Agentic AI in healthcare: Rethinking clinical workflows beyond the EMR

Has healthcare reached the limits of incremental digital change?

Healthcare has spent years adding systems, tools, and digital layers to manage growing complexity. Yet care delivery remains fragmented, workflows are brittle, largely because automation has been introduced incrementally, often on top of legacy or previously non-digital systems.

Incremental improvements and isolated AI initiatives optimise individual tasks, but they rarely change how decisions are made and executed across healthcare workflows. In many cases, they simply add another layer to an already complex environment.

Agentic AI represents a structural shift. Instead of supporting individual activities, AI agents coordinate decisions and actions across workflows, enabling healthcare organisations to move beyond patchwork digital fixes towards more adaptive, system-level change.

What “agentic” actually means in a healthcare environment

Healthcare has already adopted automation and AI copilots to support individual tasks. These approaches add efficiency, but they remain limited in scope and heavily dependent on human direction.

Agentic AI introduces a different model. AI agents can reason about objectives, plan actions, and operate across multiple systems within a workflow. In healthcare, autonomy is not about replacing clinical judgement, but about coordinating decisions and actions across workflows, while keeping humans in control of final decisions (human-in-the-loop by design).

In practice, this means fewer steps that rely on people manually pushing work from one stage to the next. As healthcare delivery spans multiple teams and systems, coordination, not execution, increasingly becomes the limiting factor.

Why optimising tasks is not enough

Healthcare organisations have invested heavily in AI tools designed to optimise individual tasks. While these solutions can improve efficiency at specific points, their impact is inherently limited when they operate in isolation.

In healthcare, value is created across connected steps, from decision-making and handoffs to execution across teams and systems. When task-level optimisations are introduced into fragmented workflows, users are often left to bridge gaps between systems. This increases cognitive load rather than reducing it, limiting both adoption and long-term value.

Agentic AI shifts the focus from task optimisation to workflow orchestration. By coordinating decisions and actions across multiple steps, AI agents help reduce fragmentation and the coordination burden placed on users.

Where agentic AI is already reshaping healthcare workflows

Agentic AI is already reshaping healthcare workflows in areas where coordination across teams and systems is critical, such as care coordination, discharge planning, prior authorisation, and clinical documentation.

In these workflows, agents operate across the process rather than a single step, ensuring information is available at the right time and that next actions are triggered without relying on manual follow-ups (coordination support, not autonomous clinical action).

For example, patient discharge is often delayed not because a patient is clinically unready, but because administrative steps fall out of sync. Test results arrive late, documentation is incomplete, approvals are still pending, or follow-up appointments have not been scheduled across different systems. Clinicians and operations staff are then forced to manually track dependencies, chase updates, and resolve gaps, turning coordination into administrative overhead.

An agentic approach does not remove these steps, but helps align them, monitoring readiness across systems, surfacing what is missing, and prompting the right actions at the right time. This reduces administrative burden and allows healthcare professionals to focus on clinical judgement and patient care.

The architectural reality: Why EMR-centric systems struggle with autonomy

EMRs were designed to solve a specific set of problems: creating a reliable system of record, supporting documentation, billing, and regulatory compliance. In that role, they have become foundational to modern healthcare operations.

However, these systems were not designed to coordinate decisions or actions across workflows. Their architectures are typically monolithic and record-centric, optimised for storing and retrieving data rather than coordinating actions in real time across multiple systems and teams.

This becomes a limiting factor for agentic AI. Autonomous agents depend on flexibility, context, and the ability to act across boundaries. When data models are rigid and integrations tightly coupled, agents are constrained to narrow interactions, slowing down decision-making and reducing their ability to adapt to change.

Autonomy exposes architectural limits that task-level automation can often hide.

Moving beyond the idea of the “perfect EMR”

For decades, healthcare organisations have prioritised highly structured and normalised data. This approach made sense in a world where systems of record needed consistency, auditability, and predictability to support clinical documentation, billing, and compliance.

Agentic systems work with data differently. Instead of relying on perfectly curated datasets, they prioritise access, context, and interpretation. This flexibility comes with trade-offs: while agentic systems are designed to handle heterogeneous and less structured inputs, their outputs are not always fully deterministic, reinforcing the need for human oversight in decision-making.

In practice, when decisions must be made in real time, speed and relevance often matter more than perfect data structures.

Open data platforms as enablers of agentic healthcare ecosystems

Agentic AI depends on the ability to operate across systems, teams, and organisational boundaries. For autonomous agents, access to information is not enough – they must also be able to act on it. Without this freedom, agents are confined to isolated tasks rather than end-to-end workflows.

Open data platforms and interoperable architectures (shared data layers rather than point-to-point integrations) create the conditions agents need to interpret context, trigger actions, and adapt as workflows evolve.

Closed, proprietary systems, often shaped by vendor lock-in and limited interoperability, break this model. When data is locked behind rigid integrations and tightly coupled architectures, AI agents are reduced to narrow, task-level roles. Autonomy becomes constrained, coordination slows down, and the promise of workflow-level impact disappears.

Open ecosystems shift the focus from protecting individual applications to enabling system-wide adaptation. This is less about technology choice and more about architectural intent. The question is whether systems are designed to evolve, or to preserve fixed pipelines in an increasingly dynamic environment.

Interoperability: The missing link in modern healthcare

Connecting systems gives healthcare providers a unified, contextual view of each patient’s data. Learn more about interoperability in this article.

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EMRs are not going away, but they are no longer the centre of the system

EMRs will continue to play a critical role as systems of record, ensuring data integrity, traceability, and compliance. However, they are increasingly unsuited to act as the central layer for decision-making and workflow orchestration in complex, fast-moving environments.

In agentic healthcare ecosystems, decision logic and coordination shift outside the EMR. Stability remains anchored in core systems, while adaptability moves to layers designed for change, creating a new balance between reliability and responsiveness.

What healthcare leaders should focus on today

The goal is not to replace existing EMR platforms, but to recognise their limitations in supporting autonomy and orchestration. Treating agentic AI as an architectural evolution, rather than a system replacement, enables more pragmatic progress.

For healthcare leaders, this means:

• Do not replace the EMR – redefine its role
• Start with workflows where coordination repeatedly breaks down
• Build foundations that support autonomy and interoperability

These foundations matter because they create optionality. By improving data access, reducing tight coupling between systems, and enabling interoperability, organisations can introduce agentic capabilities incrementally, without large-scale disruption or high-risk transformation programmes.

Conclusion: Designing healthcare systems for adaptability, not perfection

Agentic AI signals a shift from application-centric thinking towards adaptive, ecosystem-based healthcare systems. As complexity continues to increase, the ability to coordinate decisions and actions across workflows becomes more valuable than optimising individual tools or datasets.

Healthcare organisations that prioritise adaptability over perfection will be better positioned to evolve alongside AI capabilities. In this emerging landscape, success will depend less on maintaining rigid systems and more on enabling architectures that can learn, respond, and change over time.

Healthcare does not need more tools or better data. It needs systems that can coordinate decisions and actions across workflows, and agentic AI makes that possible.