I Built a Healthcare Platform Where AI Is Never Allowed to Have the Final Word.

Khushal Goel — Sun, 01 Nov 2026 06:29:25 +0000

Diagnosia: Connected Care, Reviewable by Design

Project Overview

Healthcare information is dangerously fragmented across brief consultations, handwritten prescriptions, laboratory documents, hospital discharge papers, and specialist referrals. To bridge these critical gaps, I developed Diagnosia.

Rather than a collection of disconnected AI demonstrations, Diagnosia is a cohesive healthcare workflow platform designed to help clinicians, specialists, patients, and care teams manage information seamlessly across the entire patient journey.

The system operates on a coordinated workflow where:

Information enters through existing channels.
AI prepares reviewable drafts for medical professionals.
Authorized modules reuse relevant context.
Workflow events dynamically update the patient journey in real time.

Core Capabilities & Modules

The platform is anchored by three foundational tools:

DiagnoFast: A voice-first patient intake system.
Skin Cancer Detection: Image-based screening support.
Prescription Analyzer: Medication interpretation.

To extend this foundation, four specialized modules provide additional support:

ClinScribe: Clinical encounter summarization.
LabLens: Laboratory report digitization.
DischargeCare: Transition coordination for hospital discharges.
ReferralLoop: Specialist referral tracking.

Demonstration & Patient Journey

View the demonstration files on Google Drive

The platform's visual identity emphasizes operational clarity, demonstrating an end-to-end synthetic patient journey on a live timeline. The workflow captures a voice interview to queue a report, digitizes scanned tests, and drafts clinical notes based entirely on consultation transcripts. Every state change is projected live onto the timeline without requiring manual screen reloads.

Technical Architecture & Partnerships

Diagnosia integrates several partner technologies, each with a strictly defined and isolated responsibility to prevent conflicting sources of truth:

Google Gemini API (Multimodal Engine): Reads scanned lab reports and prescription photos. All outputs are validated against a strict schema before reaching human review.
YugabyteDB (Technical Audit Trail): Maintains a record of all AI runs, attempts, and handoffs, keeping the core clinical database clean.
Meko via MCP server (Shared Agent Context): Manages approved knowledge and memory.
MongoDB Atlas (Workflow Event Store): Uses Change Streams to instantly project workflow events onto open care screens.

Architecture Principles & Safety Boundaries

The engineering principles prioritize patient safety and data integrity above all else:

Single Responsibility: No sponsor database is permitted to override the local identity authority, which remains the absolute single source of truth.
Human-in-the-Loop: All AI outputs are treated strictly as drafts until approved by an authorized human reviewer. Nothing reaches a reviewer unchecked.
Data Separation: Technical audits are completely separated from clinical data, ensuring records never pollute the core database.
Strict Authorization: Modules only access context they are explicitly authorized to view.
Testing Boundaries: As a pilot project, the platform operates entirely on synthetic data.

Project Philosophy

Building a modular, feature-frozen product with strict safety boundaries was an incredible test of technical discipline. Wrangling the architecture to ensure the local identity authority remained secure reinforced a core lesson: building for healthcare is not just about rapid innovation; it is about meticulously engineering trust.

DEV Community: Khushal Goel