Healthcare AI Use Cases

ClosedLoop AI Use Cases

Providers & ACOs

Appointment No-Shows - Predict patients most likely to miss appointments
Predict patients most likely to acquire infections like sepsis
Predict patients most likely to benefit from periodic check ups
Leakage - Network Integrity - Out of Network
Total Risk: Who will be my most expensive patients this year
ED Over-Utilization: Which of my patients would most benefit from establishing a relationship with a primary care provider?
Readmissions: Which patients are most likely to be readmitted to the hospital?
Transportation: Which patients are most likely to need help with transportation in order to make it to their appointments
Rising Risk: Which patients are likely to see large increases in their overall health risk over the next 30/60 days?
Chronic Disease: Which patients are most likely to develop or progress in the severity of a chronic disease
Medication Adherence: Which patients are most likely to be noncompliant with medications?

Payers and Health Plans

Risk Adjustment - Suspect Diagnosis
Preventable Hospitalizations
Readmissions - Predict patient readmissions
Chronic Disease Onset and Progression - Predict onset or progression of chronic diseases.
Patients with highest overall risk in the next 6 months or year
Trend: Which members are likely to see large increases in their overall health risk over the next three to six months?
Readmissions: Which members are most likely to be readmitted to the hospital?
Medication Adherence: Which members are most likely to be noncompliant with medication plans?
Palliative Care: Which members are most likely to benefit from discussions with a palliative care specialist?
ED Over-Utilization: Which members are most likely to benefit from establishing a relationship with a primary care provider?
Disenrollment: Which members will most likely to disenroll?
Fraud: How do new data sources allow health plans to spot fraud sooner?
Payment Integrity: How can AI help ensure proper reimbursement?

Pharma & Life Science

Biomarkers: Which patients will have an increased success rate based on biological factors?
Drug-Combinations: Which drug combinations are most likely to be successful?
Segmentation: Which groups of patients respond differently to treatment?
Strategy: Which subpopulations should be included/excluded based off of predicted success rates?
Responsive: Which patients are responding to treatment?
Events: Which patients are most likely to experience adverse reactions?
Effectiveness: How will clinical trial results translate into real world effectiveness?
Value: What improvement in outcomes will a new treatment generate over existing therapies?
Switching: Which factors are most relevant in understanding which patients switch drugs?
Marketing: Which physicians can we market to?

Other