PRISMqd creates a new class of healthcare data.
PRISMqd creates a new class of healthcare data by converting the relationships among physiology, human observation, clinical reasoning, decisions, institutional conditions, interventions, and outcomes into computable causal intelligence.
The company is raising $2 million to move from founder-built public instruments and synthetic integrated reports into governed enterprise validation, design partnerships, and a scalable product roadmap.
Examine the working report toolWorking instruments—not a slide-only concept.
PHC Calculator
Facility-level patient-harm cost modeling with explicit evidence tiers, sensitivity boundaries, and auditable formulas.
Open PHCMortality Attribution Module
Population-level attributable-mortality estimation with explicit limits on individual causation and negligence conclusions.
Open MAMEnterprise Harm & Liability Report
A report engine that keeps observed evidence, derived findings, financial implications, and testable hypotheses separate.
Open the report generatorHealthcare records events. It does not preserve the full causal system.
Healthcare has spent decades digitizing events. PRISMqd digitizes the relationships between them.
From event recording to causal learning.
PRISMqd creates the signal-continuity and intelligence infrastructure required to preserve those relationships, make them measurable and auditable, support meaningful intervention, and verify whether risk was actually resolved.
PRISMqd converts healthcare from an event-recording system into a causal learning system.
Different markets examine the same underlying system.
That unlocks data healthcare has never systematically measured: clinical trajectory, reasoning, escalation, decision latency, intervention effectiveness, causal propagation, recurrence, and institutional contribution to outcomes.
One architecture can therefore answer questions currently divided among monitoring systems, EHRs, patient-safety platforms, quality departments, insurers, malpractice carriers, regulators, and financial-risk systems, because each is looking at a different projection of the same underlying causal system.
Computable causal intelligence means structured, provenance-linked, temporally ordered representations of relationships, candidate causal mechanisms, decision pathways, intervention conditions, and testable hypotheses. Observational reconstruction alone does not establish causal proof.
Validate the workflow before scaling software.
Design partnerships
Bounded enterprise engagements to map one consequential workflow, establish governance, and run prespecified validation.
Licensed methods and reports
Governed analytical instruments and repeatable reporting infrastructure for defined use cases.
Software delivery
Productization tied to validated workflows, security requirements, and demonstrated user value—not assumed in advance.
Five distinct diligence questions
- Present assets: published frameworks, working PHC and MAM instruments, prototypes, and synthetic report demonstrations.
- Design hypotheses: whether preserving the full decision-and-response chain makes continuity failures measurable and testable.
- Validation work: prospective workflow, human-factors, clinical, operational, and economic evaluation.
- Partner requirements: a bounded workflow, local authority, governance, authorized data, measures, and stop rules.
- Future commercial assumptions: repeatable adoption, licensing demand, and scalable software delivery remain to be established.
Test continuity in one consequential workflow.
The hypothesis is that preserving context, significance, authority, ownership, action, and verification can make failures in the decision-and-response chain measurable and testable. Prospective validation must determine operational fit, burden, unintended effects, and outcomes.
A partner supplies a defined workflow, local clinical authority, agreed decision rights, governance, authorized data access, privacy and security requirements, measures, and stop rules. Licensing and software scale remain future commercial assumptions.
Point tools show pieces. PRISMqd is designed around the joins.
Incident reporting
Captures reported events but does not necessarily reconstruct the complete clinical, staffing, financial, and governance pathway.
Claims and cost analytics
Quantifies utilization and spend but may not preserve the clinical sequence, authority, or intervention logic.
Consulting and legal review
Provides matter-specific analysis, often without a reusable evidence schema and productized falsification protocol.
Build the operating model, generate the evidence, and earn the right to scale.
Enterprise validation
Design-partnership development, governed data work, and prespecified evaluation.
Clinical and product assurance
Clinical, regulatory, privacy, security, legal-boundary, statistical, and human-factors review.
Product infrastructure
Production architecture, auditable report generation, integrations, and workflow delivery tied to validated requirements.
Founder
Jennifer Torrez, BSN, RN is a founder, inventor, author, and research-product leader whose work began inside high-acuity clinical systems. More than two decades in healthcare, including critical care and rapid response, taught her to synthesize incomplete evidence, coordinate action across authority gradients, and reassess interventions under extreme cognitive load. She now applies that operating judgment to AI by building governed multi-agent research environments, causal architectures, published frameworks, and inspectable prototypes. She founded PRISMqd to make the path from clinical recognition to meaningful action and verified resolution measurable, auditable, and testable.
Current limits
- No established product-market fit or repeatable enterprise adoption
- No established clinical, operational, or economic outcomes
- No claim of regulatory clearance for future software capabilities
- Integrated report currently uses synthetic demonstration data
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