Meaic
Meaic is an AI-powered healthcare platform designed to centralize patient history, provide AI-assisted disease prediction, and simplify appointment management for both patients and medical professionals. The platform aims to address inefficiencies in data accessibility, fragmented medical records, and the lack of proactive health risk identification.
Year
2024
Duration
Current
Domain
Healthcare
Platform
Web/Mobile
Challenge
The current healthcare ecosystem faces significant challenges, including fragmented access to medical history, inefficient diagnosis, and a lack of centralized data systems. Doctors often rely on incomplete patient records, while patients struggle to track appointments, prescriptions, and reports across multiple platforms.
Solution
Meaic introduces a centralized healthcare platform where patient medical histories, appointments, and reports are managed in a single location. This platform integrates machine learning to offer disease prediction insights, empowering doctors with proactive health risk assessments while keeping data accessible and secure. The system simplifies appointment scheduling and enhances communication between patients and healthcare providers, ensuring a holistic, patient-centric care experience.
1
The Spark of an Idea
The inspiration for Meaic emerged from observing the repetitive struggles of both patients and doctors in managing fragmented medical histories. During consultations, doctors often relied on incomplete records, while patients repeatedly had to explain their health conditions. This inefficiency led to delayed treatments and reduced diagnostic accuracy. Recognizing the need for a solution, our team envisioned a centralized platform where both patients and medical professionals could access accurate health data with predictive insights.
Understanding the Problem
During the discovery phase, qualitative interviews with doctors, patients, and healthcare administrators revealed several core pain points. Doctors faced difficulties accessing complete patient data, often leading to delayed or incomplete diagnoses. Patients were frustrated by the need to repeatedly explain their medical history at each consultation and faced challenges in keeping track of their health data. Healthcare administrators struggled with fragmented data spread across multiple systems, making compliance and data security complex. These insights clarified the need for a centralized platform where patient data would be easily accessible and AI-driven insights could assist decision-making.
Approach
User Research and Persona Development
Conducted 12 doctor interviews, 15 patient interviews, and 5 healthcare admin sessions to gather qualitative insights.
Created empathy maps to understand emotional pain points and frustrations among users.
Mapped the user journey across pre-consultation, consultation, and post-consultation phases.
Developed two primary personas to represent both healthcare professionals and chronic patients.
Identified core pain points such as fragmented health data, inefficient diagnosis processes, and unclear communication pathways.
Technology Integration (ML and Explainability)
Integrated Machine Learning (Teacher-Student Ensemble) for disease prediction.
Implemented LIME (Local Interpretable Model-agnostic Explanations) for transparent AI predictions.
Provided confidence scores alongside predictions to help doctors assess diagnostic reliability.
Designed a feedback loop where doctors could validate AI predictions, enhancing the model’s accuracy.
Ensured HIPAA compliance with data encryption and secure storage practices.
Visual and Interaction Design
Developed low-fidelity wireframes focusing on clean, minimalistic layouts.
Designed role-based dashboards for patients, doctors, and administrators.
Selected Nunito Sans as the primary typeface for clarity and readability.
Applied a professional color scheme with #18364B (Primary) and #FF5A5A (Accent) for visual hierarchy.
Prioritized accessibility with high contrast ratios and scalable text for inclusivity.
Primary Persona
Dr. Anjali Sharma, 38
An experienced General Practitioner working in an urban healthcare facility with over 12 years of practice. Anjali is tech-savvy but prefers simplified tools that reduce administrative workload.
Empathy
Anjali often feels frustrated when accessing patient data, as information is frequently incomplete or spread across multiple platforms. She feels the pressure of making critical decisions without comprehensive health histories, which leads to stress and occasional diagnostic delays.
User Journey
Before a consultation, Anjali struggles to gather complete patient information, often relying on the patient's memory. During consultations, she feels limited by incomplete records, leading to a sense of professional inefficiency. Post-consultation, she often worries about follow-ups and whether patients are adhering to treatment plans. This creates a sense of professional dissatisfaction and a need for a more reliable system.
Secondary Persona
Rohan Patel, 29
A software engineer living with chronic asthma who requires frequent check-ups and prescription renewals. Rohan is highly tech-savvy and prefers managing his health records digitally.
Empathy
Rohan often feels anxious during medical visits, unsure if his doctors have access to his full medical history. His biggest frustration is managing scattered health data across multiple platforms, leading to repeated explanations during each consultation. He feels disconnected from his treatment plans due to limited visibility into his health data.
User Journey
Before an appointment, Rohan often spends time collecting scattered medical records and feels overwhelmed by the complexity of managing health data. During consultations, he feels uncertain whether his health information is accurately reflected in the discussions. After consultations, he struggles with tracking medication schedules and appointment follow-ups, leading to stress about his long-term health management.
Key Features
Centralized Patient History
A single, secure location where patients' complete medical history, reports, and prescriptions are stored, ensuring data is available for both patients and doctors.
AI Disease Prediction with Explainability
AI models provide predictive health insights with explainable outputs using LIME, allowing doctors to understand contributing risk factors alongside a confidence score.
Simplified Appointment Management
A streamlined system for patients to book, reschedule, and receive reminders for their appointments, reducing missed consultations.
Role-Based Dashboards
Custom dashboards for patients, doctors, and admins tailored to their needs, ensuring clarity without overwhelming information.
Primary User Flow
Low Fidelity Design
2
From Concept to Prototype
Once the foundational features were outlined, the team developed low-fidelity wireframes to map the platform’s structure and data flow. Prototypes were created with a focus on data visualization and predictive health insights while keeping navigation simple. User testing during this phase revealed the importance of clearly explaining AI-based predictions, leading to the addition of a confidence score and risk factor breakdown within the health dashboard.
High Fidelity Design
User Feedback and Results
Feedback sessions with both doctors and patients revealed key insights. Doctors found the centralized data view helpful but requested clearer language for predictive health reports. Patients appreciated the ease of accessing their health data but suggested more intuitive labels for test results and conditions. These insights led to adjustments, including a simplified results page and the addition of tooltips explaining medical terms more clearly.
3
Refining Through Feedback
Following feedback, the user interface was further refined. The primary user flow was simplified to ensure faster access to patient records, while the color palette was slightly adjusted for better contrast. Key changes included reducing cognitive load by restructuring the health data display and enhancing the visual hierarchy of critical insights.
