With the number of patients and limited time doctors have the positive impact for patients and healthcare budgets cannot be under-estimated. 

Wharton professor Philip Tetlock showed in a landmark 2005 study, experts that jump to conclusions can diagnose and prognose badly. He gives a telling example in his book of a senior consultants so machine learning & AI can mitigate such situations. 

Big Data-Machine learning will also change the care home  and social services aspects of healthcare. For too long these act as unjoined aspects of caring for citizens. An elderly patient who becomes ill often has to wait for a doctor to visit or an ambulance is called for what may be a wasted trip. 

A combination of telehealth with the processes/platforms Bernard Marr describes means care home residents can be remotely checked quickly and effectively by healthcare professionals and many times the prognosis actioned immediately saving distressful pain, time and money.

Extend this to include machine -sensors in equipment and fittings joined by the IoT and you begin to see joined up healthcare underpinned by healthcare platforms incorporating these technologies. 

And all the time, real-time analytics embedded in the platforms delivers the big picture needed to plan and manage healthcare services and face the challenges of ever-increasing expectations and limited budgets.

Joined up government whether in a predominantly government provided healthcare system like Europe's or mixed private and government like the US.