Rare Disease · Predictive Physician Intelligence
47% of your target physicians will miss the diagnosis.
We know which ones.

Axiaris predicts diagnostic readiness per physician — before the MSL call, before claims data exists. The first forward-looking intelligence in rare disease.

Axiaris · Diagnostic Readiness · HAE
● LIVE
Physicians profiled
High readiness
Will miss diagnosis
Your next two MSL visits — who should you prioritize?
Physician A
Allergist · Boston · 12yr
WILL DIAGNOSE
91
83% prediction of correct diagnosis within 90 days
Node 1 ✓Node 2 ✓Node 3 ✗
Physician C
Allergist · Providence · 15yr
WILL MISS
29
14% prediction — fails at initial suspicion
Node 1 ✗Node 2 ✗Node 3 ✓
Your MSL's time is limited. Axiaris tells you where it matters most — before the visit, not after.
847K+
Physician decisions recorded
30+
Programs completed
50–74%
Completion rate vs 15–20% industry average
7
Rare disease specialties covered
Patent-pending
Proprietary simulation methodology
The Intelligence Gap
01 / 05

The HCP data industry is looking
in the rearview mirror.

Existing HCP Data Vendors
Claims data — prescribed 90 days ago
Prescription history — behavior already happened
Survey intent — what they say, not what they do
No diagnostic confidence signal
Same dataset available to every competitor
vs
Axiaris
Diagnostic prediction — will this physician suspect the disease
Behavioral decision data — measured under pressure
Confidence calibration — overconfidence vs uncertainty
Node-level failure map — where and why they miss
Proprietary — zero competitor access
$200B+
Global pharma commercial operations
$15–20B
HCP intelligence market · all players backward-looking
$0
Forward-looking diagnostic behavioral prediction in rare disease today
The Platform
02 / 05

The diagnostic decision is the
real commercial bottleneck.

Sample Platform Output · HAE Program
"74% probability this physician will correctly identify and refer a rare disease patient within 90 days — before the MSL call, before the prescription, before claims data exists."
NODE 01
Diagnostic Suspicion
Will this physician suspect rare disease at first presentation?
Axiaris measures this
★ Highest value signal
NODE 02
Diagnostic Workup
Correct confirmatory tests in the right sequence?
Axiaris measures this
NODE 03
Treatment Decision
Correct therapy initiated at the right moment?
Axiaris measures this
MODULE 01
Diagnostic Readiness Score
0–100 per physician. Decision accuracy + confidence calibration + time-pressure performance.
Proprietary · Patent-pending
MODULE 02
Diagnostic Pathway Failure Analysis
Cohort map: where physicians fail, by node, specialty, geography. No individual opt-in required.
Cohort deliverable
MODULE 03
AI Physician Certification
First AI-scored diagnostic readiness certification in rare disease. Performance-based, not attendance.
Commercial data signal
Intelligence Tracks
03 / 05

The same data asset.
Two intelligence tracks.

