Redesigning Patient Support Workflows Around AI Segmentation and Adherence Forecasting
Patient support triage redesigned around two production models - segmentation to group patients by likely barriers, and adherence forecasting to flag drop-off risk - so care teams intervene earlier and route effort where it matters most.
The Challenge
- Patient support teams could not tell in advance which specialty-medicine patients were most at risk of never starting therapy or dropping off
- Adherence signals sat in fragmented clinical, dispensing, and program-enrolment systems with no common patient view
- No forecasting layer existed - risk was discovered after a patient had already lapsed
- PSP care-team capacity was spread evenly across cohorts, not weighted to highest-risk patients
- Time to therapy for [INSERT specialty programs or drug categories] was a stated business priority for [INSERT owning function or executive]
Approach
- Redesigned the patient-triage workflow around two models feeding the same care-team queue
- Built a segmentation model to cluster patients by likely adherence barriers (clinical, behavioural, access-related)
- Built an adherence-forecasting model to score drop-off risk for each patient across the therapy timeline
- Integrated clinical and program data into a unified feature store designed to generalize across demographics and specialty programs
- Routed high-risk patients to earlier and higher-touch outreach; routed lower-risk cohorts to lighter self-serve pathways
- Embedded model outputs into care-team tooling so scoring drives action, not a separate dashboard
What Was Delivered
- Segmentation model clustering patients by clinical and behavioural attributes into actionable cohorts
- Adherence-forecasting model producing per-patient drop-off risk scores across the therapy timeline
- Feature engineering pipeline on Microsoft Fabric spanning ingestion, cohort assignment, and risk scoring
- Power BI reporting layer surfacing cohort composition and drop-off risk to PSP leadership and care teams
- Deployment pattern designed to extend across specialty programs without rebuilding the data layer
Business Impact
Bayshore’s AI initiative has introduced a new level of precision into its patient support programs.
By enabling data-driven triage and outreach, the organization is better positioned to intervene early with at-risk patients.
Segmentation and forecasting models running in production on Microsoft Fabric, extensible to additional Bayshore programs without rebuild.
The scalable model infrastructure has also improved the efficiency of PSP operations by focusing care team efforts where they are most needed.
These early outcomes establish a foundation for broader analytics adoption across Bayshore’s national care network.
Microsoft Fabric - Power BI - Segmentation model - Adherence forecasting model
Through AI, we're not just predicting patient needs; we're actively shaping a future where every individual receives the right care at the right time, enhancing the quality of life for all Canadians.
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