When AI Meets Care Delivery: Why Workflows Matter

There is no shortage of discussion about what AI could do in healthcare. For many health services, the more immediate question is where it fits into day-to-day care delivery and whether it will make a meaningful difference to how work is actually done.

Documentation, clinical summaries, scheduling, task management and workflow automation are all areas where AI and automation have potential. Introducing AI into one part of a process, however, does not necessarily improve the process as a whole.

Consider clinical documentation. Generating a summary faster may save time at one point in the process. The summary may still need to be reviewed, added to the patient record, shared with another clinician or used to trigger follow-up. If those steps rely on manual work or disconnected systems, improving the first step only gets you so far.

This is why it is useful to start with the workflow rather than the AI capability.

Look at the work around the task

Healthcare workflows rarely happen within a single system or involve a single person.

A scheduling process may need to take into account clinician availability, patient requirements, location, travel time and service priorities. An automatically generated task still needs to reach the right person, with the information they need to act on it.

There is also an important distinction between work that can be automated and work that can be supported by AI. Administrative and repetitive tasks may be relatively straightforward. Clinical decisions require judgement and context, even where AI is helping clinicians access or interpret information.

Looking at the complete workflow can reveal where the real constraint sits.

Sometimes it will be the task itself. Sometimes it will be the information required to complete it. In other cases, the challenge sits between teams, during a handover, or in the effort required to move between systems to keep the process moving.

A workflow automation process may correctly generate a task, but the outcome still depends on whether the right person receives it, has the information they need and can act on it at the right time.

Information flow and system connectivity matter

Healthcare delivery depends on information moving between people, systems and care settings.

A workflow may involve information from clinicians, patients, carers, monitoring devices, contact centres and administrative teams. That information may need to move between hospital and community services or external providers before the next step can happen.

Introducing AI does not remove those dependencies.

If information remains difficult to access, duplicated across systems or isolated within different parts of an organisation, automating one part of the workflow may have limited impact on the wider process.

This is where the underlying digital environment matters. AI needs to work with the systems, information and workflows already supporting care, rather than becoming another separate tool for teams to manage.

This gets harder when care is delivered across settings

These issues are particularly relevant to virtual care, remote patient monitoring, Hospital in the Home and community-based care.

Take an alert from a remote patient monitoring program. Generating the alert is one step. Someone needs to review it alongside other patient information, determine whether action is required, escalate it where appropriate, communicate with the patient and document what happened.

The same applies to Hospital in the Home. Scheduling a home visit is not simply about finding an available appointment. Clinician availability, travel time, patient requirements, service priorities and escalation pathways can all influence how that visit is coordinated.

As care is delivered across more locations, information needs to move with it. Teams need visibility of what has happened, what requires attention, who is responsible for the next step and whether action has occurred.

How CareMonitor is embedding AI into care delivery

CareMonitor is a digital healthcare platform supporting care delivery across hospital, home and community settings.

AI is being embedded within these workflows to support areas including documentation, workflow automation, task management, coordination and access to information.

CareMonitor's AI Care Workflows also include operational agents designed to support defined tasks within existing workflows while maintaining organisational oversight and controls.

This allows AI to support work already moving through the platform rather than requiring teams to manage another standalone tool or disconnected process.

Care teams are rarely looking for more technology to manage. They need support built into the work they are already doing. In practice, AI is often most useful when it becomes part of the workflow itself, helping teams access information, complete tasks and coordinate care without introducing another system to think about.

Start with the process you want to improve

For organisations considering where AI could be useful, mapping the process is a practical place to start.

Where is time currently being spent? Which steps are manual? Where are teams waiting for information? Where does work move from one person to another? Which decisions require clinical judgement? What happens after an automated step is completed?

It is also worth deciding what improvement would look like. Depending on the workflow, that could mean fewer manual steps, less duplication, easier access to information, better coordination or greater visibility of tasks across a team.

Before introducing an AI capability, understand the workflow it will become part of. Look at the information it needs, who needs to act on its output, where clinical judgement remains important and what happens next.

Looking at how AI and automation could support your care delivery workflows?

Contact us to learn about workflow automation, virtual care, remote patient monitoring and care delivery beyond traditional hospital settings.