GLP-1 Continuity Intelligence
Over 65% of GLP-1 patients discontinue treatment within the first year. Billions leak annually through silent abandonment, invisible until claims data confirms it's too late. Preventra gives care coordinators, pharmacists, and population health teams a daily prioritized list of patients to reach before treatment fails.
Built For
"By the time a missed refill shows up in claims data, the opportunity to retain that patient has already disappeared."
The failure was visible weeks earlier, in pharmacy signals your clinical system never saw. Preventra connects those signals early enough to stop the leakage, fitting into your existing workflows without a single system overhaul.
of GLP-1 patients discontinue treatment within the first year
in wasted pharmaceutical spend linked to preventable dropout
predictor of discontinuation is provider and pharmacy refill reliability, not side effects, not medication cost
How Preventra Works
Native HL7/FHIR connectors for Epic, Cerner, and Athena, plus pharmacy claims feeds. Live within days, not months.
Preventra fuses real-time refill data with clinical records to surface dropout risk before it becomes visible in claims data.
Every patient ranked by discontinuation risk. Plain-language reason summaries. Recommended next action, call, pharmacist consult, financial assistance, or routine follow-up.
Track avoidable pharmaceutical spend, intervention ROI, and shared savings impact across the full GLP-1 population.
Patient Risk Worklist
M. Torres
Refill gap · Day 22
High 94%
Call today
D. Nguyen
Provider change · Side effects
High 88%
Pharmacist consult
R. Okafor
Cost pressure · Income gap
Med 71%
Financial assist.
S. Kim
Pharmacy reliability
Med 65%
Pharmacy switch
L. Patel
Stable · On track
Low 18%
Routine follow-up
Preventra Insight
3 high-risk patients share a single pharmacy. Switching to a higher-reliability dispenser reduces projected dropout by 34% in this cohort.
Intelligence Architecture
Monitors care transitions and discharge patterns to identify patients at elevated dropout risk during the highest-vulnerability windows, the first 30 and 90 days of therapy.
Combines pharmacy refill data with clinical signals to predict medication adherence failures before they occur, revealing which patients need outreach today, not after the next claim.
Detects systemic breakdowns in provider and pharmacy reliability, the #1 predictor of GLP-1 failure, and flags population-level disruption before it cascades into widespread dropout.
Why Current Systems Miss It
Claims data lags by 30-60 days
By the time a missed refill appears in your claims feed, the patient has already been off therapy for weeks. The intervention window is gone.
Pharmacy and clinical data never connect
EHRs track clinical visits. PBMs track fills. Neither system sees the full picture. The dropout signal lives in the gap between them.
Care teams respond, they don't anticipate
Without early signals, outreach is triggered by events that have already happened. Care coordinators are managing dropout, not preventing it.
Blanket outreach wastes limited capacity
Without ranked risk visibility, care teams contact everyone, or no one. Resources scatter instead of focusing where they change outcomes.
Preventra closes this gap. By fusing real-time pharmacy refill signals with clinical data, it surfaces dropout risk days or weeks before any existing system, giving care teams a ranked worklist while intervention is still possible.
How It Works In Practice
Preventra detects a combination of pharmacy refill timing, provider reliability signals, and clinical context, 14 to 30 days before a fill is actually missed.
The patient appears on the daily worklist, ranked by dropout probability. The reason is explained in plain language, financial pressure, pharmacy reliability gap, provider change, or side-effect pattern.
The care coordinator, or pharmacist, or population health manager, sees the patient at the top of their list with a recommended action already generated.
A targeted call, financial assistance referral, or pharmacy switch is initiated while the patient is still on therapy. The intervention window is open.
Treatment continues. Shared savings are protected. The intervention cost is a fraction of the pharmaceutical spend that would have been wasted.
Seamless Integration
Preventra is an operational layer on top of your existing EHR, pharmacy, and analytics infrastructure. No new system for IT to manage. No staff retraining. Live within days.
Every adherence risk flag includes ranked clinical and pharmacy drivers with a plain-language explanation. No data science interpretation required.
End-to-end encryption, role-based access controls, and full audit logging, meeting the security requirements of health systems, insurers, and ACOs out of the box.
