Clinicast research · US vs non-US · September 2026
What works in clinic Reels, US vs non-US.
Every Reel is compared with what a Reel of the same age usually gets at its own clinic, for 192 clinics in the US and 63 outside it. Here is what holds in both groups, what only one shows, and how what they post differs.
Reels read
8,154
Accounts
255
Usual views at the typical clinic
3,873
Likes per 1K views
11.2
Two groups, read the same way.
Each group is read the way a report on that group alone would read it.
US
non-US
Clinics
192
63
Reels compared
6,831
1,323
Usual views at the typical clinic
5,307
1,727
Likes per 1K views
13.0
5.7
Findings of its own
13
7
Usual views by clinic size
Under 10K followers: 1,034 · 50 clinics
10K–50K followers: 2,048 · 30 clinics
50K–250K followers: 10,980 · 50 clinics
250K+ followers: 74,132 · 62 clinics
Under 10K followers: 1,236 · 45 clinics
10K+ followers: 5,874 · 18 clinics
8,154 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 486 Reels from 146 clinics, with 10.8 likes per 1K views. Openings that showed a treatment reached 1.12×, and a talking face reached 0.95×.
US
0.83×414 Reels · 121 clinicsBelow usual
non-US
0.90×72 Reels · 25 clinicsNo clear difference
What held up
Drawing the clinics again gave a range of 0.79× to 0.91×, the older and newer halves sat at 0.82× and 0.84×, and dropping the most extreme Reel left it at 0.83×.
What limits it
Only 302 of the 486 Reels had confident labels, though those still showed 0.82×.
Try this
Put the words in an overlay on moving footage instead of a plain card, for example "[Treatment] from start to finish" over the first frame of the treatment.
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 that open on a treatment got 1.12× the usual across 1,257 Reels from 193 clinics, with 8.6 likes per 1K views. A silent face in the first second sat at 1×, and a text card at 0.83×.
US
1.15×1,072 Reels · 145 clinicsAbove usual
non-US
1.00×185 Reels · 48 clinicsNo clear difference
A clear gap between the groups. US median against non-US: 1.15×. Redrawing each group's clinics kept it between 1.03× and 1.23×.
What held up
Drawing the clinics again gave a range of 1.05× to 1.18×, the 1,005 Reels with confident labels showed 1.12×, and dropping the most extreme Reel left it at 1.12×.
What limits it
The older half sat at 1.07× against 1.14× for the newer half, and likes per 1K views were 8.6, lower than several other openings.
Try this
Start the first frame with the treatment already in motion and say "This is [treatment] on [area], and here is what it feels like."
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 that leave the viewer with entertainment got 1.13× the usual across 931 Reels from 157 clinics, with 16.5 likes per 1K views. Reels ending on a next step sat at 0.99× across 3,361 Reels, and Reels with no clear payoff at 0.95×.
US
1.14×843 Reels · 134 clinicsAbove usual
non-US
1.04×88 Reels · 23 clinicsNo clear difference
What held up
Drawing the clinics again gave a range of 1.04× to 1.21×, the older and newer halves sat at 1.13× and 1.11×, and dropping the most extreme Reel left it at 1.12×.
What limits it
Only 503 Reels had confident labels, though those showed 1.18×.
Try this
Script one Reel whose only job is to be fun to watch, such as "What people think happens during [treatment] versus what really happens."
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 with a humorous tone got 1.11× the usual across 808 Reels from 153 clinics, with 13.8 likes per 1K views. Aspirational Reels sat at 0.97× across 2,738 Reels, while neutral Reels sat at 1.1×.
US
1.13×750 Reels · 134 clinicsAbove usual
non-US
0.97×58 Reels · 19 clinicsNo clear difference
Real Reels from this group
@ellengendlermd41,049 views · 1.0× the usual (39,148)
@griffin_plastic_surgery14,396 views · 1.0× the usual (13,827)
@drrealnice35,813 views · 1.4× the usual (25,445)
Reels that give no advice got 1.1× the usual across 935 Reels from 176 clinics, with 13.7 likes per 1K views. Reels with one concrete action sat at 1× across 2,831 Reels, and Reels with several steps at 0.97×.
