Arcametric-Researcher-Brief

You know the evidence is out there, trapped in a hundred clinics that can't compare notes. Now you can pool it, de-identified, without ever handling a patient's name.

1. Document the arc of care, with privacy by design.

Arcametric is the documentation and outcomes layer for interventional mental health. It runs beside the records your clinic already keeps, structures the full arc of care, and is built so patient identity never reaches our servers.

Two things set it apart: how much of care it captures, and where identity stops. The rest of this brief follows from those two facts.

The arc of care: the full clinician workflow from intake through outcomes and reporting. — **How much of care it captures.** Arcametric documents the full arc of care, from intake through the treatment phases to follow-up, outcomes, and reporting, structured at every step.

Where identity stops. Patient names, dates of birth, and contact details stay in the clinic. Only coded references cross the boundary.

2. The Infrastructure Problem

Interventional mental health is generating real-world clinical data at scale. Most of it is not computable.

Sessions are documented in free-text notes, entered into EHR fields designed for visit-based primary care, or recorded inconsistently across providers and protocols. Assessment instruments are administered but not scored in structured form. Adverse events are narrated, not coded. Follow-up outcomes are tracked informally or not at all.

The result is a rapidly expanding clinical practice with almost no structured, analyzable real-world evidence to draw from. The problem is not a shortage of sessions. It is a shortage of infrastructure designed to capture what those sessions produce in a form that supports analysis.

3. Why Current Systems Create Friction

General-purpose records were built for visit-based, diagnosis-driven care. Five gaps recur across interventional mental health settings.

Where it breaks	What happens
Structure	General-purpose EHRs were designed for visit-based, diagnosis-driven care. The preparation, dosing, and integration arc does not map onto standard encounter templates, so clinicians adapt with free text, which produces no computable output.
Identity	Patient identity is embedded throughout standard clinical records, which makes downstream de-identification a retroactive process with variable reliability. Systems designed to store identifiers cannot easily produce an evidence layer that removes them.
Longitudinal linkage	Tracking across sessions requires explicit subject-linking architecture. Standard encounter-based documentation does not maintain that link in a computable form, so cross-session analysis requires manual chart reconstruction.
Safety	Adverse-event patterns, substance interactions, vital-sign deviations, and protocol variances are captured narratively. They cannot be queried programmatically or aggregated across providers for signal detection.
Export	The output of existing documentation workflows is PDF notes and static charts. There is no structured export format suitable for registry submission, IRB data packages, or outcome-correlation analysis.

4. Pool evidence without pooling identities.

Arcametric is designed as a structured clinical intelligence layer, not a general-purpose records system. Its architecture makes specific decisions oriented toward producing computable data from routine clinical documentation, without asking practitioners to do extra data-entry work for research purposes.

The core design intent is structured documentation as a standard of care that also produces an evidence record as a byproduct, rather than a research data-collection workflow bolted onto clinical practice.

Structured input controls replace free text across the preparation, dosing, and integration phases. Substance, route, and dose are captured against governed reference tables, not typed as unstructured text.
Coded subject linking connects sessions longitudinally without direct patient identifiers in the shared data model. The treating organization keeps the identity-to-code mapping locally.
Structured assessment administration produces scored, timestamped records from instruments administered within the platform. Scores are stored as discrete values, not narrative summaries.
Coded safety events are captured as structured records against standardized terminology references. Adverse events, interaction flags, and protocol deviations can be queried, not kept as free-text narrative.
Export-oriented design produces structured data outputs from routine documentation rather than requiring a separate extraction or cleaning step downstream.

What the architecture makes possible

Each design choice carries through, row by row, from the architecture to what it means and what it makes possible.

The architecture	What that means	What it makes possible
Zero-PHI by design: the shared database has no free-text field where a name could be typed.	There is no patient identity in the shared system to expose.	Protect. A vendor that holds no identity is a shorter security review.
Your clinic keeps the only map between a code and a person, and it never leaves your building.	We hold no key that turns a code back into a patient.	Own. The part that matters stays yours; your records cannot become someone else's asset.
Every entry is coded at the moment of care, with standard vocabularies applied at the source.	A session recorded at your site matches one recorded elsewhere.	Benchmark. You compare outcomes against the network while exposing no identity.
The documentation you already owe is structured from the start.	Ordinary care produces records that are already research shaped.	Export. You export research-ready evidence from routine work, not a separate project.

5. Aggregate, standardize, and export your evidence.

Each capability below turns routine documentation into structured, computable evidence.

Capability	What it enables
Longitudinal patient trajectory	Structured session data linked by coded subject ID across the full care arc, so cross-session outcome analysis needs no manual chart reconstruction.
Adverse-event analysis	Structured adverse-event capture with severity, timing, and session context, and can be queried across subjects with shared exposure variables.
Assessment outcome correlation	Structured scores from administered instruments correlated against session variables such as substance, dose, route, and phase, supporting pre and post analysis without reconstruction.
Network benchmarking	Aggregated outcome distributions across consenting practitioners, enabling site-level comparison against network norms for specific protocols and populations.
Structured export packages	Documentation-derived exports suited to IRB data packages, registry submission, and downstream statistical analysis.
Reference table coverage	Substances, routes, and clinical interactions governed against reference tables, with crosswalk mapping toward recognized external standards.

Architecture note. The de-identified design reduces certain centralized identifier risks, but it does not constitute a certified de-identification method under HIPAA Safe Harbor or Expert Determination standards. Arcametric has completed legal review of its current architecture and public-facing claims. Research teams should still evaluate the data-governance posture with their own IRB and legal counsel before relying on Arcametric outputs for regulated research purposes.

6. Who This Brief Is For

Audience	Who it serves
Research teams and investigators	Principal investigators, research coordinators, and data scientists evaluating existing or prospective interventional mental health data infrastructure for study design or real-world evidence review.
IRB and ethics evaluators	Boards and ethics committees assessing data-capture systems used in interventional mental health, particularly around de-identification architecture and subject-linking methodology.
Outcomes researchers	Researchers modeling cost-effectiveness, safety, or population-level outcomes who require structured, computable longitudinal data rather than chart abstractions.
Registry and standards bodies	Organizations developing data standards, reporting frameworks, or registry protocols who want to evaluate a production data model against emerging field standards.

7. Verify the model, the boundary, and the exports.

Review area	Key questions
Data model architecture	How are sessions, subjects, assessments, and safety events structured? What are the entity relationships, and how is longitudinal linkage maintained?
De-identification posture	What identifiers are excluded from the shared model by design? What stays in the local organization's control? What legal review is complete, and what remains institution-specific?
Reference table governance	What external standards are the substance, route, and interaction reference tables aligned to? Where do crosswalk gaps exist, and what is the remediation roadmap?
Export format and completeness	What computable outputs are available today, and in what format? What fields are present or missing relative to registry or IRB submission requirements?
Benchmark methodology	How are network comparisons constructed? What are the inclusion criteria, opt-in governance, and suppression rules for small-cell disclosure risk?

Next step: request a technical briefing

Arcametric is available for qualified research review. We can provide a technical briefing that covers the data model, the de-identification architecture, the current capability state, and the export format. Technical review comes first, and we will surface limitations directly.

Contact: hello@arcametric.com · arcametric.com

A Structured Evidence Layer for Interventional Mental Health