Automation in healthcare is often framed as a question of efficiency. This team challenged that narrative by asking a more important question: efficiency for whom, and at whose expense?
During the Map the System Competition at Johns Hopkins University, Shilpy Singla Vohra, Deborah Law-Onilearo, and Pooja Prasad examined the rapid adoption of AI and automation across the U.S. healthcare system alongside a striking lack of investment in workforce reskilling. Their analysis revealed a growing paradox: while healthcare organizations experience productivity gains and cost reductions at the top of the system, workers at the frontline often face displacement, burnout, and widening inequality.
What stood out most about their presentation was the way they moved beyond blaming technology itself. Instead, they identified the deeper structural failures driving the issue. As Deborah explained, the core problem “lies in a policy and design failure. Healthcare organizations moved quickly to adopt AI and automated tools for efficiency and cost reduction, while investment in workforce reskilling, governance, and equity protections remained weak, fragmented, and reactive.” The team illustrated how payment models incentivize cost-cutting, training pathways remain disconnected, and displaced workers are often expected to independently adapt to rapidly changing demands. Together, these factors create a system where the human consequences of automation are largely ignored because they are difficult to measure within traditional efficiency metrics.
Shilpy brought a strong sense of urgency to the discussion when describing the long-term implications of inaction. “With AI and automation moving so fast, we are literally sitting on an active volcano,” she shared. “If we don’t start reskilling now, the gap between the skills people have and what the job market needs will explode.” What made her perspective especially compelling was that it emerged directly from the team’s research process. She explained that prior to this project, she had not thought deeply about workforce reskilling, but by the conclusion of their work had become an advocate for it herself.
Pooja’s contribution emphasized the complexity of the stakeholder landscape surrounding healthcare automation. The team made it clear that there is no single policy solution capable of resolving the issue. Instead, meaningful progress requires systems-level thinking, causal loop mapping, and collaboration across healthcare organizations, policymakers, workers, and patients. Their presentation consistently reinforced the idea that automation cannot be treated as an isolated technological advancement, but rather as a societal shift with widespread consequences.
One of the strongest aspects of the presentation was the practicality of the team’s proposed next steps. They argued that any large-scale automation initiative in healthcare should begin with three core requirements: a workforce impact assessment, protected funding for reskilling programs, and meaningful input from frontline workers and patients throughout implementation. This framework reframes the conversation entirely, shifting the focus away from simply asking, “How do we deploy this technology?” and toward the more critical question: “Who is responsible for the outcomes it creates?”
Although the team members approached the topic from different perspectives, they ultimately arrived at a shared conclusion: the automation paradox is not inevitable. It is the result of human decisions, policy choices, and institutional priorities — which means it can also be changed.
Special thanks to Mahima Singh for sharing insights that helped shape this article.
