Intervention-response intelligence for value-based care

Know who will benefit before you intervene.

Care Compass goes beyond predicting risk. It identifies members likely to respond, members unlikely to respond, and members who may experience an unfavorable outcome—then reveals the clinical, care-delivery, and community barriers shaping that response.

See how it works Request a demo
130+ intervention–outcome analyses across complex populations
Member-level estimates of who is likely to benefit—and by how much
Auditable findings tested for robustness before operational use
Member Decision point Avoided event Untreated path
The problem

High risk does not mean high benefit.

Two members can have the same predicted risk and respond very differently to the same intervention. Treating them as interchangeable wastes scarce capacity and can obscure the members who could benefit most.

What a typical risk score gives you

  • ✕ Members ranked by predicted utilization or cost
  • ✕ Broad enrollment into the same program
  • ✕ Correlations presented as explanations
  • ✕ Program impact averaged across the population

What care teams actually need to know

  • ✓ Who is likely to benefit from a defined intervention
  • ✓ Who is unlikely to respond—and which barriers may explain why
  • ✓ Who shows an unfavorable estimated response and needs clinical review
  • ✓ How to allocate limited resources for the greatest expected impact
RiskWho is likely to experience the outcome?
ResponseWhose outcome is likely to change with intervention?
ActionWhere should limited care-management capacity go first?
How Care Compass works

From population data to an operational intervention strategy

Care Compass analyzes longitudinal claims, pharmacy, available clinical data, and community context, then delivers population, cohort, and member-level intelligence inside existing workflows.

1

Define the decision

Evaluate a specific intervention against a specific outcome within clearly separated baseline, intervention, and outcome periods.

2

Estimate differential response

Measure the average population effect and the estimated effect for each member—not simply the probability of an event.

3

Explain the pathways

Connect response patterns to care fragmentation, medication burden, adherence, access, and community barriers.

4

Allocate and measure

Prioritize members under real capacity and budget constraints, estimate avoided events and savings, and export the target population.

Risk identifies who may need help. Care Compass identifies whose outcome may change because of it—and where limited resources can make the greatest difference.
From population risk to evidence-guided action
Care Compass flags this turn Redirected outcome
Why "high risk" isn't the same as "will respond"

The difference between predicting an outcome and changing one

Care Compass estimates how a defined intervention may change an outcome across the population and for individual members, while making uncertainty and validation visible.

Responder intelligence

Find the members with the greatest estimated benefit

Members are stratified by their estimated response to a defined intervention, allowing care teams to prioritize benefit rather than risk alone.

Non-responder intelligence

Reveal when the current intervention is not enough

Care Compass identifies members with little estimated benefit and surfaces the clinical, care-delivery, and structural barriers associated with non-response.

Unfavorable response

Escalate members who may require a different strategy

An unfavorable estimated effect is not treated as a recommendation. It is a signal for clinical review, alternative support, or a different intervention pathway.

Resource intelligence

Optimize decisions under real-world constraints

Resource-allocation modeling helps leaders determine which eligible members to prioritize when staffing, program capacity, and budgets cannot cover everyone.

Under the hood

Designed to earn trust before guiding action

Care Compass combines transparent causal analysis, member-level response estimation, pathway intelligence, and independent robustness testing so decision-makers can examine the evidence behind every result.

  • Clear temporal design

    Baseline, intervention, and outcome periods are explicitly separated to preserve what happened before and after the intervention and reduce inappropriate use of post-intervention information.

  • Population and member-level effects

    The platform estimates both the average effect across a population and how that effect varies across members, supporting responder, non-responder, and unfavorable-response classification.

  • High-dimensional healthcare context

    Claims, pharmacy, utilization, clinical variables when available, and social or environmental context are evaluated together while distinguishing effect modifiers from additional confounders.

  • Auditability and robustness

    Each analysis can be checked for identification, overlap, assumptions, direction agreement, sensitivity, placebo behavior, and subset stability, with a date-stamped validation record.

