2026 Ascend Solutions Team

The Challenge

Every year the Hajj pilgrimage draws roughly two million worshippers to Mecca, and the intense crowd movement and traffic flow that come with it raise disease transmission risk while placing enormous demand on the healthcare systems serving them. The organizations caring for those pilgrims were working from data scattered across separate systems, which drove higher operational costs, delayed decisions, and limited visibility into what was actually happening on the ground. That fragmentation compounded two related pressures: facility overcrowding and increased emergency demand during the busiest ritual days, and resource allocation delays that left beds, staff, and medical supplies poorly matched to real need.

The Solution

The student team's first decision was structural. Rather than force the sponsor's growing collection of clinical, facility, and environmental data into rigid relational tables, the team built a knowledge graph, where information is represented as connected nodes and relationships that absorb new sources without a redesign. The ontology links Patient to Encounter, and Encounter in turn to Diagnosis, Facility, and Date, with Date carrying weather onto every record so environmental conditions become queryable alongside clinical ones. Each additional source strengthens the graph rather than straining it, giving Ascend Solutions a foundation that scales as new data and relationships are introduced.

On top of that structure, the team delivered a Power BI situational awareness dashboard covering roughly 347,000 encounters across 342 facilities. It tracks healthcare demand across the Hajj timeline, where daily encounters climb from roughly 2,500 to a sustained plateau near 23,100 before tapering off, and it breaks disease burden down by phase, where the single most common diagnosis code — an upper respiratory condition — accounts for 16,380 encounters during the Hajj ritual phase alone. Demand concentrates at a 9:00 a.m. peak hour.

The Impact

Three findings anchor the team's recommendations: demand surges concentrated in the peak arrival and ritual phases, clear facility pressure where certain sites consistently absorb more volume than others, and identifiable peak operational hours showing when staffing and resources are needed most. The team recommended expanding the underlying data to include variables such as staffing levels, facility capacity, and patient history; building simulation capability to evaluate scenarios like staffing shortages, bed capacity constraints, and disease outbreaks before they occur; and using the platform to support day-to-day planning. Together these steps improve resource allocation, reduce operational risk, and move healthcare operators from a reactive posture to a proactive one during large-scale events like Hajj.