Indication-depth patient insight
Moderate-to-severe plaque psoriasis · 1,184 evidence items
The answer
Injection fear dominates first-dose conversation — but patients who misunderstand early flares are the ones most likely to quit before the biologic has a fair trial.
Injection fear before first biologic dose
78% of corpus · 52 posts
Support materials need normalised injection-site imagery and nurse-led walkthroughs before day one.
Flare mistaken for treatment failure
71% of corpus · high-risk misconception
Week-three worsening is driving discontinuation — expectation-setting belongs in initiation, not the FAQ.
Prior authorization delay stress
58% of corpus · access burden
Coverage friction is an emotional load as much as a logistics problem — bridge programmes should be visible early.
Interactive preview — scroll inside the screen, switch tabs
Ranked unmet needs and HCP signals from public conversation — by indication, every one backed by a verbatim quote.
The difference
Generic listening tool
Volume · sentiment · keywords
Typical social listening output
Top keywords
True. But nothing here tells you what to build, what to say, or where the evidence is.
IndicationIQ
Named unmet needs, evidenced
IndicationIQ — myeloma · patient unmet needs
Bone pain misread as disease progression
Misconception
Fear of self-injection at home
Access barrier
Fatigue not attributed to treatment
Education gap
Insurance delays before treatment starts
Access barrier
“I didn't realise the bone pain was the myeloma — I thought the treatment wasn't working.”
Patient · Reddit r/multiplemyeloma
Who it's for
Ranked unmet needs with quotes your medical and compliance reviewers can audit.
Faster, evidence-backed patient support and launch insight you can put in front of clients.
How it works
Define the condition, therapeutic area, keywords, data sources, and project phase — patient support, HCP launch, or combined.
Gather posts from public open sources such as Reddit, X, and Bluesky using your indication-specific keyword set.
Automated coding surfaces specific challenges in patient language — sized by frequency, with sentiment and author context alongside.
Human reviewers validate insights against verbatim quotes, then export report-ready evidence for programmes and client deliverables.
Two core workflows
Understand what patients and caregivers struggle with in a specific indication — symptoms, fears, access friction, treatment misunderstandings, and support gaps.
Track what clinicians are saying publicly before and during launch — education gaps, messaging opportunities, and therapy-area themes from open sources.
Why IndicationIQ
The unit of work is a therapy area and patient need — not a brand mention tracker.
Volume and sentiment stay available — but the deliverable is named unmet needs, misconceptions, and HCP signals with frequency and proof.
Every insight links to source, date, and verbatim text for regulated review workflows.
Automated extraction with human review before anything reaches a client report.
FAQ
See how IndicationIQ works on your therapy area and use case.
