Clinicast research · US aesthetic-medicine accounts · September 2026
What stood out across 6,831 clinic Reels.
Every Reel is compared with what a Reel of the same age usually gets at its own clinic, so a small clinic's hit counts as much as a big one's. These are the patterns across all of them, and each shows a few real public Reels as examples.
Reels read
6,831
Accounts
192
Usual views at the typical clinic
5,881
Likes per 1K views
13.0
A clinic's usual is what its typical Reel gets by the same age, and the typical clinic is the middle of 192. By clinic size: Under 10K followers, 1,029 · 10K–50K followers, 2,065 · 50K–250K followers, 11,405 · 250K+ followers, 72,456.
Public metrics saved on September 29, 2026. These are patterns worth testing, not proof of what causes reach.
6,831 clinic Reels, labeled.
Pick a label to color the map. Right of the dashed line beat the usual for a Reel of the same age at its own clinic. Each tile opens its Reel on Instagram.
Reels that open on a text card got 0.83× the usual, across 414 Reels from 121 clinics. Reels that open on a treatment got 1.15×, and a silent face got 1.01×.
What held up
Redrawing the clinics kept the result between 0.79× and 0.91×, and it stayed at 0.83× without the most extreme Reel.
What limits it
Only 243 of the 414 Reels had a confident label, though those still sat at 0.8×.
Try this
Swap the opening text card for treatment footage and move the words onto it: "Open on [the treatment step] with '[one short question]' as text over it."
Real Reels from this group
Typical of the group, not its best or worst. Views are set against what a Reel of the same age usually gets at that clinic. Press play to watch one.
@dr.alex.earle64,030 views · 0.8× the usual (82,891)Asks the viewer a question · Surgery · Expert authority
Reels whose first second shows a treatment got 1.15× the usual, across 1,072 Reels from 145 clinics. Openings on a talking face got 0.91×, and text cards got 0.83×.
What held up
Redrawing the clinics kept it between 1.08× and 1.23×, and the newer half of Reels sat at 1.19×.
What limits it
The older half sat at 1.08×, so the lift was smaller in earlier posts.
Try this
Make the very first frame the treatment itself: "Start on [the device or tool at work] while the voiceover says '[what this step does]'."
Real Reels from this group
Typical of the group, not its best or worst. Views are set against what a Reel of the same age usually gets at that clinic. Press play to watch one.
@laradevganmd18,843 views · 1.3× the usual (14,255)Tells a story · Surgery · Expert authority
Reels where the viewer leaves entertained got 1.14× the usual, across 843 Reels from 134 clinics. Reels that leave a next step got 0.97×, and a changed belief got 0.95×.
What held up
Redrawing the clinics kept it between 1.06× and 1.25×, and both halves agreed at 1.15× and 1.13×.
What limits it
Only 468 of the 843 Reels had a confident label, so some labels may be loose.
Try this
Plan one Reel whose only job is to be fun to watch: "The [role] trying to explain [treatment] in [silly time limit]."
Real Reels from this group
Typical of the group, not its best or worst. Views are set against what a Reel of the same age usually gets at that clinic. Press play to watch one.
@jordanharper_np53,087 views · 1.4× the usual (39,104)States a claim · Personal · Story
Reels that lean on numbers or a study got 0.86× the usual, across 225 Reels from 83 clinics. Reels with no evidence got 1.04×, and evidence shown on screen got 1.03×.
Real Reels from this group
@jeaninedownie993 views · 0.7× the usual (1,343)
@tonyyounmd215,185 views · 0.6× the usual (337,755)
@teawithmd25,470 views · 0.9× the usual (29,605)
Reels that give no advice got 1.10× the usual, across 819 Reels from 142 clinics. Reels with one concrete action got 0.99×, and several steps got 0.94×.
Real Reels from this group
@drrealnice35,813 views · 1.4× the usual (25,445)
@drtessmauricio4,049 views · 1.1× the usual (3,831)
@cameo.facial.aesthetics6,073 views · 1.8× the usual (3,365)
Reels that rely on reasoning got 0.92× the usual, across 1,225 Reels from 146 clinics. Evidence shown on screen got 1.03×, and personal experience got 1.01×.
