Researcher Brief | Arcametric

Interventional mental health is already generating valuable real-world clinical data. Arcametric structures it at the point of care so it can be compared, analyzed, and exported without moving patient names into the shared system.

1. Document each step in the arc of care, without ever sharing a patient's name.

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 keeps patient names, dates of birth, and contact details in the clinic.

Two design choices drive the research value: how much of the care arc is structured, and where patient identity stops. The rest of this brief follows from those two facts.

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. Interventional mental health is generating real-world data that current systems can't analyze.

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. General-purpose records turn a continuous care arc into disconnected, unstructured data.

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 to make structured documentation part of routine clinical work, so the same record can also support evidence generation without becoming a separate research data-entry project.

Structured input controls capture the variables intended for comparison across preparation, treatment/dosing, and integration. Substance, route, and dose are captured against governed reference tables rather than typed as unstructured text.
De-identified subject linking connects sessions longitudinally without direct patient identifiers in the shared data model. The treating organization keeps the identity-to-reference mapping locally.
Structured assessment administration produces scored, timestamped records from instruments administered within the platform. Scores are stored as discrete values, not narrative summaries.
Structured safety events are captured against governed terminology references. Adverse events, interaction flags, and protocol deviations can be queried instead of being trapped in free-text narrative.
Export-oriented design produces structured data outputs from routine documentation, reducing the reconstruction and cleaning required downstream.

De-identified architecture opens up new possibilities.

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 clinical data model has no field intended to hold a patient name.	Patient names, dates of birth, and contact details remain outside the shared clinical data model.	You reduce centralized identity exposure because the shared system does not store patient names.
Your clinic keeps the only identity-to-reference mapping in its own systems.	Arcametric does not hold the identity-to-reference mapping.	You keep control of the information that connects a de-identified reference back to a person.
Comparable clinical variables are coded at the point of documentation, with governed reference vocabularies applied at the source.	The same defined variable can be recorded consistently across sessions, practitioners, and sites.	You can compare outcomes across eligible records as data accumulates, while patient identity remains outside the shared system.
Routine clinical documentation is structured from the start.	Routine documentation produces structured records that can be analyzed without reconstructing them from narrative notes.	You can export structured evidence from routine work without creating a separate research data-entry workflow.

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 a de-identified subject reference across the full care arc, so cross-session outcome analysis does not require 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	Benchmarking across eligible records and participating sites, with comparison value increasing as additional outcomes are logged and network participation grows.
Structured export packages	Structured exports that can support downstream statistical analysis and preparation of IRB or registry data packages, subject to each study or registry's requirements.
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 by itself constitute a certified de-identification method under HIPAA Safe Harbor or Expert Determination standards. Research teams should evaluate the data-governance posture with their own IRB and legal counsel before relying on Arcametric outputs for regulated research purposes.

6. Review Arcametric from the research lens that matters to you.

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: review the architecture with us

If you want to evaluate Arcametric for research, registry, or evidence-generation use, the next step is a technical briefing. We will walk through the data model, de-identification architecture, current capability state, export formats, and known limitations directly.

Next, see the Clinical Data Taxonomy and Export Dictionary for the de-identified data model, the variable dictionary, and the structured export formats.

Contact: hello@arcametric.com · arcametric.com

A Structured Evidence Layer for Interventional Mental Health