A trial result and the claim made on its behalf are two different things, and the gap between them is where most health coverage goes wrong. This is research for the analyst, the policy researcher and the reporter who need the endpoint as the protocol defined it, the sponsor and its stake named, the approval and the price traced to the decision that set them — and the workforce and distribution questions behind a health system read as structure, not sentiment.
Tennessee, Mississippi, North Carolina, and other states are loosening or repealing CON rules as private-equity chains and REIT landlords consolidate hospitals those regulations were designed to oversee.
Real health research from the public library, shown as it was published. The stamps and the sources are the product.
Each one is a question a specialist actually brings, and each is answered by a scan this domain carries — not a generic report with the name swapped in.
“Does the Phase 3 readout support the label the sponsor is guiding to, or only the endpoint it met?”
“Who actually pays when a drug is priced at launch, and which lever moved the price?”
“This statistic doubled in a year. Did the disease change, or did the counting?”
“What does the regional staffing shortage look like once the vacancy data is separated from the press release?”
Every judgment carries a grade for how it is actually known, every figure carries who reported it, and the structured record below is read on every scan before the model writes a word.
42 first-tier sources for health, and 26 blocked outright. Search results are one input; every figure is graded on where it came from, not on being found. How the grades work →
Scale here is clinical and regulatory weight — the patients, the payers and the authorities a connection reaches — and Complexity is how ambiguous the evidence and the pathway still are.
Small clinical significance, easy to understand. Standard regulatory processes explain the connection.
Meaningful health sector weight and the implications are clear. The relationship is well understood.
Meaningful stakes with real uncertainty. The data may be ambiguous, the regulatory path unclear, or the patient impact hard to quantify.
Very high stakes at population or market level. Patient safety, regulatory action, or public health consequences make this one of the most important connections, regardless of complexity.
No precedent. A fundamental shift in the health landscape that existing frameworks may not cover.
When clinical significance grows to population-level or market-wide scale, FLOW C reclassifies to FLOW D immediately. The priority shifts from 'analyze carefully' to 'mobilize response.'
Large clinical significance means FLOW D regardless of complexity. A straightforward drug recall affecting millions of patients is FLOW D, not FLOW A.
FLOW S requires conditions that fall entirely outside existing clinical, regulatory, or commercial frameworks — max 1-2 per analysis. The test: do existing playbooks apply at all?
One vocabulary across every domain — a FLOW D here reads at the same weight as a FLOW D in any other — so the classification travels, and so does your judgment. The framework in full →
7 ways in, then the rest of the catalog organised as the Health workbench organises it — establish the background, map the argument, work the record, analyse the connections, decide what to do. Every one comes back graded, sourced and exportable.
Don’t know which one you need? The Router reads a plain question and picks one; the Planner takes a goal and designs a linked sequence. Every completed scan opens onto its own follow-ups — ask it a question, re-check what has changed, red-team its judgments, or push a signal onto your watchboard.
And the Studio turns a set of health scans into a planned, researched, edited report or book. Every format →
Open the health workbench with a drug program, a trial, a payer or a system. What comes back is the evidence, graded and sourced.