US
1.10×819 Reels · 142 clinicsAbove usual
non-US
1.09×116 Reels · 34 clinicsNo clear difference
Real Reels from this group
@dretaibeyar6,646 views · 1.4× the usual (4,843)
@amybirksrn8,638 views · 1.4× the usual (6,387)
@dr_tamirgil7,757 views · 1.7× the usual (4,548)
Reels that ask viewers out loud to visit a link got 0.86× the usual across 111 Reels from 61 clinics, with 15.7 likes per 1K views. Reels with no spoken call to action sat at 1× across 5,063 Reels, and spoken booking or message requests at 0.95×.
US
0.87×102 Reels · 53 clinicsBelow usual
non-US
0.83×9 Reels · 8 clinicsToo few Reels to tell
Real Reels from this group
@drdustinportela14,362 views · 0.9× the usual (16,854)
@musiccityplasticsurgery34,305 views · 0.6× the usual (55,296)
@dermarkologist123,948 views · 0.9× the usual (138,234)
Story Reels got 1.08× the usual across 706 Reels from 169 clinics, with 15.1 likes per 1K views. Comparison Reels sat at 1.09×, while teaching Reels sat at 0.98× across 2,389 Reels.
US
1.06×597 Reels · 133 clinicsNo clear difference
non-US
1.18×109 Reels · 36 clinicsAbove usual
Real Reels from this group
@docmd_tlv3,881 views · 1.3× the usual (2,936)
@epilogic_il1,269 views · 1.1× the usual (1,119)
@draharonamir9,301 views · 1.8× the usual (5,294)
Reels with a surprising tone got 1.09× the usual across 243 Reels from 107 clinics, with 10.3 likes per 1K views. Humorous Reels sat at 1.11×, and aspirational Reels at 0.97×.
US
1.09×215 Reels · 87 clinicsNo clear difference
non-US
1.20×28 Reels · 20 clinicsToo few Reels to tell
Real Reels from this group
@drpimplepopper1,541,956 views · 1.5× the usual (1,038,231)
@elasemedspas8,875 views · 1.1× the usual (8,103)
@cameo.facial.aesthetics4,496 views · 1.8× the usual (2,508)
Reels whose caption asks viewers to comment got 1.1× the usual across 561 Reels from 80 clinics, with 11.2 likes per 1K views. Captions that ask for nothing sat at 1× across 4,982 Reels, and captions that ask for several things at 0.92×.
US
1.09×530 Reels · 65 clinicsAbove usual
non-US
1.16×31 Reels · 15 clinicsToo few Reels to tell
Real Reels from this group
@drguymassry33,916 views · 1.3× the usual (25,439)
@dr_meir_babaev994 views · 1.2× the usual (851)
@derm.talk243,264 views · 1.4× the usual (174,444)
Where one group stands out.
12 findings passed the test inside one group but not across all clinics. Each shows both groups, and says so where the gap between them held up too.
non-US clinics
Before and after: significantly above usual
Reels like this had a median of 1.28× their clinic's usual views, across 124 Reels.
US
0.99×834 Reels · 130 clinicsNo clear difference
non-US
1.28×124 Reels · 42 clinicsAbove usual
A clear gap between the groups. US median against non-US: 0.77×. Redrawing each group's clinics kept it between 0.52× and 0.92×.
Real Reels from this group
@dr.rosen1,193 views · 1.7× the usual (683)
@keshet_refua1,906 views · 2.7× the usual (695)
@aeofficial.il1,259 views · 1.8× the usual (693)
non-US clinics
A list: above usual
Reels like this had a median of 1.13× their clinic's usual views, across 131 Reels.
@dr.molla.clinic1,154 views · 0.8× the usual (1,406)
@drsilfen889 views · 0.7× the usual (1,197)
@dr_zivperetz6,999 views · 1.0× the usual (7,328)
US clinics
Skincare: below usual
Reels like this had a median of 0.92× their clinic's usual views, across 1251 Reels.