Members Similar members Similar members honest split — one member per pair receives the action Received the action No action taken the gap between them = the individualized effect

A simplified view of how Care Compass isolates the true effect of an action for members who look alike on paper.

What's inside

One intelligence layer—from population strategy to member action

Care Compass brings causal findings, pathways, community context, longitudinal patient stories, and resource decisions into one embeddable experience.

Intervention-response engine

Evaluates defined interventions and outcomes across a library of 130+ analyses, producing population effects, member-level estimates, confidence measures, and response classifications.

Patient Story

Transforms longitudinal medical history into a plain-language view of diagnoses, medications, utilization, barriers, and the risk pathways most relevant to the member.

Community Intelligence

Identifies high-need, high-benefit, low-coverage communities and connects intervention response with geography, access, deprivation, and social vulnerability themes.

Risk-pathway network

Maps how factors such as neighborhood deprivation, fragmented care, polypharmacy, and non-adherence connect to admissions, emergency visits, and other outcomes.

Resource Allocation

Uses optimization to prioritize eligible members and programs under staffing, capacity, budget, and expected-impact constraints.

Natural-language exploration

Lets users question the evidence across members, cohorts, pathways, providers, and communities, then move directly from an answer to the supporting population.

Care Compass in action

One high-risk population. Three very different response paths.

An illustrative admissions-prevention example showing why risk alone is not enough to allocate care-management resources.

6,408 members
ESRD population · admissions and emergency-visit prevention

A traditional risk model may place much of this population into the same high-priority queue. Care Compass asks a different question: whose outcome is likely to change because of the intervention?

1

994 likely responders identified

These members showed an estimated beneficial response to the admissions and emergency-visit prevention intervention, creating a defensible priority group for care-management outreach.

2

2,358 members with structural barriers

Rather than labeling them as failures, Care Compass identifies patterns associated with non-response—such as care fragmentation, medication burden, adherence challenges, and community-level barriers—so the strategy can be redesigned.

3

3,056 newer members separated from false certainty

Members without sufficient longitudinal history are not forced into an unreliable response classification. They remain visible for alternative assessment until enough information is available.

4

More than $750,000 in demonstrated savings

By focusing resources where the intervention showed benefit instead of treating the entire high-risk population alike, the analysis demonstrated savings of more than $750,000 across approximately 6,000 ESRD patients.

Evidence you can inspect

Every analysis is built to be challenged

Care Compass does not hide a result behind a score. It preserves the population definition, temporal design, assumptions, effect estimates, member classifications, pathway evidence, and validation record.

130+completed intervention–outcome analyses
3 levelspopulation, cohort, and member-level intelligence
One auditidentification, assumptions, robustness tests, and warnings
Effect estimates

Average and member-level effects show whether an intervention is associated with better, neutral, or unfavorable outcomes—and how large that difference may be.

Responder phenotypes

Member clusters and contributing features reveal which combinations of clinical, utilization, and access characteristics distinguish response patterns.

Causal pathways

Interactive network views connect upstream factors, care-system friction, interventions, and outcomes across populations, providers, and geography.

Counterfactual risk

Risk with and without the intervention makes the estimated change understandable without requiring users to interpret model internals.

Robustness testing

Independent validation checks test whether findings remain directionally stable under placebo, subset, and sensitivity challenges.

Operational outputs

Teams can filter and export the supporting member population, review pathways and contributors, and connect results to care-management workflows.

The operational difference

Same team. Same budget. A completely different starting point.

This is the practical difference Care Compass makes to how a care management team spends its day.

Without Care Compass

  • Broad, population-level outreach with no way to prioritize
  • A risk score with no explanation of the underlying cause
  • High care manager workload, diluted impact
  • No visibility into which interventions actually work
  • No way to know which members won't benefit from outreach
  • No closed-loop measurement of what worked
  • Star ratings at risk from missed quality gaps

With Care Compass

  • Targeted to the members with the highest benefit potential
  • An evidence-backed pathway showing factors associated with response
  • Recommendations sized to your team's real capacity
  • Population and member-level estimates for defined interventions
  • Non-responders and unfavorable estimates routed for different review
  • Estimated avoided events and savings calculated transparently
  • Member, provider, community, and population views in one workflow

Same resources. Sharper focus. Greater impact.

