Practice intelligence

Seven dimensions, read from the outside in.

We examine what a prospective patient can see and what your team reports, then separate observation from assumption. No practice-management or clinical system access is required.

Start My Practice Check

Methodology by dimension

What we look at, and what we do with it

Every example below is written about a fictional practice. We never present an assumption as a fact.

Discovery

What we look at

Google Business Profile completeness
Review volume and recency
Consistency of practice details across listings
Whether services are visible before the click

Why it matters

New patients often form an impression before they ever reach the website. Listings are cheap to fix and easy to neglect.

Example observation (fictional)

The profile is claimed and accurate, but the most recent review is fourteen months old.

Possible next step

Ask the team to request a review after positive visits for one month, then re-check recency.

Conversion

What we look at

A clear appointment call to action
Online booking or an appointment request
Number of steps required to contact the practice
Mobile experience
Service clarity
Insurance and payment information
Trust signals

Why it matters

Conversion is where practices can quietly lose demand they already paid to create.

Example observation (fictional)

The practice website provides detailed service information, but the primary appointment path is difficult to find on mobile.

Possible next step

Test a more prominent mobile appointment CTA.

Response

What we look at

What happens after hours
How missed calls are recovered
Whether response expectations are stated
Who owns follow-up

Why it matters

An inquiry that arrives when nobody is watching is usually lost silently — it never appears in a report.

Example observation (fictional)

Calls outside office hours reach voicemail, and no one is responsible for returning them the next morning.

Possible next step

Write one rule: who calls back, within what window, and where the attempt is logged.

Patient experience

What we look at

Pre-visit forms
Confirmations and reminders
Pre-visit instructions
How much information is re-collected in the chair

Why it matters

Friction before the first visit shapes whether the patient returns, and it consumes front-desk time either way.

Example observation (fictional)

New-patient forms are completed on paper in the waiting room and then re-typed by staff.

Possible next step

Trial digital pre-visit forms for new patients only and compare check-in time.

Retention

What we look at

Recall process
Reactivation of lapsed patients
Unscheduled treatment follow-up
Review and referral paths

Why it matters

Returning patients are usually the least expensive appointments a practice can fill.

Example observation (fictional)

Recall reminders are generated but nobody works the list when patients do not respond.

Possible next step

Contact the first fifty patients not seen in eighteen months with one clear message and measure replies.

Technology

What we look at

Which categories of system are in use
How many systems staff log into
Where data is re-entered manually
Reported satisfaction and friction

Why it matters

Fragmentation shows up as staff time rather than a line item, so it is often tolerated far longer than it should be.

Example observation (fictional)

Seven systems are in daily use and new-patient details are entered in three of them.

Possible next step

Map one routine task across systems and count the manual hand-offs it requires.

Growth readiness

What we look at

Stated growth priority
Whether conversion is measured
Whether anyone owns the number
Capacity to absorb more patients

Why it matters

A priority without a measurement usually produces activity that cannot be evaluated afterwards.

Example observation (fictional)

The stated priority is more new patients, but inquiry-to-appointment conversion is not tracked.

Possible next step

Instrument the one number that would show whether the priority is improving.