US
0.92×1,251 Reels · 117 clinicsBelow usual
non-US
1.00×59 Reels · 23 clinicsNo clear difference
Real Reels from this group
@nbdps1,273 views · 0.8× the usual (1,625)
@drwhitneybowe20,296 views · 0.8× the usual (25,908)
@dermguru451,512 views · 0.9× the usual (486,026)
US clinics
Book or message: below usual
Reels like this had a median of 0.92× their clinic's usual views, across 346 Reels.
US
0.92×346 Reels · 110 clinicsBelow usual
non-US
0.97×236 Reels · 44 clinicsNo clear difference
Real Reels from this group
@winterparkskin323 views · 0.8× the usual (418)
@whitecoataesthetics1,085 views · 0.7× the usual (1,566)
@monarchaestheticmedicine790 views · 0.9× the usual (868)
What they post differently.
The share of a clinic's Reels with each label, averaged over clinics so a busy account counts once. Only gaps of 8 points or more that held up when each group's clinics were drawn again.
Label
US
non-US
Gap
Hashtags in the captionNone
37.2%
68.4%
non-US +31.2 ptsrange 20.2 to 42.1
Hashtags in the caption4 or more
48.6%
23.6%
US +25.0 ptsrange 16.5 to 34.8
TopicInjectables
16.0%
36.8%
non-US +20.9 ptsrange 14.4 to 28.0
FormatTeaching
32.6%
52.6%
non-US +20.0 ptsrange 13.1 to 26.8
How it is builtProblem, then fix
17.5%
35.7%
non-US +18.2 ptsrange 13.6 to 22.9
Video length30 to 59 sec
23.8%
39.9%
non-US +16.1 ptsrange 10.3 to 21.7
What keeps people watchingA problem solved at the end
33.4%
49.2%
non-US +15.7 ptsrange 9.4 to 22.5
Video lengthUnder 30 sec
52.2%
38.6%
US +13.6 ptsrange 5.1 to 20.4
Kind of contentExpert authority
17.0%
30.1%
non-US +13.1 ptsrange 7.0 to 19.0
How the video opensText on screen, no speech
30.8%
18.3%
US +12.5 ptsrange 7.5 to 17.2
One more finding
Reels keep collecting views for months
1.6× views at 4–6 months vs. 1–2 weeks · 8,154 Reels · 255 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 1.7×.
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=534
1.00×
2–4 weeksn=1210
1.01×
1–2 monthsn=2169
1.23×
2–4 monthsn=2863
1.44×
4–6 monthsn=1378
1.59×
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.
By group: US 1.61×, non-US 1.48×.
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
430
17,534
1.08×
21.7
The clinic
697
3,610
1.04×
14.9
Body, hair and wellness
389
10,633
1.03×
9.2
Skin conditions
397
12,022
1.03×
8.4
Other
550
10,697
1.00×
15.3
Injectables
1,469
2,450
1.00×
8.6
Aging and prevention
173
13,632
1.00×
15.2
Surgery
1,771
12,368
0.99×
11.0
Laser and devices
910
3,285
0.98×
10.2
Skincare
1,310
30,253
0.93×
11.1
Unclear
58
5,731
1.14×
6.6
Kind of content8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Comparison
611
15,750
1.09×
9.9
Story
706
11,701
1.08×
15.1
Behind the scenes
627
7,805
1.02×
15.0
Other
1,375
2,731
0.99×
12.6
Expert authority
1,730
5,675
0.98×
9.7
Teaching
2,389
9,858
0.98×
10.5
Myth-busting
501
13,697
0.97×
10.0
Unclear
215
12,062
1.19×
10.1
How the video opens9 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Asks the viewer a question
1,368
8,135
1.03×
9.7
Shows the result first
19
18,605
1.03×
8.7
Gives an instruction
427
10,688
1.01×
11.4
Text on screen, no speech
2,254
4,987
1.00×
10.7
Tells a story
364
10,127
1.00×
12.4
Opens by introducing the doctor
424
2,783
0.99×
19.3
Names a mistake
51
21,123
0.99×
8.2
States a claim
3,188
8,543
0.99×
11.1
Unclear
59
4,548
1.00×
10.2
Hook type8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Something unexpected
350
11,387
1.05×
12.1
Social proof
134
7,620
1.04×
10.5
Talks to the viewer
936
7,304
1.02×
11.3
A curiosity gap
2,302
9,177
1.00×
11.0
A bold claim
1,039
9,858
0.97×
11.4
A result the viewer wants
399
5,480
0.95×
9.8
A contrarian take
225
19,233
0.91×
12.9
Unclear
2,769
4,901
1.00×
11.1