Preventra also identifies where intervention does not produce measurable return, so care teams allocate capacity to the patients where outreach actually changes outcomes.
Survival Analysis
Preventra's retention analysis reveals not just which cohorts discontinue, but when, and why. Different failure patterns require different intervention strategies, timed to the right moment in each patient's treatment arc.
Loses ~20% of patients in the first 30 days. Driven by provider and pharmacy reliability failures.
Gradual attrition tied to income-relative financial burden. Responds to assistance navigation outreach.
Remarkably consistent across six months. Different behavioral profile; different resource allocation.
Financial Impact
Projected Net Annual Savings
$4.2M
after intervention costs
325
Patients retained
$3.9M
Gross drug spend protected
−$163K
Intervention program cost
24x
Net annual ROI
Estimates based on published GLP-1 adherence literature and Preventra population analytics benchmarks. Results vary by organization.
Frequently Asked Questions
GLP-1 Intelligence is an adherence intelligence layer for GLP-1 therapy. It gives care teams and health plans early visibility into which patients are at risk of discontinuing treatment before they achieve a lasting benefit, so support can reach a patient while it still matters, not after they've already dropped off.
The platform tracks the signals that tend to show up before a patient discontinues, things like refill gaps, appointment attendance, and cost or side-effect friction, and surfaces a clear, explained risk flag for each patient. Every flag comes with a plain-language reason, not just a number.
No. It's a decision-support layer, not a decision-maker. It surfaces which patients may need outreach and why, so your team can prioritize limited time and resources, the clinical and care decisions stay with your team.
Health systems, payers, and care management teams managing a GLP-1 patient population who want earlier visibility into adherence risk, rather than finding out a patient has stopped treatment at their next visit.
No. GLP-1 Intelligence is designed to work with data you're already collecting, pharmacy claims and standard clinical data, without requiring a new data infrastructure project.
Patients who discontinue GLP-1 therapy often see their underlying condition rebound, which can lead to the same complications, and hospital visits, the therapy was meant to prevent. Earlier visibility into who is at risk of dropping off means care teams can intervene before that rebound turns into a hospital visit.
GLP-1 Intelligence generates a daily, risk-ranked patient list for your care coordinators, who needs outreach, why, and what kind of intervention is likely to help. It's designed to work alongside your existing EHR and care management workflow, not replace it.
Clinical workflow integration is typically complete within 60-90 days, though care teams start seeing patient-level output from the first week of data ingestion, so value shows up well before full rollout is finished.
We help you track GLP-1 adherence and dropout rates, along with related outcomes, over time, giving you the kind of longitudinal data useful for quality reporting and program evaluation.
Instead of reviewing a full patient panel to guess who needs a call, you get a prioritized list each morning, highest-risk patients at the top, with a plain-language reason and a suggested next step.
Something like: "High dropout risk. Primary driver: out-of-pocket cost. Recommended action: copay assistance referral." No model jargon, no scores to interpret, just what's going on and what might help.
Yes, always. The flag is an input to your judgment, not a directive. If you know something about a patient that the model doesn't, your judgment takes precedence.
No. Outcomes are logged in the platform with a single click, and that feedback helps improve future predictions, no additional charting beyond your normal workflow.
Knowing the likely driver, cost, side effects, or plateau frustration, before you call means you can start the conversation in the right place. Outreach that feels specific to the patient tends to land better than a generic check-in call.
Every patient who discontinues therapy early represents drug spend without the outcome it was meant to produce, plus the downstream cost of the condition it was managing. Earlier visibility into who's at risk of dropping off means intervention dollars go where they're likely to have the most impact.
GLP-1 Intelligence's adherence and outcomes data is a natural input into population health and quality reporting workflows, helping identify which patients need outreach and supporting the kind of longitudinal tracking value-based arrangements typically require.
Pharmacy claims are the core input, with clinical data used to enrich the model. It's built to work with a standard claims feed, without requiring changes to your existing data infrastructure.
Yes. The platform produces independent adherence and outcomes data that can serve as a neutral measurement layer in outcomes-based arrangements.