Healthcare Analytics Consulting

to identify treatment anomalies and trends.

Sample reports:

• Variations of outcomes based on demographic factors (age, income level, lifestyle).
• Variations of outcomes based on a health organization’s internal factors (a specific physician, treatment, facility, and more).

Data sources:

• EHR / EMR.
• ADT.
• Department-specific systems (Cardiology, Dentistry, Neurology, etc.).
• Disease management/population health management.

to enable constant monitoring of patients’ health status, identification of irregularities, treatment plan progress evaluation, early recognition of symptoms of complications, etc.

Sample reports:

• Blood glucose levels (for diabetes patients).
• SpO2 level changes (for COPD patients).
• Overall trends on patients’ nutrition, hydration, temperature, weight, blood pressure, etc.

Data sources:

Mobile patient apps and portals for:

• Chronic condition management.
• Appointment scheduling.
• Medication intake tracking.
• Post-surgery recovery support, etc.

to define the expenses for treating particular conditions or patients groups.

Sample reports:

• Variations of expenses for treating specific conditions.
• Relationships between patient outcomes and costs covering certain periods with insights on the results of changes in the care process.

Data sources:

• EHR / EMR.
• LIS.
• RIS.
• Department-specific systems.
• Accounting system / ERP.

to enable financial planning and optimization.

Sample reports:

• Expected cash flow based on RCM data.
• Actual cash flow.
• Outstanding payments by specific payers (departments, facilities, diseases).
• Actual ROI by the types of investments (new facilities, medical equipment, such as CT or MRI, etc.).

Data sources:

• ERP or FMS.
• Revenue Cycle Management.

to calculate and track operational KPIs (e.g., for managing labs, facilities, equipment, scheduling), identify process bottlenecks and their reasons, predict demand for services and assets.

Sample reports:

• Inventory turnover rate.
• Nurse-to-patient ratio.
• Billable tests versus performed tests.
• Supplier performance rate.
• Staff demand for the next week.

Data sources:

• EHR.
• HR software.
• LIMS/LIS.
• Practice management software.
• Inventory management systems.
• SCM systems.
• Facility management systems.

to track patient satisfaction, identify service and care gaps, and implement relevant improvements.

Sample reports:

• Patient portal engagement rate.
• Patient dropout rate.
• Engagement activity efficiency.

Data sources:

• Patient portal.
• Telemedicine app.
• Patient surveys.

to support clinical decision making with ML- and AI-generated output.

Possible output:

• AI-powered treatment medication dosage recommendation.
• Custom treatment plan.
• Alert on possible complications and health risks.

Data sources:

• EHR.
• LIS.
• RIS.
• Wearable devices.

To monitor enrollment rates and protocol adherence, identify patterns in trial findings, predict workforce and IP demand, conduct post-market surveillance.

Sample reports:

• Trial sites overview for study startup (e.g., patient demographics, disease prevalence, key risk index).
• Trial progress monitoring (e.g., drop-out rate, last patient last visit).
• Pharmacology parameters (e.g., Cmax, Tmax).

Data sources:

• CTMS + EDC.
• LIMS/LIS.
• IRT/RTSM software.
• EHR/EMR + PACS.
• RPM software.

for transparent performance evaluation.

Sample reports:

• Bed utilization efficiency.
• Equipment utilization rates.
• Operating rooms utilization.
• Medication use.
• Clinical HR analytics (employee qualifications, certifications, hiring, and more).

Data sources:

• PMS.
• EHR.
• LIS.
• RIS.
• Workforce management system.
• Department-specific systems.