It’s Time to Rethink the “A” in AI: Why We Need to Stop Calling it “Artificial” Intelligence

By Janine Savage, Division President, Value-Based Care Solutions

Artificial intelligence in healthcare is seemingly everywhere. It dominates conference agendas, vendor messaging, and strategic planning conversations. But in value-based care, the word “artificial” misses the mark.

What healthcare organizations need is more than just hype about artificial intelligence. They need responsible AI: intelligence that improves patient outcomes, supports clinicians, reduces administrative burden, strengthens compliance, and drives better performance in value-based payment models.

That’s why it’s time to rethink the “A” in AI.

In healthcare, the most important question is not only whether intelligence is artificial; it’s about whether that intelligence is accountable, actionable, augmented, aligned, accessible, and auditable. I believe AI should serve a larger purpose: helping providers, payers, and healthcare policymakers leverage analytics and insights to improve quality of care, operational performance, and total cost of care to ultimately result in better outcomes across the continuum. Specialized health IT solutions improve efficiency, elevate patient care, streamline operations, and drive the outcomes we all know are necessary for the long-term sustainability of the healthcare system.

Artificial Intelligence in Healthcare Is Not the End Goal

There is no shortage of excitement about AI in healthcare. But too much of that conversation remains focused on the technology itself – the machines, the models, the novelty. Healthcare leaders don’t need or want AI for AI’s sake.

They need solutions that help them solve real problems, like:

• Improving quality outcomes
• Supporting reimbursement accuracy and compliance
• Reducing manual documentation burden
• Identifying risk earlier
• Operationalizing value-based care
• Turning healthcare data into meaningful information

For providers and operators, it’s about having information they wouldn’t otherwise have to make clinical and operational decisions. And for policymakers, it’s about spending more wisely and protecting program integrity.

That’s why “artificial” emphasizes the wrong element. The future of AI in healthcare will not be defined by how artificial it is, but by how responsibly and effectively it is used.

What If We Replace “Artificial” with “Accountable”?

In healthcare, intelligence must be accountable to outcomes. This is especially true in value-based care analytics, where insights influence care delivery, quality scores, reimbursement, and strategic decisions. In Medicaid value-based payment (VBP) programs, for example, data-driven decisions affect public dollars, provider performance, and patient outcomes.

Accountable intelligence means:

• Tying outputs to measurable quality improvement
• Supporting transparent methodologies
• Enabling traceability from source data to recommendation
• Connecting analytics to reimbursement, compliance, and care improvement

If an AI-powered recommendation cannot be understood, validated, or defended, it should not influence care management or value-based payment. In healthcare, accountability is what turns analytics into trust.

Replace Artificial with “Actionable” for Immediate insights

In healthcare analytics, we often see too many insights stay trapped in reports and dashboards. Actionable intelligence does something different; it helps users know what to do next with that information.

This is where predictive and prescriptive analyticsbecome powerful. Not only do they generate interesting observations, but they also drive interventions, care planning, coding accuracy, documentation improvement, and operational decisions.

For example, in skilled nursing organizations, actionable intelligence may mean surfacing care risks earlier, improving MDS coding accuracy before submission, identifying reimbursement opportunities, helping teams prioritize quality improvement activities, or embedding decision support into existing workflow at key points.

In other words, healthcare AI should not simply analyze the past. It should inform and improve the next decision with actionable insights.

Keep a Human Focus by Replacing “Artificial” with “Augmented”

There is understandable anxiety around AI replacing people, but the most valuable AI in healthcare does not replace clinicians, operators, or care teams. It strengthens them.

The right use of AI is not to remove human judgment, but to augment it. Augmented intelligence has verifiable benefits for you in your day-to-day. For clinicians, you can focus attention where it matters most. Administrators can better manage quality performance and reimbursement risk and help staff work more efficiently and make faster, better-informed decisions. Executives make stronger strategic decisions from better data, and policy makers design smarter programs, target resources more effectively, and evaluate impact with greater confidence.

The goal is not fewer humans in healthcare. The goal is better-supported humans who deliver better care and better-informed leaders who shape better systems.

Improve Care When You Replace “Artificial” with “Aligned”

Healthcare technology only creates value for humans when it aligns with how care is delivered and how performance is measured. In value-based care, that means alignment with:

• State and national quality strategies
• Care delivery and payment transformation priorities
• Provider readiness and workflows
• Performance measure selection
• Financial incentives
• Cross-setting care coordination

Misaligned intelligence creates friction, increases burden, encourages metric chasing, and produces outputs that may be technically interesting but operationally irrelevant.

Aligned intelligence, by contrast, helps organizations succeed in the real-life environments they actually operate in: complex, regulated, financially constrained, and deeply dependent on consistent execution. This kind of alignment is what makes AI useful in healthcare – not as a novelty, but as the right fit for the right job.

How Do We Affect Adoption When We Replace “Artificial” with “Accessible”?

Healthcare AI must be usable by the people closest to the work. If a solution requires a technical intermediary every time an insight appears, adoption will stall, not to mention care. If the interface is too complex, busy teams will not engage. If the output is disconnected from workflow, the value will never be realized. Accessible intelligence means technology is clear and embedded at the point of interaction, role-based, easy to operationalize, and designed for real-world users, not just analysts.

This matters in every healthcare setting, but especially in skilled nursing and post-acute care, where teams need practical guidance they can act on quickly. They typically don’t have direct access to informaticists or analysts, which applies to many other care settings as well.

The best healthcare analytics platform doesn’t just have the most complex model. It’s needs to be used consistently to improve care, compliance, and performance to make a difference for your patients.

Stay Compliant as You Replace “Artificial” with “Auditable”

Healthcare is a high-trust, high-scrutiny environment. AI must earn our trust. That means intelligence must be auditable, particularly when used in quality measurement, compliance workflows, reimbursement optimization, and value-based care programs. Auditable intelligence supports data lineage, transparent logic, reproducibility, validation and reliability, and defensibility with providers, payers, and regulators.

This trust is particularly important in settings where analytics intersect with payment. If a healthcare AI solution influences quality scores, reimbursement performance, or operational priorities, users and owners must be able to understand how outputs were generated. Responsible AI should drive better patient care, and features should put compliance first. In healthcare, trust is built not only through performance, but through explainability and reliability.

Responsible AI in Healthcare Means Rethinking the “A”

The healthcare industry does not need more conversation about artificial intelligence as an abstract concept. We need a clearer standard for what good looks like. That standard should be built around intelligence that is:

• Accountable to outcomes
• Actionable in workflow
• Augmented by human expertise
• Aligned to incentives and care models
• Accessible to frontline users
• Auditable under scrutiny

This is the kind of responsible AI in healthcare that can make a meaningful difference in value-based care outcomes broadly across the continuum.

Personally, it’s exciting to be at the forefront of the transformation in healthcare operations and payment being driven by responsible AI. It’s not aspirational – it’s real, and it’s happening now.