SquareML
We help insurance brokerages automate claims processing. Claims, clinical, and eligibility data are joined into one member view. The system then flags open care gaps and rising risk, ranks the next intervention, and directs outreach to the members most likely to move a HEDIS or CMS quality measure. Human review stays on every STAR-linked measure.
Six steps, one member-level view — with people in the loop wherever a rating is at stake.
The process starts by joining claims, clinical, and eligibility files into a single member-level view. Each source tells part of the story: claims show utilization and cost, clinical data shows conditions and results, and eligibility files confirm who is covered and under which line of business. Combining them removes silos and gives every downstream step a consistent foundation.
With a unified view, the system finds open gaps in care, such as overdue screenings or missed follow-ups, and flags members showing rising risk. Risk stratification draws on clinical, socio-economic, and utilization signals, so members are identified before they become high-cost. Chronic, high-utilization, and complex-care members are surfaced as separate segments, and populations can be clustered by need and cost pattern so programs are aimed rather than applied broadly.
Instead of producing a generic member list, the workflow ranks the next best intervention. Care management teams see which action to take, for which member, and in what order. This matters because outreach capacity is limited, and ranking ensures effort goes where it changes cost or outcomes most.
Outreach is directed at the members most likely to move a HEDIS or CMS quality measure. This includes medication adherence work for conditions such as diabetes, hypertension, and statin therapy, as well as follow-up after admissions, discharges, and transfers. Readmissions are tracked at 30 and 60 days by condition or procedure, so quality teams can see which providers and cohorts are drifting off target.
People remain in the loop on every measure tied to a CMS or STAR rating target. Analysts and clinicians validate results before they drive decisions, which keeps the process accurate, auditable, and compliant in a regulated environment.
Results are tracked against quality benchmarks, and outcomes feed back into the next cycle of risk identification and ranking.
Quality ratings influence revenue and bonus payments, while care management resources are finite. By prioritizing the right members and interventions, health plans improve ratings, reduce avoidable spend, and strengthen member outcomes across every line of business.
With Paramed — one of the largest health-records aggregators — an LLM/RAG pipeline that generates digital summaries from complex structured and unstructured health documents.
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