Healthcare | Lucid Sphere AI

General Analytics Features

Automated ingestion of structured and unstructured data from various sources (e.g., ERP, CRM, patient portals).
Cost-optimized storage of raw data in a data lake.
Batch and real-time healthcare data processing.
A healthcare data warehouse for analytics querying and reporting.
Automated data governance and data quality management.
Data storage, transfer, and access mechanisms compliant with the required regulatory standards (e.g., HIPAA, GDPR).
Voice and image recognition to streamline data input and interpretation.

Data visualization via customizable dashboards and self-service reports.
Automated KPI calculation (e.g., HCAHPS, ALOS, readmission rate).
Automated data segmentation (e.g., by patient demographics, health outcomes).
Continuous KPI and patient state monitoring.
Instant notifications and alerts (e.g., on fraud detection, changes in patient vitals).
Identifying trends, dependencies, and issue root causes in the healthcare data.
Forecasting future health outcomes and trends.
Smart recommendations on improving business processes and treatment plans.

Patient data analysis, including demographic data, clinical data, and patient history.
Continuous monitoring of PGHD collected from wearables, sensors, patient apps, daily rounds, etc.
Notifications & alerts on changes in a patient’s state (e.g., abnormal vitals).
Identifying trends and dependencies between treatment-related activities, lifestyle changes, and patients’ vital parameters.

Automated calculation of health outcomes KPIs, including mortality rates, readmission rates, HRQoL, PROs, and more.
Automated segmentation of outcomes by demographic factors, physician, facility, condition, etc.

Identifying trends and dependencies between health outcomes and treatment types, medications, length of stay, and other possible variables.

Forecasting possible health outcomes (e.g., readmissions, patient volume, high-risk patients).

Automatic calculation of facilities and care KPIs (e.g., ER waiting time, bed occupancy rate, patient satisfaction scores).
Equipment KPIs (e.g., asset utilization rate, lifespan).
Pharmaceuticals KPIs (e.g., medication adherence rate, inventory turnover rate).
Laboratory KPIs (e.g., turnaround time, cost per test, volume of unnecessary tests).
Personnel KPIs (e.g., nurse-to-patient ratio, patient load, turnover rate).
Supply chain management KPIs (e.g., supplier performance, stock-out rate, order accuracy).
Identification of operational bottlenecks (e.g., long patient wait time, delayed prescription processing) and root cause detection.
Prediction of demand for specific services and resources (e.g., equipment, surgical facilities, medications, staff).

Continuous monitoring and analytics of the cash flow and treatment expenses, including care delivery and overhead costs.
Automated segmentation of costs (per episode, condition, patient group), outstanding payments (e.g., per department, facility), actual ROI by the type of investments.
Notifications & alerts on due and overdue payments, potential payment or insurance fraud.
Identifying trends and dependencies between costs and operational processes, reimbursement policies, and health outcomes.
Forecasting of future costs per period, expense type, etc.
Predictive modeling to identify the financial impact of planned actions (e.g., changes in reimbursement policies, supplier change).
Smart recommendations on cost-saving opportunities and pricing optimization without negatively affecting health outcomes.

Alerts on potential health risks and complications (e.g., allergies, drug interactions, adverse effects).
Intelligent diagnostic assistance (clinical decision trees, differential rankings of potential diagnoses based on patient data).
Laboratory findings analysis.
Medical image analysis.
AI-powered treatment recommendations (e.g., medication dosage calculation, custom treatment plans based on patient history).
Clinical guidelines adherence checks and alerts.
CDS for medical specialties (tailored decision support for dermatology, ophthalmology, cardiology, etc.) and interdisciplinary collaboration support for complex cases.

Automated calculation of patient engagement KPIs, including patient dropout and portal engagement rates, patient loyalty, etc.
Identifying trends and dependencies between engagement levels and various dimensions (e.g., facilities, therapeutic departments, disease statuses, age); engagement levels and engagement activities (e.g., follow-up calls, preventive screening reminders).
Smart recommendations on improving patient engagement rates.