Context and Problem
Hybrid care programs often struggle at scale because the work is split across teams, locations, and systems. When ownership is unclear or status is not visible, tasks sit in queues, patient follow-through drops, and staff spend time chasing context instead of delivering care.
[Company] needed a clearer operating system for hybrid workflows, with consistent handoffs between remote teams and in-office staff. The pain was not a single feature gap. It was a workflow problem:
- Tasks and handoffs lived across multiple tools and were easy to miss
- Status was unclear, so teams duplicated work or waited too long
- Operational reporting existed, but did not drive action
- Integrations introduced data inconsistency and edge cases
Users and Constraints
Primary users
- Remote care coordinators managing patient panels
- In-office staff supporting visits, intake, and follow-up
- Admins responsible for throughput, staffing, and operational consistency
Constraints
- Multiple workflows depending on clinic and care program
- Integration requirements with EMR and EHR systems
- High volume work, prioritization mattered more than perfect completeness
- HIPAA-aware design and audit trails required
What I Shipped
Handoff State Model
- Defined clear workflow states (example: new, in progress, waiting, escalated, complete)
- Made ownership explicit, with visible assignment and reason codes
- Reduced ambiguity by standardizing transitions between remote and in-office roles
Operational UX and Queues
- Role-based queues that show what to do next, ordered by urgency
- Action-first UI: message, call, assign, resolve, and document without extra navigation
- Task aging and escalation patterns to prevent stalled work
Visibility and Reporting
- Program-level dashboards for throughput, follow-through, and friction points
- Clear operational metrics to support staffing decisions and process improvement
- Audit-friendly trails for key actions and handoff transitions
Integration Hardening
- Workflow-aware integration behaviors to reduce mismatches and duplicates
- Guardrails for missing fields, partial syncs, and inconsistent identifiers
- Patterns for handling edge cases without breaking the primary workflow
How AI Showed Up
AI was used as an internal accelerator for analysis and workflow design, focused on reducing time spent synthesizing feedback and identifying friction patterns. Where AI-assisted features were explored, outputs were draft-only and required human confirmation.
- No automated outreach without human approval
- Recommendations were auditable and explainable
- Outputs supported the operator, not replaced operator judgment
Outcomes
- [X]% reduction in stalled tasks over [timeline]
- [Y] days faster time to first touch for key handoff stages
- [Z]% reduction in duplicate outreach or rework due to clearer ownership and visibility
- Improved staff satisfaction due to less context chasing and fewer unclear handoffs
My Role
- Owned workflow design, requirements, and sequencing of releases
- Partnered with engineering on the handoff state model and queue behavior
- Worked with operations teams to define SLAs, escalations, and usable reporting
- Validated changes through user feedback loops and data review
What I Learned and What I Would Do Next
- Visibility beats motivation: clear ownership and status solve more than reminders.
- Queues must be actionable: a dashboard without actions becomes a museum.
- Integrations need guardrails: data inconsistency is normal, products must tolerate it.
Next: proactive risk scoring for stalled handoffs, smarter escalation routing, and deeper measurement of follow-through loops.