Healthcare has spent decades digitizing events. PRISMqd digitizes the relationships between them.
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.
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.
PRISMqd converts healthcare from an event-recording system into a causal learning system.
Current stage: published frameworks, working public instruments, prototypes, and synthetic demonstration reports. Clinical effectiveness, enterprise adoption, and economic outcomes have not yet been established.
Explore a bounded design partnershipThe missing layer is continuity.
A healthcare organization can succeed at the first step and still fail the patient.
A nurse can recognize deterioration. The monitor can contain the relevant physiologic data. The EHR can contain pieces of the story. A physician can receive a notification. A policy can technically have been followed.
And the patient can still deteriorate because the system failed to preserve context + significance + authority + ownership + action + verification across that chain.
Conventional clinical AI has an observability problem:
It can analyze what the healthcare system captured. It cannot recover important information that the healthcare system systematically failed to preserve.
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.
One sequence. Nine decision stages. Clear ownership.
- Detection
- Recognition
- Interpretation
- Prioritization
- Communication
- Escalation
- Action
- Reassessment
- Validation / Verified Resolution
Validation is the formal ninth stage of the Continuity Risk Framework. Verified Resolution expresses its public-facing purpose.
Recognize
Detection, recognition, and interpretation establish what the signal may mean.
Mobilize
Prioritization, communication, and escalation connect concern with authority.
Close
Action, reassessment, and validation make response and residual risk visible.
The Continuity Risk Framework gives leaders a common structure for examining where information changes, stalls, loses authority, or fails to reach verified closure. It is intended to support local assessment, workflow design, governance, measurement, and learning—not to replace clinical judgment or local policy.
Begin with the workflow before adding software.
The first enterprise pathway is a manual-first rescue-reliability implementation system. A partner organization defines one bounded workflow, establishes local decision rights, selects process and balancing measures, tests the operating model, and reviews what the evidence supports before considering broader implementation or software enablement.
Assess
Document the current workflow, roles, policies, measures, and constraints.
Plan
Define local adaptations, governance, measures, responsibilities, and stop rules.
Test
Run a contained, reversible test under the organization’s clinical authority.
Evaluate
Review fidelity, burden, equity, unintended effects, and what the evidence supports.
Decide
Continue, revise, expand, or stop.
From clinical safety to institutional intelligence.
Healthcare intelligence becomes substantially more powerful when physiology, human observation, clinical reasoning, operational conditions, decisions, interventions, and outcomes are modeled as one continuous causal system. PRISMqd provides the architecture for that system.
Failure-to-rescue is the first use case. Preserving clinical trajectory, reasoning, escalation, decision latency, institutional conditions, interventions, and outcomes makes the relationships available for measurement, audit, and prospective testing.
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.
Built far enough to inspect. Early enough to test honestly.
Available for review
- Published clinical-safety and governance frameworks with persistent records
- Defined rescue-readiness implementation architecture
- Product specifications and workflow models
- Inspectable interface artifacts and working browser prototypes
- A U.S. provisional patent application filed for the PRISMqd architecture
Not yet established
- Clinical effectiveness
- Operational effectiveness
- Economic impact or return on investment
- Enterprise deployment or repeatable adoption
- Regulatory classification or clearance for future software capabilities
Different tools for different points in the safety system.
Patient Harm Cost Calculator
A facility-level model that separates reported cost, capture-rate sensitivity, staffing elasticity, and clearly labeled governance scenarios.
Open PHCMortality Attribution Module
A population-level statistical instrument for examining attributable mortality under explicit evidence tiers and causation boundaries.
Open MAMEnterprise Harm & Liability Report
An integrated accountability report that separates observed evidence, derived findings, financial implications, and hypotheses requiring validation.
Generate a demonstration reportRescue Readiness Implementation System
A licensed, adaptable implementation system for examining rescue workflows, governance, measurement, training, and organizational learning. Software is not required to begin testing the workflow.
SafeChart
A clinician-controlled professional documentation concept designed to help preserve contemporaneous information about unsafe conditions, escalation attempts, responses, and follow-up. It is separate from the patient record, does not replace EHR documentation, and is not legal advice or a clinical-decision tool.
LineMap
A product concept for structured IV-access mapping, infusion-to-lumen planning, compatibility constraints, and handoff continuity. The current artifact demonstrates interaction and workflow logic. It is not a production clinical system and has not established clinical outcomes.
View the prototype contextCOMPASS
A proposed clinician-reviewed assessment and longitudinal tracking framework. COMPASS is not a validated diagnostic instrument and must not be used to diagnose, exclude, or direct treatment.
Review the frameworkTest one consequential workflow with clear boundaries.
PRISMqd is seeking a health-system partner willing to examine one defined rescue-readiness workflow through a paid, 90-day design partnership.
The partnership is designed to answer practical questions before scale: Does the architecture fit the local workflow? Can responsibilities and measures be made explicit? What implementation burden does the model create? Which assumptions fail under real operating conditions? What evidence would justify continuation?
The organization retains local clinical authority. Scope, data access, privacy, security, measures, decision rights, and stop rules are established in writing before work begins. The engagement does not guarantee improved outcomes, savings, compliance, or future licensing.
Read the briefOperating judgment shaped inside high-acuity clinical systems.
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.
Review Jennifer Torrez’s selected work$2M pre-seed: build the operating model, generate the evidence, and earn the right to scale.
The round is intended to advance controlled enterprise design partnerships, evidence generation, product finalization, clinical and regulatory review, human-factors validation, and technical work tied to validated workflows.
Review the investor overview