Real Reels from this group
@drwhitneybowe20,296 views · 0.8× the usual (25,908)
@thebudgetdermatologist71,199 views · 0.7× the usual (107,079)
@nursejamiela13,459 views · 0.9× the usual (14,420)
Reels whose caption asks viewers to comment got 1.09× the usual, across 530 Reels from 65 clinics. Captions that ask for nothing got 1×, and captions asking for a link visit got 0.95×.
Real Reels from this group
@drguymassry33,916 views · 1.3× the usual (25,439)
@drdanielbarrett161,789 views · 1.1× the usual (151,464)
@drsamanthaellis422,230 views · 1.7× the usual (255,614)
One more finding
Reels keep collecting views for months
1.6× views at 4–6 months vs. 1–2 weeks · 6,831 Reels · 192 clinics
At the same clinic, Reels four to six months old had 1.6× the views of Reels one to two weeks old.
What held up
Redrawing the clinics at random kept it between 1.5× and 1.8×.
What limits it
This is one snapshot of public numbers, so part of the gap may be that reach was different a few months ago.
Try this
Don't write off a new Reel after its first week. Compare it with Reels posted around the same time.
Median views vs. Reels one to two weeks old at the same clinic
1–2 weeksn=528
1.00×
2–4 weeksn=1037
1.02×
1–2 monthsn=1922
1.26×
2–4 monthsn=2375
1.46×
4–6 monthsn=969
1.61×
The dashed line is a Reel one to two weeks old (1.0×). The findings above compare Reels of the same age, so this gap doesn't tilt them.
The full comparison, not just the winners.
Open any group to see every category, including the weak and small ones.
Topic11 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Personal
406
20,639
1.09×
23.7
The clinic
604
4,044
1.06×
16.3
Skin conditions
336
21,427
1.03×
8.8
Injectables
959
3,552
1.01×
12.1
Body, hair and wellness
341
14,098
1.00×
10.1
Other
452
16,118
1.00×
18.7
Laser and devices
760
3,543
1.00×
12.0
Aging and prevention
138
19,492
0.98×
17.9
Surgery
1,544
16,587
0.97×
13.4
Skincare
1,251
33,478
0.92×
11.3
Unclear
40
10,969
1.36×
9.3
Kind of content8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Comparison
547
23,131
1.09×
11.0
Story
597
15,403
1.06×
17.1
Behind the scenes
589
8,682
1.02×
16.1
Other
1,243
2,812
0.99×
13.7
Myth-busting
406
24,036
0.97×
11.7
Expert authority
1,250
13,047
0.97×
13.0
Teaching
2,017
14,285
0.97×
11.7
Unclear
182
18,150
1.24×
13.0
How the video opens9 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Asks the viewer a question
1,052
13,570
1.02×
12.8
Text on screen, no speech
2,001
6,776
1.00×
11.9
Opens by introducing the doctor
373
2,916
1.00×
20.9
Tells a story
295
13,196
1.00×
14.0
Gives an instruction
376
13,179
1.00×
12.6
Names a mistake
44
27,093
0.99×
8.4
States a claim
2,624
13,531
0.98×
13.2
Shows the result first
16
41,580
0.98×
8.8
Unclear
50
6,947
1.00×
10.0
Hook type8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Social proof
105
9,786
1.07×
12.1
Something unexpected
308
13,667
1.07×
12.7
Talks to the viewer
755
11,592
1.01×
13.2
A curiosity gap
1,866
13,385
1.00×
13.6
A bold claim
850
17,180
0.97×
13.1
A result the viewer wants
285
11,755
0.92×
13.1
A contrarian take
179
34,786
0.88×
16.5
Unclear
2,483
6,140
1.00×
12.3
What keeps people watching7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
A strong opinion
47
70,036
1.42×
11.3
Something people already talk about
144
14,151
1.10×
15.1
A surprising extreme
326
15,097
1.00×
11.5
No clear reason to stay
1,519
7,192
1.00×
16.1
A problem solved at the end
2,456
18,882
0.99×
12.4
Only a doctor would know
440
9,945
0.98×
15.1
Unclear
1,899
5,804
1.00×
11.3
How it is built8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
A reaction
280
43,688
1.13×
15.1
A story
455
14,112
1.05×
16.0
A list
1,009
14,854
0.98×
12.1
Step by step
354
23,395
0.98×
13.8
One opinion, argued
427
19,458
0.98×
16.5
Explains the science
343
13,503
0.97×
12.3
Problem, then fix
1,225
11,804
0.95×
11.9
Unclear
2,738
5,748
1.00×
12.7
Format8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Reaction
