AI triage is rapidly emerging as a critical response to growing pressure across healthcare systems, where demand for physiotherapy continues to rise faster than workforce capacity.

Musculoskeletal (MSK) conditions account for a substantial proportion of primary care consultations and remain a leading cause of disability and lost productivity worldwide.

Despite strong evidence that early physiotherapy intervention improves outcomes, access remains inconsistent, with waiting times often stretching into weeks or even months.

This widening gap between demand and capacity has made triage one of the most critical and constrained points in the rehabilitation pathway.

AI is now reshaping this front line, not by replacing clinical judgement, but by enabling triage to become faster, fairer, and more consistent at scale.

From Manual Bottlenecks to Intelligent Entry Points

Traditionally, physiotherapy triage has relied on clinician availability and manual review of referrals. While clinically sound, this approach struggles with volume.

Decisions are often delayed, prioritisation can vary between services, and patients frequently wait without guidance while their condition deteriorates.

AI-enabled triage introduces a different model. By analysing structured patient-reported information, symptom patterns, and risk indicators, AI systems can support early routing decisions before a patient ever reaches a clinician’s list.

This does not remove professional oversight; instead, it ensures that clinicians engage at the right point in the pathway, with clearer context and better prioritisation.

For healthcare operators and insurers, the value lies in consistency. Entry into rehabilitation becomes less dependent on local capacity or subjective interpretation and more aligned with evidence-informed logic applied uniformly across populations.

What the Evidence Is Beginning to Show

The evidence base for AI triage in physiotherapy is still maturing, but early signals are instructive.

Clinical outcomes from AI-supported pathways appear broadly comparable to traditional models, indicating that digital triage can be implemented safely without compromising effectiveness.

More importantly, operational outcomes are already clear. Services using AI-assisted assessment and routing have reported significant reductions in waiting times, improved caseload balance, and more efficient use of clinical expertise.

This distinction matters. AI triage has not yet proven superior clinical outcomes in isolation – but it has demonstrated its ability to unlock access, which is often the limiting factor in rehabilitation success.

Smarter Triage as a System Capability

The most significant impact of AI triage emerges when it is treated not as a standalone tool, but as a system capability.

When digital triage is embedded into referral pathways, it becomes a form of demand management. Low-risk patients can receive early guidance or self-management support, while higher-risk or complex cases are escalated more quickly. Clinicians are no longer overwhelmed by volume, and patients are no longer left waiting without direction.

This model is now extending beyond MSK care. Similar approaches are being applied across neurological, pulmonary, pelvic health, frailty, and community rehabilitation pathways, indicating that AI-supported triage is becoming foundational to multidisciplinary rehabilitation services.

At scale, aggregated triage data also provides insight into population need, enabling better capacity planning, workforce deployment, and service design – benefits that extend well beyond individual patient journeys.

Safety, Governance, and Trust

As with any AI-enabled healthcare process, safety and governance are non-negotiable.

Triage decisions influence access to care and must therefore be transparent, auditable, and subject to clinical oversight.

Regulatory frameworks and medical device standards play a critical role, but governance does not end at compliance. Services must be confident that triage algorithms are fair, regularly evaluated, and responsive to emerging evidence.

The World Health Organization has repeatedly emphasised that healthcare AI must remain under human supervision, particularly in processes that affect prioritisation and access.

In physiotherapy, this means AI should accelerate decision-making without removing accountability from clinicians or organisations.

Where the Risks Remain

Despite its promise, AI triage is not a shortcut around structural challenges.

Poor integration with electronic records, inconsistent digital maturity across services, and over-reliance on automation can undermine potential gains.

AI systems trained on narrow datasets may also perform poorly when deployed across diverse populations, reinforcing inequities rather than reducing them.

Perhaps most importantly, triage must not become a gatekeeping mechanism divorced from care. Its purpose is to improve access and flow, not to delay or deflect patients away from appropriate rehabilitation.

Rehbox and the Infrastructure Approach to Triage

Rehbox approaches AI triage as part of a broader digital rehabilitation infrastructure.

Rather than positioning triage as an isolated feature, Rehbox Connect is designed to support intelligent prioritisation, caseload visibility, and clinician oversight within a single system.

The emphasis is on supporting physiotherapists to make better decisions earlier, while maintaining transparency, safety, and professional control.

By integrating triage with assessment, monitoring, and reporting, AI becomes a connective layer across the rehabilitation journey not a black box at the front door.

Looking Ahead: Triage as the Digital Front Door

AI-enabled triage is set to become a standard component of physiotherapy and rehabilitation delivery.

As digital health platforms mature, triage systems will increasingly integrate with national health apps, employer health pathways, and insurer-led programmes.

Patients will complete intelligent self-assessments, clinicians will receive structured insights instantly, and referrals will be prioritised more equitably.

In this future, AI handles initial screening and routine decision support. Physiotherapists focus on complex reasoning, therapeutic relationships, and hands-on care.

Data no longer simply records activity it informs planning, improves access, and elevates outcomes.

Smarter triage is not about replacing clinicians. It is about ensuring that every patient reaches the right care, at the right time, through systems designed to scale.