AI agents in healthcare are autonomous, intelligent systems that perform complex tasks. They analyze data (EHRs, sensors), make decisions, and act—like virtual staff. 22Software’s agentic AI coordinates diagnostics, automates workflows, and predicts risks, boosting efficiency and care quality while reducing human error and burnout. Think: tireless, precision-driven digital teammates.
Meet patients on their terms: voice, text, app, or web. Our AI voice agents and multichannel assistants handle appointments, triage, and follow-ups 24/7. Proactively engage at every touchpoint—scheduling, reminders, post-discharge check-ins—ensuring seamless, responsive care while freeing staff for critical interactions.
AI agents intelligently manage intricate scenarios: symptom analysis, multi-condition coordination, and urgent triage. They parse nuanced language, access real-time EHR data, and escalate safely. 22 Software’s development can ensure coordinated AI agents deliver consistent, compliant, and empathetic interactions—transforming complexity into clarity and trust.
In high-acuity, high-volume environments, AI agents optimize ER triage, predict ICU admissions, and automate discharge workflows. They coordinate bed turnover, staff allocation, and sepsis surveillance—freeing clinician hours for critical care while reducing operational costs. Ensures compliance across 200+ evidence-based protocols.
Streamline repetitive workflows across multisite networks: Voice-enabled AI agents manage scheduling, pre-visit screenings, and chronic care follow-ups. Automate administrative tasks, reduce no-shows through smart reminders, and maintain consistent patient engagement—all without overburdening limited clinical staff.
Prevent prescription errors and optimize inventory: AI agents flag dangerous drug interactions, auto-refill medications via patient preference profiles, and predict supply chain fluctuations. Achieve dispensing accuracy and reduce excess stock costs through real-time demand analytics and automated reordering.
Accelerate breakthroughs in precision medicine: Agentic AI processes millions of clinical trial datasets, identifies biomarker patterns, and simulates drug interactions. Automate literature synthesis, patient cohort matching, and regulatory reporting—shortening research cycles by 6-18 months while maintaining audit-ready compliance.
| Feature | Traditional automation | Al agents |
|---|---|---|
| Learning capability | Minimal learning, rule-based processes | Adaptability and continuous improvement |
| Decision-making | Limited to predefined rules | Capable of making informed decisions |
| Complexity handling | Struggles with complex tasks | Excels at managing complex healthcare tasks |
| Patient engagement | Basic interaction | Advanced conversational capabilities |
Healthcare AI agents autonomously perform complex tasks (diagnostics, EHR analysis) using decision-making capabilities, while chatbots handle basic Q&A. Agents integrate real-time data, learn continuously, and act across systems—like a virtual clinical team member versus a scripted responder.
Typically 8-16 weeks, depending on complexity. Pilot agents (e.g., appointment scheduling) deploy in 4-6 weeks. Full-scale solutions (predictive analytics) require 12+ weeks for data integration, HIPAA validation, and staff training.
Yes. Our agents connect seamlessly to Epic, Cerner, and other EHRs via HIPAA-compliant APIs, enabling real-time data sync for automated charting, risk alerts, and treatment suggestions without disrupting workflows.
Agents must enforce end-to-end data encryption, strict access controls, audit trails, and no-PHI storage policies, with regular penetration testing. We guarantee compliance through signed Business Associate Agreements (BAAs).
AI transforms diagnostics (imaging analysis, early detection), operations (automated billing), patient care (personalized treatments, virtual assistants), and prevention (outbreak forecasting, risk prediction) across clinical and administrative workflows.
Predominant tools include machine learning (predictive analytics), natural language processing (clinical notes/voice assistants), computer vision (medical imaging), and robotic process automation (claims/scheduling), each addressing specific clinical or operational needs.
Key types are reactive agents (real-time alerts), goal-based agents (objective-driven tasks like sepsis reduction), learning agents (self-improving via data), and utility-based agents (outcome optimization like OR scheduling).
Predictive analytics dominates, forecasting patient deterioration, readmissions, and operational demands like staffing needs through real-time data modeling.