263
50,439
1.14×
16.0
Ranking
439
29,764
1.10×
10.9
Before and after
834
8,793
0.99×
10.3
Myth-busting
374
23,814
0.96×
11.2
Teaching
2,365
10,029
0.96×
12.3
How-to
294
41,666
0.92×
11.5
Research
45
14,217
0.75×
11.4
Unclear
2,217
7,031
1.01×
16.4
What the viewer leaves with7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Entertainment
843
16,040
1.14×
17.6
A way to think about it
568
17,033
0.99×
14.2
A rule for deciding
411
25,383
0.98×
13.2
A next step
2,643
9,122
0.97×
13.0
No clear payoff
119
9,165
0.96×
14.8
A changed belief
321
16,507
0.95×
11.6
Unclear
1,926
5,690
1.00×
11.2
Evidence8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
None
236
11,841
1.04×
11.5
Shown on screen
1,570
10,042
1.03×
12.6
A named source
105
9,857
1.01×
19.7
Personal experience
1,106
13,718
1.01×
17.0
What I see in clinic
499
10,154
0.93×
14.1
Reasoning
1,225
19,848
0.92×
12.6
Numbers or a study
225
12,554
0.86×
13.5
Unclear
1,865
5,425
1.00×
11.3
Emotional tone7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Humor
750
14,884
1.13×
14.9
Neutral
152
12,847
1.10×
8.0
Surprise
215
31,878
1.09×
11.5
Concern
954
24,780
0.97×
12.8
Aspiration
2,172
8,721
0.96×
14.4
Reassurance
717
11,719
0.95×
13.3
Unclear
1,871
5,581
1.00×
11.3
How actionable5 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
No advice
819
13,193
1.10×
16.4
One concrete action
2,208
8,864
0.99×
13.2
A general principle
1,056
14,407
0.98×
14.4
Several steps
885
24,998
0.94×
12.3
Unclear
1,863
5,495
1.00×
11.3
Spoken call to action8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Save or share
16
20,596
1.06×
12.9
None
4,216
13,519
1.00×
13.3
Book or message
346
2,662
0.92×
16.1
Several
57
8,660
0.92×
17.9
Visit a link
102
24,263
0.87×
16.5
Comment
167
35,285
0.84×
14.7
Follow
32
11,770
0.81×
11.9
Unclear
1,895
5,495
1.00×
11.4
Who speaks7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Voiceover
681
7,541
1.03×
13.2
Clinic staff
181
2,170
1.03×
16.6
Several people
264
11,553
1.03×
16.8
No speech
1,545
3,846
1.00×
12.1
The doctor
2,937
18,979
0.97×
13.1
A patient
337
8,680
0.97×
12.5
Unclear
886
12,266
1.04×
12.9
First second on screen7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
A treatment
1,072
8,243
1.15×
10.1
Other
1,290
12,092
1.06×
13.2
A face, silent
1,518
14,229
1.01×
11.9
A product or the room
1,098
8,934
0.97×
13.7
A face, talking
1,438
12,543
0.91×
16.2
A text card
414
4,004
0.83×
12.7
Unclear
1
5,733
0.51×
51.8
Small sample
Title text on screen3 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
In the first 3 seconds
6,279
11,272
1.00×
12.9
Later
69
4,981
1.00×
18.9
None
483
7,910
0.97×
16.9
Video length5 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Under 30 sec
3,415
8,146
1.03×
11.4
60 to 89 sec
892
15,826
0.98×
16.4
30 to 59 sec
1,695
12,244
0.93×
12.2
90 sec or more
601
21,342
0.93×
18.9
Unclear
228
14,509
0.95×
9.8
Caption length4 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Short, up to 25 words
2,064
16,498
1.01×
12.0
Long, over 90 words
1,740
12,190
1.00×
15.3
Medium, 26 to 90 words
2,930
7,434
0.99×
12.6
No caption text
97
4,804
0.93×
7.7
Hashtags in the caption3 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
4 or more
3,205
8,037
1.00×
14.0
None
2,664
11,295
1.00×
11.8
1 to 3
962
19,872
0.96×
13.5
A question in the caption2 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Asks a question
1,987
11,432
1.00×
13.6
No question
4,844
10,854
1.00×
12.9
What the caption asks viewers to do6 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Comment
530
72,091
1.09×
11.3
Save or share
92
15,250
1.06×
13.0
Nothing
4,243
14,612
1.00×
12.8
Book or message
1,702
2,270
0.99×
14.4
Several things
121
22,478
0.97×
12.1
Visit a link
143
3,472
0.95×
16.4
Tagging another account2 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
No tag
6,582
10,989
1.00×
13.0
Tags another account
249
10,154
0.97×
14.8
8 starting points for your scripts.
