Use Case 1
The “AI Triage & Clinical Assistant”

The client’s doctors were overwhelmed by high patient volume. Consultations were rushed (averaging 7 minutes), leading to a 15% misdiagnosis rate on initial screening. Furthermore, 40% of the doctor’s time was spent typing notes into the EHR after the call. The platform faced a high drop-off rate because patients had to wait hours for a simple “first response” triage.

AIBI-Studio integrated a Conversational Clinical Agent powered by secure LLMs:

1. Automated Pre-Screening: Before seeing a doctor, patients engaged with an AI Triage Agent that asked dynamic, clinically relevant questions based on reported symptoms (e.g., “How long have you had this fever? Is it accompanied by a rash?”).

2. Clinical Summarization: The AI listened to the doctor-patient consultation in real-time (using speech-to-text) and automatically generated structured clinical notes (SOAP notes), prescription drafts, and referral letters for the doctor to approve.

3. Drug Interaction Safety Net: As the doctor drafted a prescription, the AI instantly cross-referenced the patient’s history and flagged potential adverse drug interactions or allergies.

• Patient Throughput: Increased by 400% as administrative time per visit dropped from 15 minutes to 3 minutes.

• Safety: Reduced the risk of misdiagnosis and prescription errors by 70% due to the automated safety checks.

• Doctor Satisfaction: Physician burnout scores decreased significantly as the “pajama time” (working on notes at night) was eliminated.

Use Case 2
The “Remote Patient Guardian” (IoT + AI)

The provider managed 5,000 elderly patients with chronic conditions (heart failure, diabetes) at home. They relied on manual check-ins and basic threshold alerts (e.g., “High Blood Pressure”). This reactive approach meant that by the time an alert was triggered, the patient often already needed hospitalization. Re-admission rates were high (22%), costing the provider millions in penalties and unreimbursed care.

Leveraging the Smart24x7 IoT heritage, AIBI-Studio built a Predictive Health Analytics Engine:

1. Multi-Sensor Fusion: The system ingested real-time data from various home devices: pulse oximeters, connected scales, wearables, and sleep monitors.

2. Predictive Decomposition: Instead of simple thresholds, the AI used Time-Series Forecasting (Prophet/LSTM models) to detect trends. For example, it could identify a slow, steady weight gain combined with decreasing activity levels—a subtle early sign of fluid retention in heart failure patients.

3. Proactive Intervention Workflow: When a “deterioration trend” was predicted, the Agentic AI autonomously scheduled a preventive check-in call or alerted the care team to adjust medication before the patient became critical.

4. Anomaly Detection: The system filtered out “noise” (e.g., a sensor falling off) from genuine health anomalies, reducing false alarms that fatigued nurses.

• Hospital Readmissions: Dropped by 35% within 6 months, saving the provider significant penalty costs.

• Nurse Productivity: Care coordinators could handle 2x the patient load because they focused only on high-risk patients identified by the predictive radar.

• Patient Peace of Mind: Patients reported feeling “watched over” 24/7, leading to higher adherence to care plans.