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 want to predict member risk and cost and decide where to step in
- Teams that want AI to spot high-cost members ahead of time, not only dashboards about the past
- Buyers who will measure savings and the effect of outreach after the models go live
Weak fit
- Small community health centers that only need UDS quality reporting
- Buyers with no analytics or care management staff to act on predictions
- Organizations that will not share claims and clinical data for the models
Bottom line
Certilytics is worth using for health plans and complex provider organizations that want AI to forecast risk and cost across their populations. It suits teams with clean data feeds and care staff who act on the lists. Buyers who mainly need quality and risk adjustment workflows may prefer Cozeva.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Certilytics is a healthcare analytics company in Louisville founded by Martin Jackson and led by President and CEO Merle Ryland.
It builds models that forecast risk and cost for health plans and other large organizations that manage the care of many people. That goes beyond simple lists of care gaps. It is not a reporting tool for community health centers or software for operating room capacity.
Results depend on clean data feeds and care teams that follow the intervention lists the models produce. Pricing is enterprise and not published, which is typical for this kind of analytics.
Among population health tools we review, Certilytics scores in the middle, between Trella and Cozeva, when a health plan needs prediction.
Cozeva fits when quality and risk adjustment workflows are the main purchase. Azara fits quality work at safety-net clinics, and Hexplora fits when a smaller set of dashboards is enough.
The score reflects useful forecasting for plans, with pricing that is not public.
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 |
| Socially Determined | 6.9 | 7.0 |
| Lightbeam Health Solutions | 6.8 | 6.5 |
| Relevant Healthcare | 6.8 | 6.6 |
| Hexplora | 6.7 | 6.5 |
Pricing
| Item | Detail |
|---|---|
| Model | Analytics software and related services for health plans and large healthcare organizations, with prices quoted for each customer. |
| What usually drives cost | The number of people covered, which sets of models you buy, how much data must be connected, and consulting services. |
| What to ask before you buy | The yearly fee for your number of members, and which prediction modules and services are included. |
| Published pricing | Certilytics does not publish prices. Every customer gets a quote. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Certilytics to function | |
| Named analytics and care management leads | Predictions go nowhere without an owner. |
| A plan for which claims and clinical data you will send | The models need data to work. |
| Current cost and risk numbers | You cannot show savings without a starting point. |
| A finished privacy review and business associate agreement | AI at a health plan stalls in security review. |
| Staff ready to reach out to members | Lists that no one acts on waste the model. |
| What will maximize your value | |
| Start with one plan product or region | Launching everywhere on day one multiplies the noise. |
| Measure the effect of outreach at 90 days | This shows whether the predictions add value. |
| Stop using analytics tools that overlap | Duplicate lists confuse teams. |
| Deal-breakers | |
| You only need UDS reporting for a community health center | |
| You cannot share claims data | |
| No one is available to own the analytics | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Security review and data mapping | 4-10 weeks |
| 2 | Test the models | 8-16 weeks |
| 3 | Expand outreach | 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 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.