Personal Health Data Aggregation Platform
Project in a Nutshell
A web-based personal health platform that consolidates patient medical records from multiple clinics, wearables, and self-reported sources into a single, easy-to-understand interface – translating fragmented clinical data into health scores, trend visualizations, and educational insights that put patients in control of their own care.
Client & Challenge
The average patient has medical records scattered across multiple providers, EMR systems, and wearable devices with no unified way to view or interpret them. Lab results arrive as raw numbers without context. Wearable data never meets the clinical record. Vaccination schedules drift out of compliance. And patients who try to take ownership of their health are blocked by medical jargon and disconnected portals.
The client needed a platform that aggregated medical data from every clinic a patient uses, integrated wearable inputs, calculated meaningful health scores, and presented everything in plain language – connected through existing clinic patient portals via OAuth.
Solution
PieSoft designed an end-to-end personal health data platform covering Discovery, MVP delivery, and an ongoing expansion path toward broader EMR integrations and richer AI-driven insight.
Discovery phase
Engagement producing the data integration architecture, OAuth flow design for multi-clinic authentication, health-scoring algorithm specification, role and permission model, technology stack decisions, and a phased feature roadmap separating MVP scope from later expansion.
MVP – core health aggregation platform
Covers every dimension of a patient’s health record – procedures, conditions, allergies, vaccinations, medications, labs, and vitals. Each category has its own dedicated module with educational content attached to every data point, so a user encountering an unfamiliar diagnosis or medication can immediately access plain-language context. A timeline view organizes all events chronologically with both monthly and yearly perspectives. A virtual avatar visualizes the user’s current health state. CDC-based vaccination scheduling surfaces upcoming and overdue immunizations. An AI healthcare assistant guides users through the platform and highlights the most important trends in their data.
Post-MVP expansion
Expanded EMR coverage across additional clinical networks, deeper wearable integrations, richer AI-driven trend analysis and personalized recommendations, and continued enhancement of the educational content library.
Technical Highlights
Multi-source data aggregation
A single user can connect any number of EMR clinics through OAuth, link wearable devices, and supplement clinical data with self-reported answers from an onboarding questionnaire. Every input feeds the same unified record, so the timeline, scores, and trend charts always reflect the complete picture rather than a single clinic’s slice of it.
Plain-language clinical translation
Every condition, procedure, medication, allergy, vaccination, and lab parameter ships with attached educational content. Lab results render as trend charts with explanatory context rather than raw values, turning a results portal into a comprehension tool.
Proprietary health scoring engine
A total health score and four organ-specific scores (heart, liver, brain, diabetes risk) are computed from conditions, recent lab trends, and vital parameters. Every score is tracked historically so users can see the effect of lifestyle and treatment changes on measurable health indicators over time.
Architecture built for secure, real-time health data
A React and Symfony stack on PostgreSQL, with Redis for caching, Mercure for real-time updates, and SQS for asynchronous processing. AWS Cognito handles authentication with Lambda triggers and SES for transactional email. Static frontend assets are served through CloudFront from S3, with backend and Mercure instances managed by ECS behind an Application Load Balancer. Infrastructure is fully expressed as Terraform code with state stored in GitLab, and the GitLab CI/CD pipeline manages test, build, and deploy stages end to end. HTTPS is enforced across every endpoint via AWS Certificate Manager. Alan AI powers the in-app healthcare assistant.
Business Value
The platform closes the three structural gaps that keep patients disengaged from their own health: no unified view across clinics, no plain-language interpretation of clinical data, and no personalized metrics to track progress over time. Patients gain a complete medical history in one place, an immediate authenticity-level understanding of what their records actually mean, and measurable scores to monitor as they make changes. The OAuth-based clinic connection model means no manual data entry and no parallel record system to maintain — the platform stays current with the source EMRs automatically.
The phased delivery model let the client launch the core aggregation, scoring, and educational layer in production before expanding into broader EMR coverage and deeper AI-driven insight, removing the typical risk of releasing a regulated-health-data product without a reference deployment. The architecture is built to scale with each additional clinical network and wearable integration brought online.
Conclusion
The Personal Health Data Aggregation Platform demonstrates PieSoft’s capability to deliver patient-facing digital health infrastructure as a complete product strategy – from Discovery and OAuth integration design through MVP, multi-source data aggregation, proprietary health scoring, and an AI-assisted user experience. The project combines regulated-data security, clinical-to-consumer translation, and deep EMR and wearable integration into a single platform built around how a patient actually experiences their own health
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