Hypersphere HealthPatient 3-D™ See it on your data

Who we serve · Health plans & ACOs

Risk adjustment sees what was coded. We see what the coding missed.

Her score adds her conditions up. Her cost doesn’t. Patient 3-D finds the members whose conditions compound — the medical gap first, quality second, documentation completeness as the consequence.

Her cost two ways · scaled to the median member in an all-payer county
Additive score — what her coding adds up to
Compounding — what her conditions do together

1 · The medical gap

  1. The medical gap. The cost her score leaves out — found before it lands.
  2. Quality. The measures that follow from closing it.
  3. Documentation completeness. The consequence, not the goal.

Median compounding multiplier 1.62×1 · at the 95th percentile, 4.5×1

What you get.

  • The medical gap first

    The members who are sicker than their score says, found before the cost lands — ranked, with the reason.

  • Quality second

    The measures that follow from closing the gap, with each action documented, date-stamped and closed.

  • One measurement, every line

    The same instrument across Medicare Advantage, Medicaid and commercial. The clinical picture does not change with the funding line.

Measured on an all-payer county.

1 in 52is sicker than their risk score says.

Every covered life in a synthetic cohort calibrated to a Midwest county, scored across five coverage types. 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 care is documented, billed and closed.

    Each action acknowledged, completed or disputed, date-stamped. Chronic care management, remote monitoring, transitional care — services already covered, now activated.

  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 5”: share of scored lives that rank higher under compounding than under additive scoring. 21.3% in the Midwest county cohort; 17.4% and 18.2% in the two regions of the academic health system cohort.