Medicaid has become a central point of a heated political battle, as Republican lawmakers push for deep cuts to help fund tax reductions. President Donald Trump and GOP leaders aim to slash Medicaid spending by $880 billion over the next decade, trimming roughly 10% of the program’s budget. However, the consequences could be severe as Medicaid provides health coverage for roughly 83 million low-income Americans, including seniors and people with disabilities.
To secure Medicaid’s future, artificial intelligence (AI) is emerging as a potential solution to rising healthcare costs. Today, AI-driven predictive analytics allows healthcare providers to identify high-risk patients before they require emergency care.
“With Medicaid facing budget constraints, AI can reduce costs without sacrificing quality,” Grace Chang, CEO and founder, Kintsugi, told me. “Operational inefficiencies, like missed diagnoses or poor patient follow-up, are often invisible but incredibly costly. AI can flag patients at risk of ER overuse or medication nonadherence – areas that bleed billions from the system but are solvable with the right tooling.”
California-based AI healthcare startup Kintsugi utilizes voice biomarkers to automate early screening for depression and anxiety patients, helping reduce clinician assessment time. Chang asserts that most healthcare systems are already understaffed, and AI can help prioritize who needs attention most, when it matters most.
According to the founder, the real risk of not using AI to solve healthcare’s toughest issues is “that we won’t use it to close critical gaps in care.”
How AI is Reducing Medicaid and Healthcare Costs in General
Administrative inefficiencies account for a significant portion of healthcare costs. But, a study by the National Center for Biotechnology Information (NCBI) estimates that AI could save the healthcare industry up to $150 billion annually by streamlining these processes. Likewise, the National Bureau Of Economic Research estimates savings as high as $200–$360 billion in health care spending through AI automation in the next four years. Today, AI is playing an essential role in Medicaid and healthcare by forecasting disease outbreaks and demographic shifts, enabling proactive resource allocation. The technology is also helping enhance predictive analytics to anticipate patient outcomes, leading to more effective treatment strategies and improved preventive care. Additionally, AI can advance personalized medicine, tailoring treatments to individual patients for better results.
Harnessing recent tech innovations, several AI-powered healthcare startups are at the forefront of improving AI adoption in Medicaid to accelerate diagnoses and improve treatment outcomes. For instance, Boston-based Quantivly is enhancing radiology efficiency through its AI-based platform to optimize MRI and CT scanner utilization. AI can pinpoint bottlenecks in imaging workflows, leading to reduced patient wait times, improved scanner throughput and hospital revenue.
“Health systems, especially those serving Medicaid populations, are being asked to do more with less. And they need to do more scans to compensate for the reality of lower margins,” Robert MacDougall, co-founder of Quantivly, told me. “Operational AI in medical imaging can help in managing throughput without putting the stress on staff. AI can be deployed in areas like scheduling, where the coordination task is too complex for any one person to manage manually.”
According to MacDougall, most scheduling systems overlook critical factors that impact scan duration, such as scanner hardware, protocol complexity, patient mobility and sedation needs. Managing these variables in real time is beyond human capability, making AI an essential tool for optimizing scheduling and efficiency – and helping hospitals’ bottom lines.
Likewise, AI-powered medication management platform Arine helps reduce prescription errors by optimizing drug regimens and flagging unnecessary medications. “AI can rapidly connect the dots across diverse data sets (patients’ medication histories, SDOH data, and clinical/medical literature) to make personalized recommendations for each patient,” Yoona Kim, CEO and Founder of Arine, explained.
She added that if a patient is prescribed a new medication without considering its potential negative impact on existing conditions, AI can flag the issue in real time—preventing complications before they result in an ER visit. “AI may automate repetitive tasks (e.g., documentation, summarization) but when it comes to patient care, we need to keep clinicians in control,” said Kim.
Given AI’s potential to improve healthcare efficiency and outcomes, will lawmakers prioritize its adoption, or will budget constraints and fiscal policies overshadow access? How this debate unfolds remains to be seen.
“The goal of operational AI is to expand access by improving how resources are used. If we can scan more patients on the same equipment without adding burden to staff, we’re improving access — especially in under-resourced areas. The key is productivity, not restriction,” MacDougall emphasized.
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