Proof
/ Healthcare ProvidersAutomated Patient Journey for a Healthcare Provider
Business situation
A reference hospital system in Latin America conducting 15,000 outpatient visits monthly with significant patient churn and cross-sell leakage.
The challenge
The hospital lacked visibility into patient journey leakage — it could not predict which patients were likely to abandon care, not attend booked appointments, or decline follow-up services.
Why it matters
Quantum Rise deployed a multidisciplinary squad to develop three analytical workstreams: predictive NPS to anticipate detractors, propensity-to-no-show modelling, and real-time analytics pipelines to capture planned exams.
What Quantum Rise delivered
The QR/OS™ approach in practice
- • 25 algorithms leveraging 680 variables • Predictive NPS model (ML to anticipate detractor patients and root causes) • No-show propensity model (deep learning) • Near real-time data pipelines for exam conversion • 20 patient outreach campaigns executed, converting 1,100+ diagnostic exams and mitigating 299 no-shows
Measurable outcome
15–18% increase in exam retention. 25 algorithms developed across 680 variables.
Enabled proactive patient engagement, reduced care abandonment, and increased revenue from planned exams.
What became repeatable
Predictive patient journey engine: NPS prediction + no-show propensity + real-time pipeline — reusable across any outpatient healthcare system.