Predictive Decompensation in Chronic RPM
Problem: Remote Patient Monitoring (RPM) generates massive data noise, leading to clinician fatigue and missed early indicators of cardiac or respiratory failure.
The Solution: We deploy Gradient Boosted Trees (XGBoost) and LSTM networks to process multimodal streams (SpO2, ECG, weight, blood pressure). The system identifies “silent” decompensation patterns—subtle shifts in vitals correlation—before symptoms become acute.
Data & Integration: Ingests data from IoT wearables via Bluetooth/Cellular; integrates via FHIR R4 with Epic/Cerner to trigger automated nurse alerts.
ROI: 32% reduction in 30-day hospital readmissions for CHF patients and a 50% decrease in alarm fatigue for monitoring teams.
XGBoostFHIR R4Biometric Fusion
Real-Time Kinematic Pose Estimation
Problem: Lack of adherence and incorrect form in home exercise programs (HEP) leads to poor post-surgical recovery and wasted therapist time.
The Solution: Leveraging MediaPipe-based pose estimation, we build browser-based computer vision engines that analyze 33 skeletal landmarks in real-time. Patients receive millisecond-latency feedback on joint angles and repetition quality.
Data & Integration: Standard RGB camera feed; JSON-based session telemetry stored in SQL for therapist review. Integrated into patient portals via WebRTC.
ROI: 45% improvement in HEP adherence and 2.4x faster functional recovery metrics in post-ACL reconstruction cohorts.
Computer VisionPose EstimationWebRTC
Ambient Scribing & SOAP Generation
Problem: Telehealth physicians spend 40% of their time on documentation, leading to high burnout and reduced patient throughput.
The Solution: A HIPAA-compliant pipeline utilizing Whisper-large-v3 for ASR (Automatic Speech Recognition) and Med-PaLM 2 for clinical reasoning. It extracts medical entities (medications, dosages, symptoms) and generates structured SOAP notes.
Data & Integration: Real-time audio streams from virtual visit platforms; output mapped to EHR discrete data fields using HL7 v2 messaging.
ROI: Saves an average of 2.5 hours per physician per day, increasing encounter capacity by 20% without adding headcount.
ASRMed-PaLM 2Entity Extraction
Vocal Biomarker Relapse Detection
Problem: Between tele-psychiatry sessions, patients with bipolar disorder or MDD often spiral into crisis without warning, as self-reporting is unreliable.
The Solution: Analyzing prosody, glottal pulses, and linguistic variety during routine telehealth calls. Our CNN-Transformer models detect acoustic features correlated with depressive or manic shifts.
Data & Integration: Anonymized Mel-spectrograms from session audio; results delivered via clinician dashboard with “Risk Score” heatmaps.
ROI: 18% earlier detection of psychiatric relapse episodes, resulting in a significant reduction in emergency department utilization.
CNN-TransformerProsodic AnalysisBehavioral Health
Autonomous Urgent Care Triage
Problem: Digital urgent care queues are often clogged with non-urgent cases (e.g., refills), delaying care for high-acuity patients like those presenting stroke symptoms.
The Solution: An Agentic AI “front door” that conducts dynamic, clinical-reasoning-based intake. It uses an adaptive Bayesian network to calculate acuity scores and route patients to the appropriate level of care.
Data & Integration: Natural language input, previous EHR history, and real-time waiting room metrics via API.
ROI: 40% reduction in wait times for high-acuity patients and 30% of cases successfully diverted to asynchronous or self-care pathways.
Bayesian NetworksNLP TriageDirect Scheduling
Privacy-Preserving Virtual Trials
Problem: Recruiting diverse patients for clinical trials is hindered by data privacy concerns and the logistical burden of visiting physical sites.
The Solution: We implement Federated Learning (FL) architectures where model training occurs on the patient’s local device (smartphone/tablet). Only encrypted model gradients are sent to the central server, never the raw data.
Data & Integration: Local health app data (Apple HealthKit/Google Fit) and trial-specific digital biomarkers.
ROI: 100% data sovereignty for participants, leading to a 4x increase in recruitment rates and significantly broader demographic representation.
Federated LearningSMPCDecentralized Trials
Multilingual Post-Visit Synthesis
Problem: Discharge instructions are often complex and static, leading to patient confusion and poor post-visit compliance (especially in non-native speakers).
The Solution: RAG-based LLM system that synthesizes the telehealth transcript and EHR data into personalized, literacy-level-appropriate “Next Steps” videos or text in 50+ languages.
Data & Integration: Telehealth encounter summaries and ICD-10 codes. Delivered via SMS/Email or patient app.
ROI: 22% improvement in medication adherence and 15% reduction in follow-up “clarification” calls to the clinic.
RAGHealth Literacy AIMultilingual GenAI
Continuous Sepsis Monitoring in SNFs
Problem: Sepsis in post-acute care (Skilled Nursing Facilities) is often caught too late, as vitals are only checked sporadically by staff.
The Solution: Random Forest classifiers analyzing continuous heart rate variability (HRV), respiratory rate, and movement data from medical-grade wearables to predict SIRS (Systemic Inflammatory Response Syndrome) onset.
Data & Integration: Telemetry data streams; integration with nurse call systems and virtual hospitalist platforms.
ROI: Can detect sepsis up to 8 hours before traditional vitals assessment, reducing mortality rates in high-risk elderly populations by 15%.
SIRS/qSOFARandom ForestReal-Time Telemetry
Architectural Foundation
The Sabalynx Virtual Care Stack
Deploying AI in telehealth requires more than just a model. We build the underlying infrastructure to ensure HIPAA compliance, SOC2 Type II security, and sub-second latency for clinical decision support.
HL7/FHIR
Interoperability