U.S. healthcare faces a productivity crisis that more hiring and more technology alone cannot solve, and clinical-care organizations must redesign their operating models around human-AI workflows to achieve meaningful gains, McKinsey argues in a July 2 perspective.
Labor productivity in clinical-care organizations has declined roughly 1% over the past 20 years, while productivity across the broader U.S. services economy has increased more than 55%, according to the perspective. U.S. clinical-care organizations now invest more than $150 billion annually in IT, yet continue to face compounding costs and margin compression. Becker’s reported in April that hospital labor productivity peaked in 2001 and has been essentially flat or declining since, per Bureau of Labor Statistics data.
McKinsey argues that 40% to 50% end-to-end process improvements are needed, far beyond what point solutions or isolated pilots can deliver. In care delivery, a redesigned medical-surgical unit model incorporating virtual nurses and ambient documentation tools could redistribute work across clinical teams and help organizations reduce labor costs by more than 20%, according to the perspective. Some systems have seen first-year RN turnover fall by more than 60% following care model redesigns.
On the administrative side, up to 50% of administrative work can now be automated, McKinsey says. Revenue cycle management redesigns using agentic AI workflows can yield up to 40% productivity gains, while shared services redesigns can achieve 25% to 50% productivity improvements overall, according to the perspective.
McKinsey cautions that partial automation alone does not change cost structure. Capturing value requires new capabilities in human judgment, cross-functional collaboration and human-AI orchestration, as well as new roles, skills and staffing models, according to the perspective.
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