New templates built from these findings. Fill the brackets with something true for your clinic.
01 / TREATMENT FIRST
Open on [treatment] already underway, then [provider role] explains [one detail viewers never see].
No talking or text card before the treatment is on screen.
02 / FRONT DESK MOMENTS
Things [staff role] hears at the front desk that make us smile: [a light, familiar line].
Keep it kind and never about a real client.
03 / PROVIDER REACTS
[Provider role] watches [a popular beauty trend] and gives an honest first reaction.
Film the real reaction, then add one calm sentence of context.
04 / JUST WATCH
No tips today, just [treatment or device] doing its job in [treatment room].
Let the footage run with gentle sound and no voiceover.
05 / BEHIND THE DOOR
What [treatment room] looks like at [time of day] before anyone arrives.
Show the room and the team, not a person being treated.
06 / TEAM PICKS
We asked the team to choose between [option one] and [option two], and [staff role] had strong opinions.
Ask the same question in the caption for comments.
07 / EXPECTATION VERSUS CLINIC
What people imagine [treatment] looks like versus what actually happens in [treatment room].
Keep the joke on the expectation, never on a client.
08 / SOUND ON
The sound of [device or step] up close, with [provider role] naming what it does in a few words.
Open on the close-up sound in the very first second.
Turn this into a two-week test.
Six posts are a pilot, not a verdict. Keep what holds up and change one thing at a time.
01Pick the two strongest patterns to test: opening on a treatment in the first second, and Reels that aim to entertain rather than teach.
02Over two weeks, post at least two Reels of each test type alongside your normal Reels, using the templates above as starting points.
03Keep everything else steady: similar posting times, similar length, and a comment question in the caption for all Reels, test and regular alike.
04Write down every Reel's views when it is exactly 7 days old, test Reels and the rest alike, so each one is compared at the same age.
05At the end, compare each test Reel with your regular Reels at 7 days old, and treat any gap as a lead to test again, not as proof.
Your clinic, read the same way
Get the same breakdown for your clinic.
Enter your Instagram, and your first numbers show up free in about a minute.
How we made this, and what it can't prove
We read the public numbers Instagram shares for Business and Creator accounts, through its official API: views, likes and comments on Reels from the last six months.
A model read each Reel's words and frames, and Jev, a labeling model, labeled the opening, hook, structure, format and topic before any numbers were joined.
Every figure is a median of individual Reels. "Vs. usual" divides a Reel's views by what a typical Reel of the same age gets at its own clinic, so clinics of different sizes count the same.
A new Reel is still collecting views, so each Reel is compared with Reels of its own age, and each clinic's usual is worked out the same way. Without that, whatever a clinic posted lately would look weaker than it is.
A finding needs at least 50 Reels from 15 or more clinics, a range that stays above or below 1.0× when the clinics are drawn again at random, and the same direction when every clinic counts once, so a few busy accounts can't carry it. The differences are modest, so these are patterns to test, not causes.
The example Reels are public Instagram posts, shown with their account, a link and the numbers above. Nothing is quoted from them.
@dr.alex.earle64,030 views · 0.8× the usual (82,891)Asks the viewer a question · Surgery · Expert authority
@skinspirit8,922 views · 0.7× the usual (13,413)Text on screen, no speech · Skincare
@azulmedspa613 views · 0.9× the usual (657)Text on screen, no speech · Injectables
