AI’s Role in the Shift from Episodic to Coninuous Care

Healthcare is constantly under immense pressure to deliver more with less – early disease detection, personalized treatment, clinical trials, compliance, and cost control. To say traditional care resources are stretched thin is a massive understatement. Despite this, the industry stands at a pivotal moment, transitioning from the mere adoption of new digital tools and emerging tech like AI to completely reimagining how care is being delivered. 

In a recent survey of more than 400 senior leaders across payers, providers, and pharmaceutical companies conducted by Bain & Company, Bessemer Venture Partners, and Amazon Web Services (AWS), 95% say AI will transform the industry. But AI adoption can only move at the speed of trust – that is, the trust that clinicians, doctors, providers, and patients can place in emerging technology. So, when looking ahead at healthcare’s future when it comes to AI, data, and cloud, the question is no longer, “How do we add technology to our existing systems?” Instead, it will evolve to, “If we rebuild care today, how would AI, virtual care, and real-time intelligence shape it?” 

By 2026, the convergence of AI, edge computing, and data modernization will ignite a fundamental shift to a more connected, cohesive, and anticipatory human-centered model of care. This next era is no longer about isolated AI applications or quickly adopting the latest and greatest models, but about intelligent systems that orchestrate care seamlessly across every touchpoint.

2026: An Inflection Point

While many AI solutions already exist in healthcare, they are often limited to narrow tasks like managing communications, performing administrative work, or acting as prompt-driven assistants. The overarching patient journey remains splintered, as individuals frequently bounce between providers, insurers, pharmacies, and health systems that rarely, if ever, talk to one another. 

A major driver of this challenge is the inability of the current tools and platforms used to seamlessly pull complete and accurate data at a moment’s notice. According to a report from Mathematica and Reveleer, “only about one-third of leaders rate their data integration capabilities as excellent,” while 70% say their existing platforms are too complex or time-consuming for providers to use effectively, and 80% agree there are too many manual processes.

As technology continues to advance faster than governing bodies can establish robust guardrails and regulations for safety, risk, and compliance, the pressing need for better data integration and usability sets up 2026 to be a natural inflection point for the rise of intelligent systems capable of coordinating care, anticipating needs, and reducing bottlenecks. This is also the moment when AI agents will begin taking on more operational responsibility. Rather than just supporting care teams, they’ll be able to actively manage and optimize the flow of care itself.

Here’s what we can expect to see:

Data and Workflows Will Converge Across the Healthcare Ecosystem. In 2026, we can expect AI to act as a unifying force as it pulls and mines data from multiple sources, such as medical records, imaging systems, claims, pharmacy records, care files, wearables and even homebased sensors to create a comprehensive view of each patient’s health. Modern data platforms that handle vast amounts of data and support more users, like AWS Healthlake and Google Health Data Engine, are already enabling this shift by providing real time analytics, FHIR-compliant pipelines, and collaborative workspaces for care teams.

This convergence enables more cohesive AI-powered care models, where transitions between settings, such as from hospital to hospital, or even hospital to home, are seamless and well-coordinated. For example, AI-enabled planning tools being piloted at Mayo Clinic and Mercy Health can now combine patient vitals, transportation data, and home health availability to facilitate discharge procedures and follow-up tasks.

AI Will Be a Catalyst for Value-Based Care. By 2026, agentic AI systems will have a pivotal role in the progression to value-based care, where providers are incentivized to proactively keep populations healthy rather than only treating illness and injury after it happens. Agentic systems will be used to reach across tools and datasets, and create a continuous, adaptive flow of insights and feedback loops to assist care teams to identify high-risk patients, provice alerts of any gaps in care, recommend informed interventions, and predict trends or outcomes based on socioeconomic factors. Importantly, agentic AI won’t replace clinical judgement, but instead, it will strengthen providers’ ability to act early. 

Prominent healthcare networks like Kaiser Permanente and Intermountain Health are aggressively investing and testing AI tools that can extract patient data and proactively flag patients at risk of medication noncompliance, inconsistency in medical appointments, or gaps in access to nutrition. By addressing these underlying elements, providers can provide anticipatory care that helps to prevent health issues before they are likely to arise.

Ambient Intelligence Will Transform Clinical Environments. Today, most care models are reactive, only responding after symptoms appear. As agentic AI capabilities increase in 2026, providers will be able to move beyond traditional text-based outputs and static automation of defined tasks to actionable intelligence, transforming formerly passive spaces into active participants in care delivery by allowing them to sense, analyze, and respond to patient needs in real-time. Imagine a hospital room where lighting could be adjusted when a patient moves across the room, vital signs and patient activities could be observed and evaluated, and medications administered, monitored and modified, with proactive alerts to physicians in the right circumstances, all without manual intervention. 

Ambient intelligence is currently being piloted by institutions like UC San Diego Health, where sensors monitor patient movement to detect elevated risk of falls or accidents, and in John Hopkins’ smart stethoscopes that alert nurses to early signs of respiratory decline.

AI Agents Will Emerge as System-Level Operations Managers. Enabled bythe three previous predictions, the biggest shift we’ll see in 2026 is AI stepping into operational management roles. Just as these agents are already aiding in patient flow, enabling treatment planning, and coordinating health insights, in 2026 agentic AI will develop the ability to act as a system-wide coordinator, constantly evaluating everything from capacity and resource utilization to staffing levels and process constraints.

Rather than acting within a single workflow as has been the case, AI agents will break silos that have long plagued many types of healthcare organizations. By dynamically reallocating supplies when demand surges and matching resource availability with different process needs, 2026 will see AI agents’ ability to adjust operational plans based on live data in hospitals, outpatient centers, pharmacies, and even home-based care environments.

Beyond the New Year: AI Enables Human Relationships 

As agentic AI and other emerging technical capability pushes healthcare to redesign itself, 2026 marks a shift from AI as a supportive tool to AI as an active orchestrator of care operations. Clinical teams won’t just be informed but fully supported by a solution that is continuously optimizing itself in the background. And as more and more operational workflows will be open to new interpretation and guardrails, human-in-the-loop systems will remain critical for accountability, safety, and ethical AI usage. 

Healthcare’s next era of innovation will focus on anticipation, context, and collaboration, ensuring that technology strengthens the patient-clinician relationship by clearing many of the obstacles that complicate it today. Technology will be the silent partner, streamlining workflows and freeing clinical teams to focus on what matters most: compassionate, patient-centered care.