HealthSimplify

Case Study • May 10, 2025

An AI-powered app designed for everyday people to make healthcare understandable.

Research

Patients and caregivers often receive healthcare documents such as aftercare summaries, prescriptions, and lab results that are difficult to understand due to complex medical jargon. Low comprehension of medical information negatively impacts patient decision-making and health outcomes. Even with access to healthcare services, limited health literacy leads to confusion and anxiety

Personas

William is a 26-year-old marketing coordinator who wants to better understand his lab results without relying on search engines for medical research. He often finds test results confusing and worries about misinformation when researching his symptoms online.

Josey is a 38-year-old software engineer and working mother who wants to efficiently manage her personal and family healthcare documentation. She finds aftercare summaries overwhelming and time-consuming to interpret due to complex medical terminology.

Both users expressed the need for a reliable and simplified explanation of medical records that would save time and reduce stress when managing their healthcare decisions.

Lean UX Canvas

The Lean UX Canvas defined the primary business problem: patients and caregivers struggle to interpret complex healthcare documentation due to medical jargon. This results in confusion and anxiety when managing personal healthcare.

My hypotheses included:

— Simplified medical documentation would improve comprehension for patients with limited health literacy.

— Rewriting lab results in simpler language would reduce anxiety about health status.

— Users would trust AI-generated explanations if presented alongside original documents.

Success was measured through:

— 80% of users correctly interpreting simplified aftercare summaries and lab results.

— 70% of users reporting reduced anxiety after reviewing simplified documentation.

— 65% of users expressing trust in simplified AI-generated explanations.

MVP

We created a mobile application prototype that enables users to:

— Upload lab results and aftercare summaries

— Scan healthcare documentation

— View AI-generated simplified explanations of medical results

The MVP included two core flows: 1. Uploading original medical documentation 2. Viewing simplified AI-generated explanations of test results

Value Proposition Validation

Through MVP walkthroughs and usability sessions, I aimed to understand whether users with limited health literacy could easily interpret simplified medical documentation and trust the AI-generated explanations.

User feedback indicated:

— Relief when reviewing simplified test results

— Increased confidence in understanding medical information

— Desire for added credibility features such as provider names

— Need for contextual explanations regarding medical tests

Iterations included:

— Adding a chat option for follow-up questions

— Displaying provider names to increase trust

— Including small explanations about the purpose of medical tests

These changes improved user trust and reassurance when interacting with the application.

User Feedback

"I understand the AI Explanation a little more than I understand the actual Annual Physical Examination Summary."

"The AI explanation was succinct, and it was easier to digest what I was reading."

Outcomes

— 80% of users showed confidence in making healthcare decisions based on simplified documentation

— Users reported emotional relief after understanding healthcare documents

— Simplified explanations were preferred over original summaries by all test participants

— Reduced confusion when reviewing medical terminology

This improvement in comprehension led to lower levels of anxiety and increased trust in managing personal healthcare decisions.

Prototype

View Prototype