Research Institutions
Accelerate clinical research with automated cohort management, reproducible analysis pipelines, and publication-ready analytics — all grounded in governed, provenance-tracked data.
Capabilities
Research-Grade Intelligence
Cohort Management
Automated cohort identification and management across multi-site studies. Define inclusion and exclusion criteria programmatically and track enrollment in real time across participating institutions.
Statistical Analysis Pipelines
Pre-registered analysis pipelines that execute reproducibly. Built-in support for effect size calculation, confidence intervals, Bayesian methods, and multiple comparison corrections.
Publication-Ready Visualization
Generate journal-quality figures and tables that meet the formatting requirements of major publications. Export to vector formats with full metadata for peer review transparency.
Data Provenance
Every data point maintains its full provenance chain from collection to publication. Satisfy even the most rigorous IRB and peer review requirements with automated audit trails.
Multi-Site Harmonization
Aggregate and harmonize data across institutions with different EHR systems, assessment instruments, and data formats. Automatic FHIR R4 standardization ensures interoperability.
IRB-Compliant Export
Export de-identified datasets with configurable privacy controls. Built-in support for Safe Harbor and Expert Determination de-identification methods with full audit logging.
From Data Collection to Publication
Design
Define study parameters, register analysis plans, configure cohort criteria
Collect
Multi-source data ingestion with automatic standardization and quality checks
Analyze
Execute pre-registered analysis pipelines with full reproducibility
Publish
Generate journal-ready figures, tables, and supplementary materials
Real-World Scenario
Multi-Site Depression Biomarker Study
4 sites, 500 patients, 18 months. Here is how MindCODE supports every phase of a complex longitudinal research design.
Study Design
A consortium of 4 academic medical centers launches an 18-month longitudinal study on depression biomarkers. 500 patients will be enrolled across sites. Data sources include PHQ-9 and GAD-7 assessments (collected biweekly), resting-state fMRI (baseline, 6-month, 12-month), wrist actigraphy for sleep and activity (continuous), and blood-based inflammatory biomarkers (monthly draws).
Data Harmonization
Each site uses a different EHR system. Site A runs Epic, Site B uses Cerner, Site C has a custom research database, and Site D uses CPRS. MindCODE ingests FHIR R4 exports from each, maps local assessment codes to a unified ontology, and aligns all time-series data to a common temporal reference. Discrepancies — such as Site C using a 0-24 PHQ-9 variant — are flagged and resolved with configurable mapping rules.
Analysis Execution
Pre-registered analysis scripts run against the harmonized dataset. MindCODE computes behavioral features from actigraphy (sleep efficiency, circadian rhythm stability, activity fragmentation), aligns them with clinical assessment windows, and executes the registered statistical models: mixed-effects regression for longitudinal trajectories, cluster analysis for response subtypes, and mediation models linking sleep disruption to PHQ-9 change.
Publication Output
The system generates a cohort characteristics table (Table 1) with demographics stratified by site, a trajectory plot showing PHQ-9 change by response cluster, forest plots for biomarker effect sizes, and supplementary data packages. All figures export in SVG and PDF at 300 DPI. Every result links back to the exact dataset version, analysis script commit hash, and execution timestamp.
Deliverables
Research Outputs
Every output is traceable, reproducible, and formatted for its intended audience — from peer reviewers to funding agencies.
Cohort Definition Files
JSON / CSVMachine-readable inclusion and exclusion criteria with full audit trail. Every enrollment decision is logged with the criteria version that was applied, enabling post-hoc review of cohort composition decisions.
Pre-Registered Analysis Scripts
Versioned, reproducibleAnalysis code is version-controlled and linked to the pre-registration record. Each execution produces a deterministic result given the same input data, with environment specifications (package versions, random seeds) captured automatically.
Statistical Results
Structured tablesEffect sizes (Cohen's d, odds ratios), 95% confidence intervals, exact p-values, and Bayes factors where applicable. Multiple comparison corrections (Bonferroni, FDR) are applied and documented. Results are exportable as structured data for meta-analyses.
Publication-Ready Figures
SVG / PDFJournal-quality figures formatted to APA, AMA, or custom style guides. Vector export at configurable DPI. Figures include embedded metadata linking to the source data and analysis parameters that produced them.
Supplementary Data Packages
ZIP archivePeer reviewer packages containing de-identified datasets, analysis scripts, environment specifications, and a reproducibility manifest. Reviewers can re-execute analyses independently to verify results.
Governance
Data Governance for Research
Consent tracking, IRB compliance, and de-identification are not afterthoughts — they are built into every data operation.
Consent Tracking
Every data point is linked to the consent version under which it was collected. When consent is updated or withdrawn, downstream datasets are automatically flagged. Consent scope is granular: patients can consent to clinical use but not research, or to specific study protocols. Re-consent workflows are tracked with timestamps and version identifiers.
IRB Compliance
Study protocols are registered in MindCODE with their IRB approval numbers, amendment history, and expiration dates. The system prevents data access for studies with expired approvals and alerts administrators 30 days before expiration. Protocol deviations are logged and reportable for continuing review submissions.
De-Identification
Support for both Safe Harbor (removal of 18 HIPAA identifiers) and Expert Determination methods. De-identification rules are applied consistently across all export paths. The system generates a de-identification audit report showing which fields were removed, masked, or generalized — and certifies the method used for each dataset release.
Ready to Get Started?
Build AI systems you can explain, govern, and trust. Whether you are a clinic, a research group, or a regulated software team, MindCODE gives you the infrastructure to move from fragmented data to accountable intelligence.
Or reach us at service@mindcode.cc