What keeps people watching7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
A strong opinion
54
45,139
1.35×
9.7
Something people already talk about
157
12,067
1.14×
13.4
A surprising extreme
383
10,352
1.02×
10.3
A problem solved at the end
3,113
10,890
1.00×
10.5
No clear reason to stay
1,719
5,688
1.00×
14.6
Only a doctor would know
633
4,617
0.99×
11.0
Unclear
2,095
4,697
1.00×
10.4
How it is built8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
A reaction
305
36,440
1.11×
13.8
A story
533
11,807
1.07×
14.0
Step by step
425
13,715
1.00×
11.9
A list
1,140
12,033
1.00×
11.0
Explains the science
432
8,765
1.00×
10.2
One opinion, argued
520
12,185
0.98×
14.1
Problem, then fix
1,718
5,306
0.98×
9.3
Unclear
3,081
4,616
1.00×
11.2
Format8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Reaction
278
45,993
1.12×
14.6
Ranking
459
25,310
1.10×
10.6
Before and after
958
7,071
1.00×
9.2
Teaching
3,090
5,456
0.97×
9.8
Myth-busting
467
13,459
0.97×
9.3
How-to
306
37,975
0.92×
11.4
Research
49
13,083
0.86×
11.4
Unclear
2,547
5,659
1.01×
14.3
What the viewer leaves with7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Entertainment
931
14,035
1.13×
16.5
A way to think about it
668
12,816
1.00×
12.2
A next step
3,361
5,480
0.99×
10.9
A rule for deciding
504
13,516
0.98×
10.3
A changed belief
393
10,661
0.97×
9.6
No clear payoff
158
5,435
0.95×
12.0
Unclear
2,139
4,548
1.00×
10.2
Evidence8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
None
271
10,661
1.05×
10.2
Shown on screen
1,969
7,199
1.03×
10.3
Personal experience
1,280
10,240
1.02×
15.2
A named source
137
5,086
1.00×
13.1
What I see in clinic
609
6,416
0.96×
11.5
Reasoning
1,575
10,126
0.95×
10.7
Numbers or a study
262
10,340
0.92×
11.7
Unclear
2,051
4,465
1.00×
10.5
Emotional tone7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Humor
808
12,830
1.11×
13.8
Neutral
184
10,021
1.10×
7.4
Surprise
243
21,881
1.09×
10.3
Concern
1,181
14,662
0.99×
11.0
Reassurance
942
6,599
0.99×
10.5
Aspiration
2,738
5,707
0.97×
11.7
Unclear
2,058
4,518
1.00×
10.5
How actionable5 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
No advice
935
10,841
1.10×
13.7
One concrete action
2,831
5,424
1.00×
11.0
A general principle
1,332
9,419
0.98×
11.3
Several steps
1,007
17,979
0.97×
11.3
Unclear
2,049
4,479
1.00×
10.4
Spoken call to action8 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Save or share
18
15,112
1.08×
11.4
None
5,063
9,428
1.00×
11.4
Several
63
4,843
0.97×
16.2
Book or message
582
1,880
0.95×
10.1
Comment
192
28,401
0.93×
13.2
Visit a link
111
19,571
0.86×
15.7
Follow
42
9,946
0.83×
10.4
Unclear
2,083
4,524
1.00×
10.5
Who speaks7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Several people
342
7,255
1.06×
12.9
Clinic staff
197
2,170
1.03×
16.4
Voiceover
845
5,380
1.02×
11.0
A patient
442
6,358
1.00×
10.2
No speech
1,725
3,334
0.99×
10.7
The doctor
3,628
11,432
0.98×
11.0
Unclear
975
10,581
1.04×
11.7
First second on screen7 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
A treatment
1,257
5,919
1.12×
8.6
Other
1,414
10,501
1.04×
11.9
A face, silent
1,845
9,122
1.00×
10.1
A product or the room
1,337
5,489
0.98×
11.8
A face, talking
1,813
7,479
0.95×
13.1
A text card
486
3,682
0.83×
10.8
Unclear
2
4,440
0.52×
28.1
Small sample
Title text on screen3 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Later
93
3,383
1.01×
11.7
In the first 3 seconds
7,452
7,647
1.00×
11.0
None
609
5,510
1.00×
12.5
Video length5 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Under 30 sec
3,866
6,284
1.03×
10.3
60 to 89 sec
1,081
9,368
1.00×
14.1
30 to 59 sec
2,270
6,601
0.96×
9.8
90 sec or more
669
16,673
0.95×
17.5
Unclear
268
8,859
0.97×
8.7
Caption length4 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Short, up to 25 words
