AI Ops

AI Ops is the AI operating layer built into Trialize, designed for clinical-ops leaders, data managers, and sponsors who need AI applied across the full trial lifecycle—from recruitment through data lock. It is intended to reduce time, lower monitoring costs, and improve signal detection without requiring additional headcount or replacing existing systems.

Key benchmarks cited include risk and quality issue detection in 24–48 hours versus 4–6 weeks with traditional methods, monitoring cost reductions of 30–40%, data cleaning effort reductions of 60–80%, adverse event sensitivity of approximately 90% using digital biomarkers, and outcome prediction accuracy of 85–90% compared to 70–75% with traditional approaches.

AI-Driven Patient Recruitment and Screening

• NLP and ML scan EHRs, clinical notes, labs, and patient-reported outcomes (PRO) to identify likely-eligible participants and predict enrollment success.
• Highlights under-represented groups to support targeted and inclusive outreach, including flagging barriers such as transport, language, and social determinants of health.
• Virtual screening combined with remote, literacy-aware consent reduces geography and access barriers.
• Data sources include structured and unstructured EHR, claims, ePRO, and demographics; models include NLP for criteria parsing, classifiers for eligibility and enrollment likelihood, and equity heuristics.
• Workflow: candidate list generation → virtual screening → TeleConsent → ePRO kickoff.
• Guardrails include literacy-aware consent, fairness checks for algorithmic bias, and human-in-the-loop review on eligibility edge cases.
• Metrics to track: pre-screen precision and recall, screen-fail rate, enrollment-likelihood calibration, and diversity targets met.
• Powered by the ePRO and TeleConsent, Data Hub, and Analytics modules.

Digital Biomarkers and Continuous Monitoring

• Shifts monitoring from episodic site visits to continuous, multi-modal signals including accelerometry, heart rate variability, sleep, and voice, analyzed by deep learning.
• AE sensitivity is approximately 90% in AI-based digital biomarker systems versus 70–75% for traditional episodic checks; however, false positive rates are higher (15–20% versus 5–10%), making threshold tuning and clinical validation essential.
• Example benchmark: smartwatch-derived models achieved 93% accuracy and 96% AUROC in mortality prediction in oncology cohorts.
• Guardrails include pre-specifying alert thresholds, piloting to calibrate false positive burden, documenting clinical actions, and monitoring for model drift.
• Metrics to track: sensitivity and specificity, PPV and NPV at chosen thresholds, alert-to-action time, and rate of actionable versus non-actionable alerts.
• Powered by the ePRO and TeleConsent, Data Hub, and Analytics modules.

Risk-Based Monitoring and Quality Assurance

• Learns site- and form-level patterns to flag anomalies, protocol deviations, and data-integrity concerns within 24–48 hours of data entry.
• Delivers 40–60× faster detection windows compared to traditional 4–6 week cycles, with monitoring costs reduced by 30–40% as human reviewers focus on high-risk items.
• Guardrails include false-positive calibration, risk thresholds set by phase and therapeutic area, documented overrides, and maintained human audit trails.
• Metrics to track: time-to-detect, percentage of issues found by RBM versus source data verification, on-site visit reduction, and cost per resolved issue.
• Powered by the Automate, Analytics, and Data Hub modules.

Automated Data Cleaning and Standardization

• Automates reconciliation, outlier detection, dictionary mapping, and imputation; NLP standardizes free-text fields; models predict likely values based on context.
• Reduces manual cleaning from a typical 60–80 hours per 100 patients to 12–16 hours of oversight, with most processing completing within 24–48 hours—a net 60–80% reduction in effort.
• Handles complex missingness using deep learning, GAN, and RNN methods while preserving statistical power when validated.
• Guardrails include human-in-the-loop approvals, change logs, performance qualification per study, and conservative imputation rules where safety is implicated.
• Metrics to track: query volume and auto-close percentage, cleaning cycle time, imputation error versus ground truth, and dictionary up-version latency.
• Powered by the Data Hub, Automate, AI Medical Coder, and Analytics modules.

Predictive Modeling and Outcome Forecasting

• Dynamic models update as data accumulate to forecast outcomes, safety events, and operational risk.
• Bayesian deep learning provides uncertainty estimates to guide decisions.
• Traditional models using 10–20 variables achieve approximately 70–75% accuracy; AI models analyzing richer feature sets reach approximately 85–90%, but require strict overfit controls and prospective validation.
• Guardrails include model registration, locking features and hyperparameters, drift testing, and uncertainty thresholds for automated recommendations.
• Metrics to track: calibration (Brier score), discrimination (AUROC), decision utility, and uplift versus prior baselines.
• Powered by the Analytics, Data Hub, and Automate modules.

Adaptive Trial Design with ITT-Aware Governance

• Supports response-adaptive randomization, dose-finding, early stopping, and futility and efficacy rules driven by continuously updated evidence.
• Adaptive changes must be reconciled with Intention-to-Treat (ITT) principles; dynamic protocol elements must account for comparability with historical controls.
• Bayesian designs with pre-specified decision rules and uncertainty reporting are recommended.
• Metrics to track: Type I and II error control in simulation, expected sample size reduction, bias under adaptation, and regulator-ready decision logs.
• Powered by the Analytics and Data Hub modules.

Architecture and Technical Foundation

• Connectors ingest data from EHR, EDC, CTMS, IRT, and PRO systems into the Data Hub, which maintains version-controlled, Git-style history.
• Pipelines follow the sequence: ingest → standardize → feature store → model inference → Automate rules → Analytics dashboards.
• Model operations include a registry, versioning, shadow and pilot runs, threshold calibration, and drift monitoring.
• Human-in-the-loop (HITL) loops cover coder approvals, imputation review, consent Q&A capture, and RBM triage.
• Full audit traceability is maintained, with one-click dataset export to biostatistics teams.

Validation, Bias, and Safety Guardrails

• Installation qualification and operational qualification (IQ/OQ) are provided; performance qualification (PQ) is run per study with defined acceptance thresholds for sensitivity, specificity, false positive budget, and alert-to-action SLAs.
• Subgroup performance is monitored; recruitment models are tuned to reduce disparity; exclusions are documented.
• Every automated action logs the input, model version, threshold, decision rationale, and any human overrides.
• Safety-first defaults apply conservative thresholds in early phases, with defined escalation pathways and human sign-off required on critical queries.

Implementation Path

1. Weeks 0–2: connector mapping, baseline metrics capture, and shadow-mode RBM and data cleaning.
2. Month 1: initial validation and review of shadow-