Population health & analytics · Predictive analytics for plans
Certilytics
Certilytics builds predictive analytics for health plans and large healthcare organizations managing population risk and cost. It is predictive analytics software, not an FQHC UDS reporter.
Strong fit
- Health plans and large provider organizations that need predictive risk, cost, and intervention analytics on member populations
- Teams that want AI models for anticipating high-cost members rather than only retrospective dashboards
- Buyers measuring savings and intervention lift after the models go live
Weak fit
- Small FQHCs that only need UDS quality reporting
- Buyers without analytics or care-management owners to act on predictions
- Organizations refusing to share claims and clinical data for modeling
Bottom line
Certilytics earns a Recommend for plans and complex provider organizations that buy predictive population analytics rather than only care-gap worklists. Led by President and CEO Merle Ryland and founded by Martin Jackson in Louisville, the company sells AI-enabled risk and cost forecasting with third-party revenue estimates roughly in the $25-100M band - above our $5-50M prefer range and under the hard cap. Outcomes depend on clean data feeds and care teams that follow the intervention lists. Score sits mid-board between Trella and Cozeva when predictive plan analytics is the buying problem.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Certilytics is a Louisville healthcare analytics company that builds predictive models for health plans and other large organizations managing population risk and cost.
It is predictive analytics software, not an FQHC UDS reporter and not OR capacity software. Compare Cozeva when quality and risk-adjustment workflows are the core buy, Azara when safety-net quality is the job, and Hexplora when a smaller mid-market dashboard suite is enough.
Score reflects useful plan-side predictive depth with enterprise pricing opacity typical of this aisle.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Azara Healthcare | 7.6 | 7.2 |
| Cozeva | 7.5 | 7.2 |
| Persivia | 7.4 | 7.1 |
| Syra Health | 7.4 | 7.1 |
| Certilytics | 7.3 | 7.0 |
| Trella Health | 7.3 | 7.0 |
| IMAT Solutions | 7.0 | 6.7 |
| Elligint Health | 6.9 | 6.6 |
| Olio | 6.9 | 6.7 |
| Lightbeam Health Solutions | 6.8 | 6.5 |
| Relevant Healthcare | 6.8 | 6.6 |
| Hexplora | 6.7 | 6.5 |
Pricing
| Item | Detail |
|---|---|
| Model | Enterprise analytics SaaS and related services for health plans and large healthcare organizations; quote-based. |
| What usually drives cost | Covered lives, model packs, data integration scope, and professional services. |
| What to ask in diligence | Annual platform fee at your membership size, with which predictive modules and services are included. |
| Published pricing | No public list price; enterprise quotes only. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Certilytics to function | |
| Analytics and care-management owners named | Predictions die without owners. |
| Claims and clinical feeds scoped | Models need data. |
| Baseline cost and risk metrics | You cannot prove savings without a baseline. |
| Privacy and BAA review complete | Plan AI stalls in security. |
| Intervention capacity ready | Lists without outreach waste the model. |
| What will maximize your value | |
| Pilot one product or region first | Enterprise day one multiplies noise. |
| Measure intervention lift at 90 days | Prove predictive value. |
| Retire overlapping analytics tools | Duplicate queues confuse teams. |
| Deal-breakers | |
| Only need FQHC UDS reporting | |
| No claims sharing allowed | |
| No analytics owners available | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Security and data mapping | 4-10 weeks |
| 2 | Model pilot | 8-16 weeks |
| 3 | Scale interventions | 4-8 weeks |
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category actually buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.