Despite multiple attempts at resolving labour grievances between health workers and the government, industrial unrest is unending. The current nurses’ strike, was to be averted by a return-to-work promise of a reneged Collective Bargaining Agreement(CBA) by the government. If lessons are to be taken from the teachers’, then medics should brace for a similar path.
Presently, the government is the minority employer in health services, and is heavily cushioned by the private sector’s bigger share of health workers. Otherwise the labour demand on the government would be too heavy. How long this order persists is a guess?
Beyond a certain point, the private sector’s growth bends back to a public dominated one. At the moment, fluidity exists between the two sides, with patients receiving a component of care from either side.
The private sector domination is because ailments have been mostly minor and within out-of-pocket payers. As complexity and chronicity of diseases increase, private sector costs far outstrip capacity of most patients. Similarly a formalisation of private care into insurance dominated one, is likely to raise its costs as well as shrink the number of providers. This then sees them shift to the public side. The government must be ready for this surge.
Interestingly though for unions, is technology’s confluence with union roles: job preservation and better benefits for unionised members. How does the union’s future look like with machine-learning era around the corner?
Automation, digitisation and AI-robotics are being piloted across several fronts. Initially for tasks like, lifting, cleaning, sorting etcetra to augment patient care, but ultimately, once sufficiently trained, they could have capacity to work with humans then fill in or stand in for humans.
Tech giants like IBM, Dell, Siemens, GE among many others including non-traditional tech firms are innovating around the subject. Already our everyday duties are aided by machine interpretation. The microscope is getting a smarter “retool”, gene sequencing is automated, AI guided programmes can read meta-analyses of a thousand studies and draw conclusions, recall an individual patient’s 10-year medical history and remind the doctor of a missed or forgotten important aspect of patient history: quicker and maybe more rationally.
On the automobile and logistics industry using algorithm- based decision-making matrices self-driving ambulances could select the quickest routes to hospitals, pick up and drop patients and so on.
The starting point is medicines delivering drones with auto-navigational aids replacing human couriers, then self-service points like receptions for bookings and records. In the pharmacy they could analyse patients risk and optimise choice of drugs by advising, checking for dispensing errors, detection and correction of potential adverse drug interactions.
How will humans compete with such “robots” capable of long hours without overtime demands and no unions?