The New Healthcare Spending Law Puts Healthcare Systems on the Clock
By Sean Cassidy
The clock is ticking for healthcare systems in wake of the new spending package.
The near-universal reaction among health systems and others in the industry is that conditions will worsen for them and their patients over the next several years due to spending cuts and eligibility restrictions, particularly in the Medicaid program.
Since the bill was signed into law on July 4, healthcare administrators have been crunching numbers and making projections, trying to determine how to make the new math work while minimizing the impact on patients. Each hospital system will need to find its own formula, but there is one step they can all take now that will have enormous benefit for existing Medicaid patients: implement programs that accelerate disease diagnosis, so patients receive treatment before they potentially lose coverage in 2027.
Existing screening programs for cancers, diabetes, cardiovascular, and other diseases, follow evidence-based guidelines and target patients in designated age cohorts or patients with family histories or pre-existing conditions. Screening is essential for identifying potential health problems before symptoms appear, but we can’t screen everyone for every disease; casting a wide net is too expensive given the ultimate rate of detection. Ideally, health systems would be able to implement targeted screening and diagnostic programs that identify more patients at relatively low cost. Early detection makes treatment more effective, improving patient outcomes, reducing long-term complications and even saving lives. Screenings can also deliver strong value for health systems, offering low-cost interventions that avert expensive treatments and save billions in healthcare spending.
But how?
Healthcare systems that want to screen, diagnose, and treat as many Medicaid patients as possible before access to care is reduced or eliminated should adopt AI-powered digital tools that can evaluate large populations of patients for their risk of having undiagnosed cancers or chronic diseases.
These tools analyze existing clinical data to help identify patients who may be at higher risk for certain conditions. The analysis is performed digitally and does not require direct participation from patients or clinicians. Patients identified as high risk can then be referred to low-cost screening programs (e.g., low dose CT scans for lung cancer or continuous cardiac monitoring for arrhythmias) that facilitate disease diagnosis. Because of the AI-powered “front end” to this process, the percentage of flagged patients who are ultimately diagnosed with disease is significantly higher than with random screening. For example, an NEJM Catalyst study at Geisinger Health found that an AI model flagged colorectal cancer at 8x the rate of standard screening—8% vs. 1% (Underberger, D., Boell, K., et al.).
AI-powered early disease detection programs, if implemented before the 2027 cuts, could help thousands—or even millions—of patients receive life-changing treatment before they lose their Medicaid coverage. Because these programs help catch diseases earlier, when treatment is simpler, community providers can manage care locally and retain treatment revenue, instead of losing patients to larger tertiary care facilities after delayed diagnoses make conditions more complex.
Despite the new challenges, healthcare systems will have to find a way to continue to do what they’ve always done: adapt and innovate to serve their communities and provide the best possible care. AI-powered early disease detection programs can meaningfully support that mission. The sooner they begin, the better.
Sean Cassidy is CEO and Co-founder of Lucem Health, a leader in AI-driven early disease detection solutions. Cassidy founded the company with Mayo Clinic in 2021. He is responsible for helping the Lucem Health team fulfill the company’s mission to revolutionize care delivery through the practical application of clinical AI. He is also a member of the Board of Directors of TrueLearn, a healthcare focused education technology company.