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Technology that improves patient outcomes.

Every minute spent on administrative work is a minute not spent with patients. Every delayed clinical decision is a risk. We've built AI systems that help clinicians make better decisions faster, engage patients between visits, and cut the overhead that slows healthcare down. HIPAA compliance isn't an afterthought—it's the foundation.

Overview

Built for healthcare.

We help healthcare organizations improve clinical decision-making, unify fragmented patient data, accelerate telemedicine adoption, and increase patient engagement. Our solutions integrate with existing EHRs and are built on HIPAA-compliant, FedRAMP-authorized infrastructure.

The Challenges

What makes this hard.

01

Clinical Decision Support That Clinicians Actually Use

Decision support systems exist. Clinicians ignore most of them. Why? They slow work down or they're wrong often enough that trust erodes. We've built systems that integrate into existing workflows, surface high-confidence recommendations at the right moment, and explain reasoning clearly. One health system saw clinicians adopt recommendations on 65% of cases where the system's confidence was high—and those recommendations improved outcomes measurably.

02

Patient Data Locked in Incompatible Systems

Your EHR, practice management, imaging system, and lab platform all exist in silos. Patient history is fragmented. Clinicians get incomplete pictures. We've built integrations that unify patient data without creating security vulnerabilities. HL7, FHIR, and proprietary formats all feed into a unified, queryable patient record. Clinicians see the full context.

03

Telemedicine Adoption Stalled by Poor Experience

You launched a telemedicine platform. Usage tanked because it was clunky, slow, or didn't actually reduce friction compared to phone calls. We've built end-to-end telemedicine systems—video, asynchronous messaging, prescription delivery, follow-up scheduling—that patients and providers both want to use. One urgent care network increased telehealth utilization from 8% to 34% of visits within 4 months.

04

Patient Engagement Programs That Don't Drive Compliance

Reminder apps and generic health tips don't change behavior. We've built personalized engagement systems that send the right message at the right time—medication reminders tied to pharmacy fills, preventive care nudges based on clinical guidelines, condition-specific education tuned to individual literacy and preference. Medication adherence improved 23% at one client.

What We Build

How we help.

Strategy

We map clinical workflows, identify bottlenecks, and design AI interventions that fit into how clinicians actually work—not how you wish they worked.

AI + Data

Predictive models for high-risk patients. Clinical decision support that surfaces relevant guidelines and historical outcomes. Real-time patient monitoring from wearables and EHR data.

Software Dev

EHR integrations. Telemedicine platforms. Provider and patient portals. Every interface designed for speed and reliability under clinical pressure.

Cloud + QA

Deployed on HIPAA-compliant infrastructure with encryption, audit trails, and disaster recovery. Tested for uptime, performance, and data integrity. Healthcare can't tolerate downtime.

Learning

We train your clinical and technical teams so they own the systems long-term, not dependent on us.
Use Cases

Where it pays off.

Predicting Patient No-Shows

One health system was losing 15% of scheduled appointments to no-shows, creating gaps in the schedule and delaying care for others. We built a model that predicted high-risk no-shows 48 hours before the appointment using historical attendance, condition severity, transportation barriers, and weather. Automated outreach—calls, texts, logistics support—reduced no-shows by 8 percentage points. Revenue impact: $2.1M annually from improved utilization.

Clinical Decision Support for Sepsis Recognition

Sepsis kills 1 in 5 patients. Early recognition is everything. A hospital deployed our system that monitors vital signs, lab values, and medications in real-time, flagging sepsis risk within the first 6 hours of admission. Clinicians review the alert, and if they agree, sepsis protocols activate immediately. Result: average time to antibiotics decreased from 2.8 hours to 1.1 hours. Mortality rates improved significantly.

Patient Risk Stratification for Remote Monitoring

Discharging high-risk patients created readmission problems. Low-risk patients in the hospital were tying up beds. We built a risk model that identified patients who could be safely managed at home with remote monitoring—vitals via wearable, asynchronous provider check-ins via app, medication adherence monitoring. Readmission rates for monitored patients dropped 19%. Hospital bed utilization improved.

Urgent Care Triage and Demand Forecasting

UrgentCareX (our product) powers triage and throughput optimization. Patients report symptoms through an app. Our system estimates acuity, recommends wait time expectations, and routes them to the right provider type. We also forecast demand by hour and day, so staffing matches expected volume. Wait times dropped 34% without hiring more staff.

The Stack

Technologies we ship with.

HL7/FHIR
Epic API
Cerner API
Athena API
Google Cloud Healthcare API
AWS HealthLake
Time-series Models
NLP
Mobile SDKs (iOS/Android)
FedRAMP Cloud
PostgreSQL
Selected Work

Proof, not promises.

Case Study

Pauseitive

Organizational management system with mental wellness focus

Case Study

Forest Fusion

Environmental monitoring platform with health analytics

FAQ

Questions, answered.

How do you keep patient data secure and HIPAA-compliant?

HIPAA compliance is baked into the architecture. Data is encrypted at rest and in transit. Access is role-based and logged. Infrastructure is deployed on FedRAMP-authorized cloud with regular security audits. Your legal team reviews our compliance documentation before deployment.

Can clinicians override AI recommendations?

Absolutely. The system is a decision support tool, not an autonomous decision-maker. Clinicians see the recommendation, understand the reasoning, and decide whether to act. The system learns from their feedback to improve future recommendations.

How do you handle patient consent and data sharing?

Consent is explicit and granular. Patients understand what data is used and for what purposes. We build consent management into the system so preferences are enforced automatically. You control which patient groups opt into features like predictive monitoring.

What if your model makes a clinically wrong prediction?

All predictions are logged with their reasoning and actual outcomes. We monitor model performance in production and retrain when drift is detected. We also run regular audits to identify potential biases, especially across different patient populations.

Ready to make clinicians more effective and improve outcomes?

We'll review your clinical workflows, data infrastructure, and compliance requirements. Then we'll show you where AI creates the biggest impact without disrupting how you work.