My role: Research, Research synthesis, Taskflow, Prototype, Usability Testing
Team: Jinnan Chen, David Cox, Aleena Qaiser, Vivan Gao
Problem Statement
Medical trainees at the University of Michigan at Advanced Cardiovascular Life Support (ACLS) training need a better feedback system on their simulation performances to improve their skill set and pass certifications.
Our Solution
The Game Changer in ACLS Training - A Decade of Grooming Medical Leaders. This product has helped improve the unique training for each medical professional. Allowing for better feedback and training.
Research
The Context
Medical trainees start their simulation in the simulation room as shown in the top image on the right. The simulation lasts for around 15 minutes, after which they have a debrief session in the debrief room where the instructor plays the recording and comments on their performance. This lasts about 5-10 minutes.
Target User Group
Our target group would be aligned with the current m-Team target group which are medical trainees working on improving their ACLS skills by using mTeam VR simulations.
Contextual Interview
For our primary research, we interviewed 4 different ACLS trainees, 2 instructors, and mTeam project director Dr. Popov to gain a deeper understanding of the struggles they might face during the ACLS simulation, especially the debriefing phase.
Synthesis Summary
A critical gap exists in VR medical training: trainees struggle to translate their virtual skills to real-world performance due to a lack of effective feedback. This arises from insufficient data collection, generic instruction disconnected from individual needs, and limited post-training resources for improvement.
Addressing these shortcomings, where traditional methods like mTeam procedures are already fixed, requires innovative solutions that leverage VR's potential for personalized feedback and immersive scenario practice.
To better summarize and synthesize our findings, we created two primary and two secondary personas to help us with our design process.
Primary Personas
Secondary Personas
How might we transform debriefing data into personalized learning insights to enhance each medical trainee's development trajectory and lifesaving skills in an efficient, scalable manner?
Ideation + Iterations
Concept Sketching + Downselection
Based on our findings, we sketched up potential concepts and used the Question, Options, Criteria (QOC) matrix to downselect our final ideas
QOC Downselection
Based on the QOC Analysis, the Dashboard is the best tool to use to address the big problem/question: “How can we develop an improved debriefing system for University of Michigan Medical trainees in (ACLS) training to increase their chances of passing certifications?”.
User Flow
Lo-Fi Prototype
Before building a high-fidelity prototype, we created a low-fidelity version to quickly test the core concepts of our design and identify any major usability issues.
Final Iteration
User testing revealed our debriefing flow was overly complex, despite considering post-simulation needs. The instructor's key tools, simulation, and data insights, were buried within the interface. We responded by redesigning the dashboard with a streamlined user flow and richer data presentation.
To elevate training effectiveness, our final design features three key components: an intuitive onboarding module, a data-rich and interactive debriefing dashboard for instructors and students, and personalized post-debriefing resources customized for each user type.
Onboarding
The onboarding process goes beyond a simple dashboard introduction. We leverage a learner-type model inspired by Lori Persico's research to personalize feedback by the team leader (instructor) during debriefing sessions.
Debriefing Session
Our debriefing dashboard maximizes learning through strategic design. Instant replay video takes center stage for a quick performance review. We surround the video with key indicators highlighting crucial actions and timing while flagged timecodes pinpoint mistake moments for discussion. Instructor commentary bridges the gap between teacher and student, explaining the reasoning behind feedback.
Understanding the power of visuals to tell stories, we crafted glanceable data representations of overall scores, strengths/weaknesses, and skills assessments, giving clear improvement areas. For added context, full case details are readily available.
Finally, introducing friendly competition, our ranking system allows students to track personal progress, compare performance to top peers, and stay motivated to push boundaries.
In the end, our dashboard moves beyond data to craft an engaging, holistic experience. Combining video, metrics, instructor notes, insightful visualizations and gamification, we empower immersive excellence in medical training.
Post Debriefing
For Instructors: Our system gathers comprehensive data from each training session, providing anonymous student feedback and identifying recurring areas of difficulty. This objective data analysis can reveal blind spots in your instruction, allowing you to refine your lectures and address common student misunderstandings with greater precision.
For Students: Our platform offers multiple avenues for tailored support. Schedule individual appointments with instructors to address specific areas of confusion or request additional time to master complex concepts. Detailed dashboards and simulation summaries are accessible remotely via email, enabling convenient review and reflection on individual performance. Furthermore, we encourage constructive feedback from students, which contributes directly to the continuous improvement of the training program for future cohorts.
Interactive Prototype
Take Aways
Reflection:
Structured vs. open-ended interviewing: While structured questions provide valuable data, allowing space for interviewees to explore and share unique insights can yield a deeper understanding of user needs.
Embrace stakeholder feedback: Criticism and feedback from instructors and students are invaluable for fine-tuning the system to meet their specific requirements.
Record and review interviews: Capturing and revisiting interview recordings facilitates a deeper grasp of user experiences and informs design decisions.
Teamwork fuels success: Design thrives on collaboration. Our project highlighted the importance of combining diverse skills and perspectives for a well-rounded and user-centered product.
Next Steps:
Prioritize essential features: Based on user feedback and usability testing, focus on refining and optimizing the most impactful features like the timeline with critical error markers and instructor notes.
Explore AI possibilities: While the current system leans on instructor guidance, investigate how AI-powered analytics can further personalize learning and identify individual student strengths and weaknesses.
Expand data analysis: Deepen the analysis of student vocal interactions and communication patterns to gain a comprehensive understanding of soft skills development and team dynamics within ACLS scenarios.
Pilot testing and user research: Implement the revamped system through pilot testing with real users to gather practical data and feedback, informing further iterations and improvements.
