Demand is dynamic. STRIATA predicts demand—population-specific demand; aggregate demand within the boundaries of a given geography; the behavior of a specific individual at a moment in time.
People don’t show up for the care they need. People don’t show up for care when doing so could save their life. In the US, no-shows are estimated to cost the health system $150B each year. Lack of adherence is expensive—and what makes the issue particularly dangerous—is that health systems don’t know where the risk lies, they don’t know who to engage and when.
What if you knew exactly who wouldn’t show up for care? When there’s a schedule, STRIATA predicts individual behavior to build a schedule, appointment by appointment, that addresses gaps before they happen. The software makes the day more predictable with follow-on effects on supply chain and clinical outcomes. When there isn’t a schedule, STRIATA identifies the clients at greatest risk of loss-to-follow-up or drop-out and can predict drop-out without individual-level data.
Identify 95% of children at greatest risk of drop-out
STRIATA is able to correctly identify the 95% of children at greatest risk of missing vaccination in Tanzania, providing health workers with valuable data regarding where to focus their attention. STRIATA allows for targeted interventions to occur before children miss vaccination.
Reach 4x more high-risk patients without increasing the number of people you engage.
Clients in Nigeria and Mozambique can use STRIATA to target the same number of clients with HIV/AIDS as before, but reach 4x more of those at greatest risk of not showing up for care.
Patients received care 86% sooner
Leading US heart hospital uses Macro-Eyes AI to cut the time that patients wait for care from 55 days to 12. Macro-Eyes AI is increasing access to care without adding clinical hours to the day.