ZappQ Flow

ZappQ Flow is the operational backbone of the hospital platform, designed to centralize the management of patient queues, doctor availability, and outpatient workflows. It acts as the definitive engine connecting hospital staff actions with patient interfaces, ensuring that every token assignment and status update is effectively coordinated across the entire ecosystem. The legacy system was a rigid MVP built with hardcoded logic that could not adapt to diverse hospital queue models or scale to support complex operations. It faced significant reliability issues due to fragmented workflows, frequent data inconsistencies, and a lack of necessary safeguards. These structural limitations rendered the existing codebase unsalvageable, creating a hard ceiling on operational capability. I led the complete system rebuild, redesigning the core architecture to introduce configurable workflow logic and deterministic token lifecycles. This new approach restructured operational state management to enforce strict valid transitions and prevent human error. The result was a scalable operational foundation that ensures precise real-time synchronization across nurse applications, doctor dashboards, and patient views.

The legacy queue management system created significant operational and scalability challenges:

• Rigid workflow logic that could not adapt to different hospital queue models
• Confusing interface and high cognitive load for nursing staff
• Limited operational control over doctor availability, token lifecycle, and queue state management
• No structured visibility into queue performance and operational metrics
• High operational dependency on manual coordination
• Architecture limitations preventing scalable hospital onboarding

These issues reduced operational efficiency, increased staff friction, and constrained platform scalability.

I owned the product definition, workflow architecture, and execution planning for ZappQ Flow, defining how hospital queue operations function across system surfaces. My responsibilities included:

• Defining workflow models, queue logic, and token lifecycle behavior
• Writing detailed PRDs covering operational states, workflows, and system interactions
• Designing the system interaction model across nurse interfaces, patient apps, and admin systems
• Translating operational requirements into structured product and engineering specifications
• Coordinating implementation execution and validating workflows through testing
• Acting as the primary product owner responsible for system functionality and operational correctness

ZappQ Flow was defined as the operational backbone powering hospital queue management and real-time workflow coordination. I designed the system interaction model governing how queue state changes, doctor availability, and token lifecycle events propagate across dependent platform surfaces. The system architecture defined how operational actions performed by hospital staff are created, processed, and synchronized across:

• Nurse interfaces for real-time queue control and workflow management
• Patient applications for live token visibility and operational status updates
• Administrative dashboards for operational monitoring and system oversight

• Replaced the legacy queue management system by defining and delivering a redesigned architecture with configurable workflow models, structured token lifecycle logic, and real-time operational synchronization across hospital systems.
• Workflow Architecture Redesign: Designed configurable workflow logic enabling hospitals to operate based on their specific operational models.
• Operational State Management: Introduced structured doctor availability, token status, and operational state management.
• Workflow Logic Definition: Defined deterministic operational flows and guardrails to prevent human error and invalid state transitions.
• Token Lifecycle and Queue Control: Defined structured token lifecycle handling including assignment, completion, and exception handling.
• Real-time State Propagation: Defined the system interaction model ensuring instant queue updates across nurse and admin dashboards.
• Data Consistency Architecture: Established a single source of truth for queue state to eliminate synchronization errors.

Led the rebuild of the legacy queue management system into a scalable operational platform while maintaining continuity of live hospital operations. Execution leadership included:

• Leading the system redesign from concept definition through production-ready implementation
• Making architectural product decisions affecting workflow structure and operational behavior
• Driving execution under live operational constraints without disrupting hospital workflows
• Maintaining execution momentum and delivery quality across constrained engineering capacity
• Establishing a scalable operational foundation enabling future hospital onboarding and platform growth

• Replaced the legacy queue management system with a redesigned architecture supporting scalable, real-time hospital operations and flexible workflow models
• Removed manual coordination dependencies by automating token status transitions and queue state updates
• Established operational infrastructure enabling platform expansion and hospital onboarding
• Enabled real-time operational synchronization across platform surfaces
• Established a scalable structural foundation supporting independent hospital workflow configurations