithms but will we be forward-thinking enough to clearly describe those roles and to prepare future clinicians for them?