Designing an Algorithm-DrivenNeonatal
Emergency Simulation(POC)
Project Snapshot
| Industry | Healthcare Education |
| Geography | Confidential (Proof of Concept) |
| Audience | Clinical Practitioners (Midwives / Delivery Room Staff) |
| Delivery Format | Storyline-Based Interactive Simulation (Level 4 Complexity) |
| Modules Delivered | 1 Proof of Concept Module |
| Total Estimated Seat Time | 20–25 Minutes |
| Languages | English |
| Project Duration | RFP Demonstration Prototype |
Impact at a Glance
Algorithm -integrated emergency simulation
Character -driven clinical scenario
60-second cinematic attention gainer
Real-time reference access during decision-making
Level 4 Storyline architecture with branching logic
The Strategic Context
The objective of this Proof of Concept was to demonstrate how a structured clinical resuscitation algorithm could be transformed into an immersive, decision-driven digital learning experience.
Rather than presenting protocol as static content, the goal was to:
Simulate the first minute after birth — a critical survival window
Embed procedural decision-making under time pressure
Allow learners to consult the algorithm dynamically
Reinforce accuracy through corrective feedback
The content foundation consisted of raw PowerPoint files and external resource links from which real-world case studies had to be interpreted, structured, and transformed into cohesive learning journeys.
This required both instructional precision and narrative sensitivity.
Key Challenges & Constraints
1. High Clinical
Sensitivity
Neonatal emergencies require seriousness and realism without emotional overdramatization.
2. Algorithm
Accuracy
Clinical steps had to align precisely with the neonatal resuscitation algorithm framework.
3. Behaviour Under
Pressure
The module needed to simulate urgency while still allowing reflective learning.
4. Technical Sophistication
The POC required advanced Storyline logic while maintaining intuitive navigation.
Our Strategic Approach
Instructional Governance
The POC followed a scenario-based mastery model aligned with Gagné’s instructional sequencing principles.
Analysis
- Identified critical first-minute neonatal intervention steps
- Mapped decisions directly to algorithm checkpoints
Design
- Storyboard-first approach outlining narrative flow, dialogue, and decision logic
- Introduced a central character (Mariam, midwife) to anchor the scenario
- Designed algorithm-reference integration within interaction screens
Development
- Built in Articulate Storyline (Level 4 complexity)
- Embedded real-time algorithm visibility during question answering
- Integrated conditional branching and layered feedback
- Developed cinematic attention gainer (60-second “After Birth” sequence)
Validation
- Internal review for clinical logic consistency
- Feedback loop refinement of interaction flow
Experience Design Innovation
Cinematic Attention Gainer
The module opened with a 60-second emotionally grounded “After Birth” video to establish urgency before introducing formal learning content. This shifted learners from passive cognition to situational awareness.
Illustration & Visual Language
The visual design combined:
- Soft clinical realism
- Clean interface overlays
- Character-based illustration
Mariam, the midwife, served as the narrative anchor, with the story unfolding around her actions in the first 30 seconds of newborn distress. This approach humanised protocol without compromising medical seriousness.
Embedded Algorithm Accessibility
The Neonatal Resuscitation Algorithm remained visible and downloadable within the module.
Learners could:
- Refer to the algorithm before selecting responses
- Cross-check procedural steps
- Receive feedback explicitly tied to algorithm logic
This reinforced evidence-based decision-making rather than intuitive guessing.
Scenario-Based Decision Architecture
Learners navigated clinical decisions such as:
- HR 90 bpm
- Apnoea present
- Selecting next appropriate intervention
Estimated Learning Metrics
(Based on Comparable Clinical Simulation Deployments)
Projected Decision
Accuracy Improvement:
35–50% increase in correct algorithm application
Retention Impact:
Scenario-driven clinical simulations demonstrate up to 30% stronger procedural retention compared to slide-based modules
Confidence
Increase:
25–40% improvement in self-reported emergency response confidence
Engagement
Metrics:
Interactive scenario modules show significantly higher completion and attention retention rates in healthcare training environments
Operational Execution Feedback
Week 01
Storyboard Architecture + Algorithm Mapping
Week 02
Visual Design + Character Development
Week 03
Scenario Build + Branching Logic
Week 04
QA, Refinement & Demonstration Readiness
Impact Beyond Training
(Projected Safety Outcomes)
Stronger Algorithm Adherence
Real-time access encouraged correct protocol sequencing.
Reduced Hesitation in First-Minute Decisions
Scenario immersion improved decisiveness under simulated pressure.
Higher Procedural Confidence
Character-led storytelling increased learner relatability and emotional readiness.
Stronger Algorithm Adherence
Real-time access encouraged correct protocol sequencing.
Key Takeaways
Algorithm Integration Enhances Accuracy
Embedding reference tools during decision-making strengthens protocol compliance.
Storytelling Improves Clinical Engagement
Character-driven scenarios humanise technical processes.
Simulation Builds Confidence
Practice in safe environments prepares learners for real emergencies.
Technical Sophistication Enables Realism
Advanced Storyline logic supports layered feedback and dynamic interaction.
FAQS
Q1. Was this a full course or a demonstration prototype?
This was a structured Proof of Concept designed to demonstrate algorithm integration, branching logic, and scenario-based learning.
Q2. Was the neonatal algorithm directly embedded in the module?
Yes. Learners could access and reference the algorithm while making decisions, reinforcing evidence-based clinical action.
Q3. Was the scenario character-driven?
Yes. The module followed a central character, Mariam, whose decisions guided the learner through the first critical minute after birth.
Q4. What made this technically complex?
The module utilised Level 4 Storyline architecture, including conditional branching, layered feedback, variable-driven progression, and dynamic interface states.
Q5. Can this framework be adapted to other medical protocols?
Yes. The algorithm-driven, scenario-based architecture can be replicated for other emergency or clinical training domains.
Through this proof of concept, Qquench showcases its ability to convert complex clinical algorithms into decision-based simulations that strengthen real-world readiness when time is critical.