A few weeks ago, I spoke on artificial intelligence in health care at the AI Now Conference. I focused on the distinction between substitutive automation (which replaces human labor with software or robots) and complementary automation (which deploys technology to assist, accelerate, or improve humans’ work). I developed three cases where complementary automation ought to be preferred: where it produces better outcomes; in sensitive areas like targeting persons for mental health interventions; and to improve data gathering. Law and policy (ranging from licensure rules to reimbursement regs) could help assure that the health care sector pursued complementary automation where appropriate, rather than chasing the well-hyped narrative of robot doctors and nurses.
The pushback was predictable. Even if complementary automation is better now, shouldn’t our policy reward firms that try to eliminate ever more labor costs? Doesn’t *everyone* agree that the US spends too much on health care–and isn’t technology the best way of reducing that spending? Let me try to address each of these views, boiling down some perspectives from a longer, academic article.
A Policy at War with Itself
There is a troubling tension at the heart of US labor policy on health care and automation. Numerous high-level officials express grave concerns about the “rise of the robots,” since software is taking over more jobs once done by humans. They also tend to lament growth in health care jobs as a problem. In an economy where automation is pervasive, one would think they would be thankful for new positions at hospitals, nursing homes, and EHR vendors. But they remain conflicted, anxious about maintaining some arbitrary cap on health spending.