2,416
12,374
1.01×
10.1
Long, over 90 words
2,089
6,518
1.00×
12.8
Medium, 26 to 90 words
3,517
5,003
0.99×
11.0
No caption text
132
3,095
0.90×
7.5
Hashtags in the caption3 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
4 or more
3,547
6,100
1.00×
12.9
None
3,500
6,240
1.00×
9.5
1 to 3
1,107
15,341
0.97×
11.1
A question in the caption2 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Asks a question
2,521
5,946
1.00×
11.1
No question
5,633
8,037
1.00×
11.2
What the caption asks viewers to do6 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
Comment
561
62,157
1.10×
11.2
Save or share
96
13,933
1.03×
12.8
Nothing
4,982
10,987
1.00×
10.9
Book or message
2,199
2,040
1.00×
11.6
Visit a link
149
3,383
0.95×
16.2
Several things
167
4,052
0.92×
8.8
Tagging another account2 groups
Group
Reels
Median views
Vs. usual
Likes per 1K
Note
No tag
7,905
7,316
1.00×
11.1
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 / OPEN ON TREATMENT
Watch [treatment] on [area] from the first touch to the last, no talking until the end.
Have hands and tools already moving in the first frame.
02 / EXPECTATION VERSUS REALITY
What people picture before booking [treatment], and what the room really looks like.
Cut quickly between the two versions.
03 / STAFF ROOM MOMENT
Things our [role] says every time someone asks about [treatment].
Keep it light and true to how the team talks.
04 / THE WRONG GUESS
Ask anyone how [treatment] works and they say [common guess]. Here is the part they miss.
Show the surprising part on screen, not on a card.
05 / FIRST TIME STORY
The first week I did [treatment], I learned [lesson] the hard way.
Tell it as a short story with a clear ending.
06 / SOUND UP
Turn the sound up for what [device] sounds like during [treatment].
Let the sound carry the opening seconds.
07 / A DAY IN BRIEF
A full day at [clinic name] in the time it takes to [everyday task].
No tips, just the day as it happens.
08 / PICK ONE
[Option A] or [option B]: which would you choose?
Use this as the caption question, not a spoken line.
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 a Reel that leaves the viewer entertained.
02Over two weeks, post at least two Reels that open on a treatment and two that aim only to entertain, and keep the rest of the schedule as normal.
03Write down every Reel's views when it is 7 days old, test Reels and the rest alike, so they are compared at the same age.
04At the end, compare the 7-day views of each test group with the 7-day views of the other Reels posted in the same two weeks.
05Keep the pattern that sat above the other Reels for another round, and treat one round as a hint to test again, not proof.
Your clinic, read the same way
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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. Each group's Reels are matched by age on their own.
A finding, across all clinics or inside one group, 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. "Too few Reels to tell" means a group falls short of that.
A gap between the groups is clear when the two medians differ by 8% or more, stay apart when each group's clinics are drawn again at random, and keep their order when every clinic counts once. With this many labels compared, one clear gap may still be chance.
There are 192 US clinics and 63 non-US clinics, so the smaller group can only show bigger patterns. 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.
Public metrics saved on September 30, 2026. These are patterns worth testing, not proof of what causes reach.
US vs non-US clinic Reels, September 2026 | Clinicast
@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
