The recently passed One Big Beautiful Bill Act (H.R. 1) will introduce stricter eligibility criteria for Medicaid, including nationwide work requirements. As a result, the healthcare industry faces a collective new reality. The projected outcome, as identified by the Congressional Budget Office, is a significant decrease in the number of Americans covered by health insurance. This will likely have a particularly acute impact in rural areas, where Medicaid is a critical lynchpin of financial viability for small hospitals. This is something we feel needs to be addressed.

This new reality

As leaders in healthcare and health IT, there is an obligation that we approach this time of transition not as a political debate, but as a critical juncture requiring our proactivity. It’s a new reality, and on the horizon, it will demand a fundamental shift in how we approach healthcare delivery. In response, we must accelerate even further our transition away from the traditional fee-for-service model, which incentivizes reactive care for acute episodes, and set our aim on a system that is proactive, preventive and centered on long-term well-being.

As leaders in healthcare and health IT, there is an obligation that we approach this time of transition not as a political debate, but as a critical juncture requiring our proactivity.

Harnessing the power of AI

Embracing preventive and outcome-oriented care has long been the nation’s goal, but it is now even more so a financial and strategic requirement. One key to navigating this challenging landscape and achieving this ambitious vision lies in the transformative power of Artificial Intelligence (AI), applying it across all verticals of our industry. The financial pressures healthcare organizations will collectively face necessitate a dramatic increase in efficiency across every role and process in healthcare.

Here, I will break down how AI can reshape key aspects of healthcare:

• Administration and Revenue Cycle Management (RCM): AI can automate burdensome administrative tasks, streamline billing and coding, and reduce the overhead that inflates healthcare costs.
• Clinical Documentation: Ambient AI technologies are already helping to reduce physician burnout by automatically transcribing patient encounters, enabling clinicians to focus on care rather than keyboards.
• Overall Care Delivery: Predictive analytics can identify high-risk patients and care gaps. AI can assist in diagnostics, predict disease risk and suggest personalized treatment plans, leading to more effective and efficient care.
• Patient Education and Engagement: AI-powered tools can provide personalized health information and support to patients, empowering them to more proactively manage their conditions.

A deeper look at the benefits

AI can be used in RCM to ensure organizations capture every dollar they are rightfully owed. AI-powered “intelligent claim scrubbing,” for example, can improve upon existing rules-based claim scrubbers. Early analysis indicates, in fact, that these systems may reduce denial rates by as much as 30–40% when predictive analytics is used to flag high-risk claims before submission. This enables billing teams to correct errors proactively and avoid a resource-intensive back-and-forth with payers.

AI is also proving very helpful in the prior authorization process. At a Community Health Care Network in Fresno, implementing AI tools led to 22% fewer prior authorization denials and saved the staff an estimated 30 to 35 hours per week on appeals, according to the American Hospital Association.

Related to Clinical Documentation, AI is creating new clinical and operational capacity by freeing clinicians from their keyboards through the use of Ambient Scribes. Recent studies and health system reports show these tools can save clinicians more than an hour per day on documentation. This frees up providers to spend more time with patients, enabling higher patient volume and/or more thorough preventative care, which is crucial for success in value-based contracts. The downstream effects are equally important: a recent KLAS Research report found that such tools not only reduced physician burnout by 36% but also improved face-time with patients and directly translated productivity gains into revenue integrity, generating an additional $13,000 per clinician annually through enhanced documentation and coding.

Predictive analytics will also be essential for thriving under value-based payment models, which will become even more critical as fee-for-service revenue tightens. AI models can analyze patient data to identify individuals at high risk for costly hospital readmissions or the progression of chronic disease. This helps care teams intervene proactively with targeted support, remote monitoring and personalized education. By preventing acute events and improving outcomes, providers can achieve higher quality scores (e.g., Clinical Quality Measures or HEDIS measures) and earn significant shared savings and performance bonuses from payers, creating a vital financial buffer in a challenging reimbursement landscape.

AI can also generate personalized patient education on-demand, empowering patients to take a more active role in their health and wellbeing. A recent paper published in the Journal of Medical Internet Research dramatically improved the readability scores of patient education materials—making them shorter and easier to read. It is not hard to imagine that by making patient education content more readable, patients will be much more likely to succeed in adhering to their care plans, thereby bringing down systemic costs, as well.

Navigating our way to the summit

The road ahead is undoubtedly challenging. But it is also a moment of opportunity for provider organizations to maximize technology advancements in service of a likely challenging time. By embracing the imperative to become more efficient, by championing a shift to preventative care and by responsibly harnessing the power of AI, we can support a healthcare system that is more resilient in the face of financial pressures.