Summit's data layer maps the entire patient acquisition funnel — from first intent signal through verified fulfillment — across the largest exclusive condition-confirmed cohort in healthcare.
Summit's dataset wasn't aggregated — it was operationally generated. Three healthcare operating entities ran condition-specific patient acquisition campaigns for a decade. Every record traces back to a real patient who raised their hand on an unbranded campaign — not a claim, not a model, not a list purchase.
The result is a patient intent layer — not a list. Every record represents a real, diagnosed patient who self-identified with a chronic condition by completing an unbranded supply, Rx, or coverage intake form, or calling in on a click-to-call campaign. First-party, post-conversion, and consented — captured at the moment of in-market shopping between prescriptions, refills, and coverage decisions.
HCP prescribers linked to T1/T2 patients. National provider identifier matched, specialty-confirmed. Warm prescribers, not cold specialty codes.
Form-completed or click-to-call patient inquiries. Self-identified condition + intent. The new patient acquisition cohort and gold seed for predictive AI.
Form completed → called → clinically confirmed → failed on logistics, not qualification. Real, diagnosed, in-market patients blocked by process.
Form completed → called → clinically qualified → supply fulfilled. Closed-loop fulfillment captured against signed patient intent.
15.3M condition-confirmed inquiries across 12.17M unique patients, 2016–2026 — every one self-identified at the point of in-market shopping. Post-conversion, consented, identity on file. The funnel itself, not a behavioral proxy of it.
USE CASE 01
Hashed email + phone match into any DSP. Every impression hits a patient who already proved condition-specific intent — not a behavioral guess.
USE CASE 02
Feed T3 as a verified seed into predictive audience models, AI training, and lookalike expansion. Post-conversion, condition-confirmed, self-identified — not a behavioral proxy.
USE CASE 03
Match T3 to pharmacy claims for closed-loop attribution from intent to fill. Or suppress T3 from buys you don't want to re-target — never burn CPM on hand-raisers.
Summit patients don't map to one drug category — they map to 5–7 simultaneously. A single record can seed a GLP-1 campaign, a cardiovascular campaign, a hypertension campaign, an antidepressant campaign, and a sleep-disorder campaign at the same time. Real patients on multiple condition pathways — not modeled prevalence.
CARDIO-METABOLIC STACK
Diabetes ∩ Hypertension ∩ Hyperlipidemia ∩ Heart Failure / ACE inhibitors, ARBs, statins, anticoagulants, SGLT2 — five activatable categories per patient.
GLP-1 BRIDGE OVERLAYS
BMI ≥27 ∩ T2D ∩ CVD ∩ OSA / Wegovy, Zepbound, Foundayo cohorts ready July 1, 2026. 58,681 confirmed AFib · 12,906 diabetes+cardiac overlap.
OSA–GLP-1 CROSSOVER
OSA ∩ Obesity ∩ Resistant HTN / Highest-LTV cohort for Zepbound dual-indication. Phone-hash-only segment perfect for CTV + audio activation.
STEP 01
Match Summit's 15.3M record cohort against your campaign's eligibility criteria. Output: a deterministic, condition-specific audience.
STEP 02
Push to your DSP, identity platform, or programmatic stack. Compatible with LiveRamp, Epsilon CORE ID, native DSP graphs, and your clean room.
STEP 03
Match-back to confirmed Rx fulfillment events. Closed-loop attribution from impression to therapy initiation.
Every category uses the same underlying cohort and identity layer.
USE CASE · 01
15.3M condition-confirmed hand-raisers. Reach diagnosed patients before they walk into the doctor's office — upstream of the next claim, not downstream of the last.
USE CASE · 02
T3 = the gold seed for predictive AI and lookalike expansion. Post-conversion, condition-confirmed, identity on file — not a behavioral proxy.
USE CASE · 03
First-party patient records with explicit consent. The cleanest training data in healthcare for in-market intent models.
USE CASE · 04
30,797 patients in active supply 3+ years. Activate patients in the early refill window — before they fall off therapy.
USE CASE · 05
Identify patients on competitor therapies who match your switching profile — comorbidity-aware, payer-aware.
USE CASE · 06
Pull T3 out of impression buys — don't burn CPM on hand-raisers you already converted.
USE CASE · 07
140,216 T3→T1 conversions on file. Match impression to confirmed Rx fill with a single identity graph.
USE CASE · 08
166,464 NPIs linked to confirmed patients — warm prescribers, not cold specialty codes.
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