From Sick Care to Smart Care: the Intelligent Use of AI in Healthcare

For too long, healthcare has been stuck in a reactive loop.  We wait for patients to get sick, then rush to treat the symptoms.  In an era of workforce strain, rising chronic disease and unsustainable costs, that model is not viable.  Health systems are forced to do more with less – while simultaneously improving outcomes and patient experience.

The good news?  We have the tools to flip the script.

Artificial intelligence (AI), powered by secure, high-quality data, offers a path forward: a shift from sick care to smart care, addressing the causes of disease ahead of the symptoms.  Predictive, preventative and proactive.  This isn’t tech hyper.  It’s a strategic imperative for any provider organization looking to stay ahead of clinical and operational headwinds.

Predictive, not just reactive

AI excels at seeing patterns in data that the human eye misses.  Applied to patient records, imaging, genetics and real-time biometric data, AI can identify at-risk individuals and trigger early interventions before symptoms escalate.  Think of a patient whose wearable picks up small but consistent changes in respiration, blood pressure and sleep quality.  AI correlates these patterns with early signs of sepsis, prompting a clinical team to act hours before the patient feels unwell.

This kind of prediction isn’t speculative.  It’s already at work in some emergency departments and ICUs today.

A vivid example includes how ambient sensing, AI-driven triage and real-time risk analysis can transform the emergency room into a space of high-functioning calm.  Notably, these tools reduce moral injury and burnout by taking pressure off human decision processes during critical, high-pressure moments.

Personalization at scale

Forget one-size-fits-all protocols. AI means we can precision fit care plans to the individual.

Patients with diabetes or heart disease can receive daily nudges and support via digital companions that adjust to their behavior and biometrics. Care plans evolve based on adherence, vitals, social determinants and even environmental factors. Instead of generic checklists, patients receive what works for them when and where they need it.

That kind of personalization, at scale, is impossible to manage manually. But with AI, care teams can understand how each patient is responding in real time. For example, if a patient misses multiple doses of medication or shows changes in physical activity, the care plan can be updated automatically, and appropriate support triggered whether that's a virtual coaching session, a telehealth check-in or a reminder to a family caregiver.

Another example that illustrates this concept well is an AI-powered health twin that learns continuously, guides decision-making and adjusts care dynamically. It doesn’t replace the physician, it makes care more precise and timelier, especially for complex or chronic cases.

Clinician empowerment, not replacement

Burnout among clinicians is at a breaking point. More than half of US physicians report symptoms of burnout. The top culprits? Excessive documentation, administrative overload and not enough time with patients.

AI helps shift that balance. Intelligent triage systems can surface the most urgent cases and flag patterns that warrant closer attention. Ambient documentation tools can automatically generate visit notes from clinician-patient conversations, reducing the burden of typing and transcription. Clinical decision support systems can synthesize patient history, lab results and best-practice guidelines into concise, actionable insights.

These tools don’t just save time-they improve safety. By reducing cognitive load, they decrease the chance of errors and allow clinicians to focus on what they do best: practicing medicine with empathy, expertise and human connection.

AI is not here to replace clinicians. It’s here to support them. For example, ambient AI can pick up on tone, hesitation and language changes to support early diagnosis as well as capture patient/provider interactions. This removes the clerical overhead that erodes face-to-face care.

Seamless data, smarter infrastructure

AI only works if it has the right data. Right now, too much healthcare data is trapped in silos.  Siloes between organizations. Siloes within organizations.  Siloes of syntax.  Siloes of semantics.

To deliver proactive care, we need secure, interoperable data systems where information follows patients across settings. That includes longitudinal patient records, consent-driven data sharing, and privacy and compliance baked into every layer. These aren’t solely. technical upgrades. They’re foundational for any health system that wants to scale AI responsibly.

Cloud platforms, open standards like Fast Healthcare Interoperability Resources (FHIR) and event-driven data models are making this feasible, and technology alone won’t solve it. We also need governance, trust, and incentives aligned around the patient.

Patients, for their part, increasingly expect their data to be portable, secure, and usable. And AI-powered digital front doors are only as good as the data that powers them.

Why now? Why not now?

So why now? Because everything has changed.

AI tools have matured. Large language models, predictive analytics, and clinical algorithms are now accurate enough to be safely embedded into workflows. Data is finally being captured continuously-from wearables, apps, diagnostics and patient-reported outcomes. And healthcare systems are under enormous pressure to do more with less.

Put bluntly: there’s no path to sustainability in healthcare that doesn’t involve smarter systems.

What’s next? Turning vision into reality

If this is the future we want to build, the work starts now-and it starts with data. High-quality, secure and representative data is the foundation of any AI-powered healthcare model. Without it, even the smartest algorithm is just guesswork.

So, what can leaders do today?

1. Get your data house in order. Start with data governance. Identify your most valuable clinical and operational data sources and ensure they are accurate, clean and connected. Break down silos and push for Health Level 7 (HL7) FHIR-aligned interoperability wherever possible.
2. Double down on security. Proactive care can’t come at the expense of trust. Invest in zero-trust architectures, data encryption and continuous monitoring. Invest in zero-trust architecture, data encryption and continuous monitoring. Make cybersecurity part of the clinical safety culture.
3. Build AI literacy across the organization. This isn’t just for IT or data science teams. Everyone-from care coordinators to CFOs-needs a working understanding of what AI can (and can’t) do, how it fits into workflows and what responsible use looks like.
4. Prioritize early wins. Don’t wait for a moonshot. Start with targeted, high-impact use cases-like readmission prediction, ambient documentation or population health risk stratification. Prove value, then scale.
5. Put equity front and center. AI is only as fair as the data it’s trained on. Be intentional about including diverse populations in model development and validation. Design with equity in mind, not as an afterthought.

We already know what’s possible. Now it’s about making it practical. The shift to proactive, AI-powered healthcare won’t happen all at once-but it won’t happen at all if we don’t start building toward it.

A smarter model for health

We’re not building science fiction. We’re building a smarter, more humane model of care-one where: illness is detected before it spirals; treatment is tailored to individuals; clinicians are empowered, not buried in admin and every moment of care is backed by insight, not guesswork.

Some of these futures are already here. Others are emerging quickly. But all of them are grounded in real tools, in real settings, with real impact.

If we can stay focused on ethics, on equity, and on empowering the workforce-we can go far beyond automation. We can build a healthcare system that keeps people well. From sick care to smart care. That’s the shift.

Andy Truscott is Global Health Technology Lead at Accenture.