From evidence to value

Three ways Care Compass creates measurable impact

Value is calculated from each client's population, intervention effect, eligible members, outcome period, event cost, and operational constraints—not from a generic industry promise.

Estimated avoided events

Translate the measured intervention effect into an expected number of avoided admissions, emergency visits, or other defined outcomes over the analysis period.

Effect × exposure

Care Compass translates each measured effect across the defined outcome period and clearly displays the enrollment and exposure assumptions used.

Resource reallocation

Move scarce care-management capacity away from broad, low-yield enrollment and toward members with the strongest evidence of potential benefit.

Capacity → impact

Optimization makes the tradeoffs visible when workforce, budget, or program slots cannot cover every eligible member.

Client-specific savings

Apply the client's own event costs and assumptions to the estimated avoided events, then expose the formula and supporting member population for review.

Events × cost

In the ESRD analysis, intervention targeting demonstrated more than $750,000 in savings across approximately 6,000 patients.

Who it's for

Built for organizations that carry the risk

Care Compass is designed for teams who are financially accountable for the health outcomes of a defined population.

Health plans

Medicare Advantage plans

Reduce avoidable admissions eroding your medical loss ratio, and close the quality gaps that put Star bonuses at risk.

Government programs

ACO REACH & Medicaid MCOs

Demonstrate real per-member savings and coordinate care across fragmented provider networks — without a bigger analytics budget.

Digital health

Value-based care vendors

Show payers causal proof of program impact instead of an assumed ROI, and plug straight into workflows your team already uses.

Life sciences

Pharmaceutical companies

Separate true drug performance from care-coordination failures, and isolate the real drivers of medication non-adherence.

Where Care Compass stands apart

Built for a different decision

Care Compass complements risk stratification and care-management software by adding the intervention-response evidence needed to decide who should receive limited resources.

Capability Legacy risk platforms Basic analytics vendors Care management software Care Compass
Ranks members by risk ✓ ✓ ✓ ✓
Maps multi-step risk and care-friction pathways ✕ ✕ ✕ ✓
Estimates differential response to a defined intervention ✕ ✕ ✕ ✓
Classifies likely responders and non-responders Limited ✕ Limited ✓
Surfaces unfavorable estimated response for clinical review ✕ ✕ ✕ ✓
Fits recommendations to your team's real capacity ✕ ✕ Limited ✓
Maps social and environmental risk by neighborhood ✕ Limited ✕ ✓
Connects effect estimates to avoided events and savings ✕ ✕ Limited ✓
Questions we hear most

A few things worth knowing up front

Do we need to connect our EHR?

No. Care Compass can begin with longitudinal medical and pharmacy claims and fit into an existing data environment. Available laboratory, eligibility, provider, and community data can add context when present.

Is Care Compass making clinical decisions on its own?

No. Care Compass is decision support. It surfaces estimated response, uncertainty, pathways, barriers, and prioritization options; a qualified care manager or clinician makes the final decision.

How is this different from a chatbot or generic analytics?

Care Compass does not generate an answer from language alone. Its responses are grounded in structured intervention–outcome analyses, member-level estimates, longitudinal records, pathway evidence, and validation results.

How is this different from a standard risk-scoring tool?

Risk scores estimate who is likely to experience an outcome. Care Compass estimates how a defined intervention may change that outcome and how the effect varies across members.

Get in touch

See what Care Compass finds in your own data

Tell us a bit about your organization and we'll set up time to walk through what Care Compass can surface in your own claims data.

  • Start with longitudinal claims and pharmacy data already available
  • Evaluate a priority intervention and outcome on your population
  • Receive population, member, pathway, audit, and savings outputs

We usually reply within one business day.