MedShift: Research-Based Medication Memory App

Research-to-Application Translation

Overview

MedShift is a concept app demonstrating how cognitive neuroscience research can be translated into practical health tools. The app applies Event Memory Retrieval and Comparison (EMRC) theory to address a common problem: patients forgetting medication changes and accidentally taking old medications.

This interactive demo showcases the core UX flow, explaining the cognitive science principles at each step.

The Problem

When medications change, patients often:

• Forget they switched medications
• Take the old medication by mistake
• Can’t remember which medication is current
• Experience confusion at the pharmacy

Traditional reminder apps simply notify patients to take medication—they don’t address the underlying memory interference problem.

The Science: EMRC Theory

MedShift is based on Event Memory Retrieval and Comparison (EMRC) theory (Wahlheim & Zacks, 2024, Trends in Cognitive Sciences), which describes how the brain naturally updates memories:

Key Principles

1. Retrieval: Actively recalling old information before encountering new information
2. Prediction: Generating expectations based on past experience
3. Prediction Error: Detecting mismatches between expectation and reality
4. Integration: Encoding the full change sequence into a “recursive representation”

Why This Matters

Research shows that when people encode both the old and new information together with an explicit change marker, memory interference is reduced. The old memory actually helps rather than hurts recall of the new information—a phenomenon called proactive facilitation.

The MedShift Solution

MedShift guides users through a 4-step process based on EMRC principles:

Step 1: Pre-Retrieval

Before taking the new medication, the app prompts users to actively recall their old medication routine.

Step 2: Prediction Generation

Users confirm what they expect to take based on their old routine.

Step 3: Prediction Error Alert

The app explicitly highlights the change—creating a clear prediction error signal.

Step 4: Recursive Encoding

Users encode the complete change narrative: “I used to take X, but now I take Y because Z.”

Demo Walkthrough

The interactive demo follows Sarah, a 68-year-old patient whose doctor changed her diabetes medication from a blue pill (Metformin 500mg) to a white pill (Metformin XR 750mg).

Key Screens

Morning Reminder → Old Medication Retrieval → Prediction Confirmation → Change Alert → Memory Encoding → Memory Test (1 week later)

Each screen includes a “Memory Science” explanation box showing how that step relates to cognitive neuroscience research.

Design Philosophy

1. Evidence-Based Intervention

Every UX decision is grounded in published cognitive science research, not intuition.

2. Transparent Science Communication

Users see why each step matters through embedded “Memory Science” explanations.

3. Active Engagement

Rather than passive reminders, the app requires active cognitive engagement with the change.

4. Complete Memory Encoding

The app ensures users encode the full change narrative, not just the new information.

Research Foundation

Primary Theory:

• Wahlheim, C. N., & Zacks, J. M. (2024). Memory updating and the structure of event representations. Trends in Cognitive Sciences.

Supporting Research:

• Work on proactive interference and facilitation
• Event segmentation and memory encoding
• Prediction error in learning and memory

Broader Implications

This demo illustrates how cognitive neuroscience research can be translated into:

• Health technology: Medication adherence, habit change, behavior modification
• Education: Helping students update misconceptions
• Training: Professional skill updating (e.g., medical protocols, safety procedures)
• Therapy: Cognitive restructuring interventions

Technical Implementation

The demo is built as a lightweight, interactive prototype:

• Pure HTML/CSS/JavaScript (no dependencies)
• Mobile-responsive design
• 13-screen interactive walkthrough
• Embedded science explanations at each step

Try the Demo

Launch Interactive Demo

Future Directions

This concept could be extended to:

1. Validated clinical trials testing efficacy vs. standard reminder apps
2. Generalization to other memory updating contexts (address changes, password updates, routine changes)
3. Personalization based on individual memory updating patterns
4. Integration with healthcare systems and prescription databases

Keywords: Applied Cognitive Science, EMRC Theory, Medication Adherence, UX Design, Memory Updating, Translational Research, Health Technology