Care Transitions & Coordination

A clinical risk model is explainable enough to trust when every risk label shows the supporting data, applied rules, reasoning factors, and recommended actions. cliexaProtocols evaluates event history, medications, labs and diagnostics, comorbidities, social and behavioral risk, and patient-reported outcomes, weighting each domain based on the encounter and available information. Outputs identify why the patient has a given risk state and why an intervention aligns with a guideline. Inference activity is logged, auditable, reproducible, and linked to underlying data; clinicians review the advisory output and retain decision authority.

Opioid use disorder risk can be flagged before prescribing by combining validated assessments, patient-record data, and explainable clinical rules into a real-time composite score. cliexa uses SOAPP-14, DAST-10, AUDIT, GAD-7, PHQ-9, the Pain Disability Index, and the Current Opioid Misuse Measure, supported by an XGBoost classifier trained on real patient records. Patients are stratified into minimal, low, medium, or high risk, with a clinical action protocol for each tier. This approach was qualified as a solution on the Mayo Clinic Platform in January 2026.

Clinical decision support changes provider behavior when its thresholds reflect real practice and every recommendation includes clear, case-specific reasoning. cliexaProtocols analyzes historical decisions and outcomes, calibrates rule thresholds to the clinic's observed practice patterns, and flags deviations between written protocols and day-to-day care. Its outputs explain the current risk state, contributing factors, triggered actions, and applicable guideline or policy. Because recommendations arrive with relevant context, they can inform scheduling, resource allocation, ancillary service eligibility, documentation, and billing workflows.

Remote monitoring built on cardiology algorithms turns physiologic readings and reported symptoms into defined risk states, escalation logic, and automated case alerts. cliexa's cardiovascular monitoring algorithms were co-developed with the American College of Cardiology beginning in 2018 and cover atrial fibrillation, hypertension, heart failure, and coronary artery disease. Each includes clinical assessment criteria, monitoring signals, symptom tracking, and threshold-based escalation. For hypertension, the algorithm averages blood pressure across seven days with at least 12 readings, then stratifies the patient as Stable, Attention, or High Risk, including urgent thresholds.

Remote patient monitoring clinicians act on is built by converting device data into clinically relevant alerts within the workflow where care decisions occur. cliexa integrates with more than 300 Bluetooth-enabled medical devices measuring blood pressure, heart rate and rhythm, body weight, physical activity, glucose, and oxygen saturation. Condition-specific algorithms evaluate those signals and use risk states to trigger patient case alerts and real-time escalation through cliexaConnect inside the EMR. The output is advisory, explainable, and reviewed by clinicians, who retain decision authority.