Hypersphere HealthPatient 3-D™ See it on your data

Who we serve · Public programs

Measure the population before you fund the program.

A Rural Health Transformation program funds what it can see. Patient 3-D measures the whole population first — all payers, every covered life — and counts clinical need before it counts reimbursement.

Illustrative proportions · one county, every payer
  • Commercial
  • Medicare
  • Medicaid
  • ACA Marketplace
  • Uninsured — counted for access, not billed

One plan sees its band. The program sees the county — and her in it, whoever pays.

158,8671 covered lives, five coverage types, one run.

What you get.

  • The whole population

    Every covered life measured, all payers, in one run — not one plan’s slice of the county.

  • Need before reimbursement

    What the population needs clinically, and what the payment system funds and doesn’t.

  • A report you can reproduce

    Every reported figure resolves to a run record, so the report you file can be rebuilt by someone who isn’t us.

Measured on a whole county.

1 in 32patients with multiple conditions is compounding in a way no risk score can represent.

A synthetic, all-payer cohort calibrated to a Midwest county, built from public census data. Same results across five software releases.

Cohort 02 · Midwest county · all-payer158,867 lives · five coverage types
Compounding-activePatients with at least one active interaction between their conditions.
36%
Re-rankedPatients who move higher on the priority list once those interactions are counted.
21%

Same results across five software releases.

Her path, where you meet it.

The steps of the platform that land on your desk. Every one is a service already covered.

  1. She is seen.

    Her record arrives — the same record everyone had. The engine reads her conditions as one compounding trajectory and names what no single-disease guideline was watching for.

  2. Her home is connected.

    A monitoring kit chosen for her profile, not a generic box. Her blood pressure, weight and glucose reach a nurse before they reach an emergency room.

  3. Her outcomes are reported — and audited by someone who isn’t us.

    Every figure traces to an engine run. The audit lives in a store we write and cannot touch.

  4. She stays home.

    That’s the outcome. Everything else is the machinery.

Built by people who ran the systems it fixes — twenty years inside government health programs, PMO leadership across twenty-five-plus health plan launches, and a fourteen-seat clinical bench. Meet the team →

How it starts.

A proof of value: one extract under a data use agreement and your clinicians’ judgment, with nothing installed on your side. In production, Patient 3-D runs inside your environment, and your data never leaves it.

  1. One extract under a data use agreement, delivered once.

    A flat-file extract you already know how to produce, limited to the fields the analysis needs. We send the column spec; you send the file once.

  2. A sealed U.S. environment.

    The proof of value runs in a sealed environment in the United States, with no connection to your systems.

  3. Your clinicians at the review table.

    Your named clinical reviewers see every figure before any figure is released.

  4. Results in weeks.

    Your population measured in three dimensions — what each patient has, how severe, how it compounds. Weeks, not a program.

  5. Destroyed, with a certificate.

    When the work ends the environment is destroyed, and you receive a certificate of destruction.

See it on your data.

A flat-file extract, a sealed environment, your clinicians at the review table, results in weeks.

Sources

  1. Midwest county cohort, all-payer: 158,867 covered lives; 151,181 scored; 78,630 clinically eligible; 35.7% compounding-active; 21.3% re-ranked; median multiplier 1.62×; 4.5× at the 95th percentile. Rerun on five software releases; all 492 checked values came out identical every time. Synthetic cohort built from public census data; no PHI.
  2. “1 in 3”: share of scored lives with an active compounding interaction. 35.7% in a synthetic cohort calibrated to a Midwest county; 31.2% and 31.9% in the two regions of a synthetic cohort calibrated to an academic health system. Source: Patient 3-D population analyses of both cohorts.