[fluentform id=”3″]
Amazon Medical · Healthcare Systems
Scan to order: Prescription-Led Ordering Capability
Context
Prescription-based ordering is a core workflow in online pharmacy, yet handwritten prescriptions remain difficult for customers to interpret. Early prescription upload flows reduced search effort but relied on asynchronous manual digitization, introducing waiting, uncertainty, and high abandonment despite strong purchase intent.
The opportunity was to move from an “upload and wait” model to a real-time, confidence-led ordering experience, without compromising regulatory or clinical safeguards.
My Role
Led experience and system design for Scan to Order, partnering with product, engineering, and compliance teams to define interaction models, trust safeguards, and failure-handling strategies for a machine-assisted prescription workflow.
System Overview
Scan to Order was designed as a single, cohesive capability, composed of tightly coupled layers rather than sequential rollout phases.
The system focuses on interpretation, verification, and trust preservation—the three most failure-prone points in prescription-led commerce.
Interpretation Layer
- Turning handwritten prescriptions into actionable signals
- Interpretation was redesigned to be real-time and assistive, rather than queued and authoritative.
- Prescriptions are scanned immediately, with the system proposing ranked medicine interpretations instead of a single prediction. This preserves speed while avoiding false certainty.
- Customers move directly from upload to action, replacing idle waiting with visible progress.
Verification Layer
Balancing accuracy with cognitive load
Verification balances correctness with usability by allowing customers to confirm or adjust system suggestions, while retaining human review as the final approval gate.
This shared responsibility model improves accuracy without requiring customers to fully interpret prescriptions themselves.
Trust & Safeguards Layer
Containing failure in a high-risk domain
In pharmacy, incorrect recommendations are trust-breaking events, not recoverable UX errors.
Failure was treated as a first-class system state, with defined validation checkpoints, controlled substitution rules limited to safe equivalence cases, and transparent correction paths.
Errors are absorbed and corrected without penalizing the customer or blocking safe progress.
Scalability & Future Readiness
Designed as a capability layer, Scan to Order shifts human review to exception handling and scales without linear cost growth.
The same foundation can support repeat ordering from documents, diagnostic recommendations, structured health record conversion, and other document-led commerce workflows.
Why This Work Matters
Scan to Order reframes pharmacy ordering from a search problem into a confidence problem.
By combining automation, customer participation, and human oversight, the system reduces waiting, preserves correctness, and scales responsibly within a regulated healthcare environment.
Why This Enabled?
- Enabled a phased rollout without breaking regulatory trust
- Created a system foundation teams could extend safely over time
- Aligned pharmacy, clinic, and diagnostics experiences under a shared structure.
Off to holiday AirAsia (I5)
Off to holiday AirAsia (I5)
Phoenix Edu Solutions
Phoenix Edu Solutions
National Law School of India University