Commercial Intelligence
Educational Intelligence
Axiaris · Diagnostic Readiness Intelligence
● PREDICTIVE
HAE
Fabry Disease
NF1
HAE · Northeast US · 47 physicians. Node 1 failure: 47%.
IDSpecialtyScorePredictionTier
+
PHY-0041
Boston · 12yr
Allergist9183%HIGH
Node 1 · Suspicion
HIGH22s
Node 2 · Workup
HIGH34s
Node 3 · TreatmentProphylaxis timing
HIGH9s ⚠
⚠ Overconfidence · Node 3
Profile
Gap:Overconfidence · Node 3
Cohort:34% of allergists
+
PHY-0017
Hartford · 8yr
Immunologist8776%HIGH
Node 1
HIGH18s
Node 2Minor hesitation
MED41s
Node 3
MED52s
Profile
Gap:None significant
Flag:Node 2 hesitation
+
PHY-0089
New Haven · 6yr
Hematologist2914%LOW
Node 1 · Suspicion
LOW112s
Node 2 · Workup
LOW98s
Node 3 · Treatment
LOW77s
⚠ Recognition gaps · Nodes 1+2
Profile
Gap:Recognition · Nodes 1+2
Treatment:Intact
Anonymized · Individual profiles on opt-in · Cohort data: no opt-in required
Fabry Disease · DE + UK · 2319 neurologists. Node 1 failure: 72% Germany vs 38% UK.
IDSpecialtyScorePredictionTier
+
NEU-0312
Munich · 18yr
Neurologist9488%HIGH
Node 1Atypical Fabry
HIGH24s
Node 2 · Enzyme testing
HIGH28s
Node 3 · ERT initiation
HIGH31s
Profile
Cohort rank:Top 6%
Gap:None
+
NEU-0187
Hamburg · 9yr
Neurologist5138%MEDIUM
Node 1Missed atypical
MED89s
Node 2
LOW62s
Node 3
LOW54s
⟳ Matches 72% German cohort pattern
Profile
Gap:Node 1 · Recognition
Cohort:72% German neurologists
Geographic cohort patterns · no opt-in required
NF1 · 5 countries · 94 physicians. 71% show high confidence, low accuracy at MEK inhibitor decision.
IDSpecialtyScorePredictionTier
+
ONC-0044
Paris · 11yr
Ped. Oncologist8881%HIGH
Node 1 · NF1 diagnosis
HIGH19s
Node 2 · MEK eligibility
HIGH33s
Node 3 · Treatment timing
MED48s
Profile
Gap:None significant
+
ONC-0071
Madrid · 7yr
Neurologist4431%MEDIUM
Node 1
HIGH21s
Node 2 · MEK eligibility
HIGH11s ⚠
Node 3 · Treatment timing
HIGH8s ⚠
⚠ 71% NF1 cohort overconfidence pattern
Profile
Gap:Overconfidence · Nodes 2+3
Cohort:71% of NF1 physicians
71% overconfidence rate is cohort data · no opt-in required
The behavioral layer your
CRM doesn't have.

Readiness Tier per HCP delivered directly into your commercial workflow. Enriches next-best-action — does not replace it.

CRM Integration
Veeva / Salesforce compatible — behavioral attribute per HCP
DPFA for MSL territory planning · no opt-in required
Individual profiles via explicit opt-in consent
Enriches next-best-action inputs — does not replace them
📊
Cohort Knowledge Gap Report
Medical Affairs · No opt-in required
Where physicians fail by node, specialty, geography — MSL briefing and speaker program design
Pre vs post accuracy — quantifies the gap your education is designed to close
Confidence Calibration Report — identifies overconfident physicians before a patient is missed
Cohort data · aggregate · no individual opt-in
🏆
Physician Diagnostic Certification
Medical Olympics · Performance-based
First AI-scored diagnostic certification in rare disease — performance, not attendance
Medical Affairs: identify certified diagnosticians for advisory boards and speaker programs
Annual recertification creates a longitudinal behavioral dataset per physician
Individual data · explicit opt-in consent
Same simulation. Two tracks. Commercial Intelligence and Educational Intelligence are generated from the same physician behavioral data — governed by the same consent architecture.
The Asset
04 / 05

The training data doesn't exist anywhere else.
It took 30 programs to build.

PREDICTION ACCURACY · PROGRAM 1 → 30COMPOUNDING ↑
Accuracy improves with every program run
Decisions in training set
Physician profiles
Disease pathways mapped
Years to replicate baseline
Forward-looking. Proprietary.
Cannot be purchased.

847,000 physician decisions across 30+ programs. A competitor starting today has zero training data.

Prescription Analytics
What was prescribed last quarter
Backward-looking
HCP Contact Platforms
Who they are, how to reach them
Backward-looking
Axiaris
What they will decide next
Forward-looking
Evidence
05 / 05

0 neurologists. 5 countries.

0%
Relative improvement
Diagnostic suspicion
0%
↑ 158%
Diagnostic suspicion
12% → 31%
0%
↑ 88%
Enzyme testing ordered
17% → 32%
0%
Completion rate
vs 15–20% industry avg
Contact

Diagnostic readiness map
for your disease area.

Which physicians are missing the diagnosis · Where · Why

intelligence@axiarisdata.com →