Patient Visit Average in Physiotherapy: Why It’s a Poor Metric to Track

Patient Visit Average (PVA) is often treated as a proxy for clinician competence and clinical quality. In reality, it mostly measures retention and is heavily distorted by tenure, case mix, and skewed episode distributions. This article explains why PVA is a poor metric for comparing clinicians, why it’s a lag indicator that fails as a coaching tool, and which lead indicators (like time between visit one and two, and the proportion reaching a fourth consult) give clinic owners actionable insight earlier in the patient journey. It also links to a deeper look at patient retention and invites clinic owners into the People Community discussion space.

PVA is a retention signal, not a quality signal — and it’s a lagging metric. Here’s why it fails, and what to track instead.

What Patient Visit Average actually measures

At its core, Patient Visit Average (PVA) measures one thing only: the average number of consultations per episode of care.

It does not tell you:

Whether the initial diagnosis was accurate
Whether loading and progression were appropriate
Whether patients achieved meaningful functional outcomes
Whether discharge was timely, intentional, or successful

PVA is fundamentally a retention signal. Retention can be positive, but retention alone is not a reliable indicator of quality.

Why PVA fails as a clinician comparison metric

One of the most common mistakes in private practice is using PVA to compare clinicians. This is where the metric begins to actively mislead.

PVA is heavily skewed by tenure. Clinicians who have been in a practice longer naturally accumulate long-term patients. New clinicians, by contrast, tend to see:

More acute presentations
More walk-in or word-of-mouth traffic
Conditions with short natural recovery timelines

Comparing these two clinicians using a single average says far more about time in the building than clinical competence.

The distribution problem no one talks about

Most physiotherapy practices do not have neat, symmetrical data.

What they usually have is:

A large number of patients attending one to three sessions
A small number of patients attending long-term (often 20+ visits)

This creates a skewed, often bimodal distribution. In these conditions, averages become unstable and highly sensitive to case mix. Two clinics (or two clinicians) can deliver equally high-quality care and end up with very different PVAs simply due to the types of patients they see.

High PVA does not equal high-quality care

One of the most damaging narratives in physiotherapy is the idea that a high PVA automatically reflects better clinical work.

In reality, high PVA can coexist with:

Over-servicing
Poor progression
Missed or unclear diagnosis
Unintentional patient dependency

PVA rewards continuity, not correctness. A confident, charismatic clinician with weak clinical reasoning can maintain a high PVA just as easily as a highly skilled clinician delivering best-practice care.

PVA is a lag indicator (and a poor coaching tool)

Perhaps the most important limitation of PVA is that it is a lag indicator.

By the time a low PVA becomes visible:

The patients are already gone
The opportunity to intervene has passed
Coaching becomes reactive rather than proactive

This makes PVA a weak tool for developing clinicians. It tells you something went wrong, but far too late to meaningfully correct it.

Why patient retention conversations often go wrong

Many discussions about PVA are proxy conversations about patient retention. But retention itself is complex, context-dependent, and often misunderstood.

If you’re interested in this from the patient’s perspective, you may find it useful to read Patient retention: why patients don’t come back , which explores the behavioural and experiential drivers behind early drop-off.

The key point: retention is shaped early, not late.

Want to pressure-test your clinic metrics?

This is the kind of practical systems discussion we run inside our Profits Coaching Program — real clinic owners and senior clinicians workshopping what to measure, how to coach it, and how to build systems that actually improve outcomes.

Better metrics to watch instead

If the goal is to improve care quality and clinician development, attention needs to shift toward lead indicators that operate earlier in the episode of care.

Two particularly useful metrics are:

Time between the first and second consultation — this often reflects clarity, confidence, and momentum established in the initial assessment.
The proportion of new patients who reach their fourth consultation — indicating whether patients are genuinely entering the core of a rehabilitation process.

These metrics provide actionable insight while there is still time to intervene, support clinicians, and adjust systems.

What high-quality assessment actually looks like

There is no shortcut to assessing clinical quality. High-quality evaluation involves looking at individual cases and asking:

Was the reasoning process clear and defensible?
Was the plan communicated effectively?
Was follow-up timing appropriate?
Was discharge intentional?

These questions cannot be answered by a single average.

Moving beyond blunt metrics

PVA is not inherently bad. It is simply over-used and misapplied. When treated as a descriptive statistic at a system level, it can offer limited insight. When used as a moral or performance benchmark, it obscures far more than it reveals.

The takeaway

Patient Visit Average tells you how long people stayed. It does not tell you whether the care was good.

If we want better outcomes, better clinicians, and more sustainable practices, we need to stop moralising averages and start measuring what actually shapes patient trajectories.