DIPROMES — Comprehensive management for therapeutic medical equipment
A complete web platform for Distribuidora de Productos Médicos y Servicios, a Dominican therapeutic-equipment rental company. It covers patients, equipment assignment, GPS tracking, clinical notes, and invoicing — all in one system.
View live demoThe problem: medical operations with no system
DIPROMES operates by renting therapeutic equipment (ventilators, oxygen concentrators, compression devices) to patients in clinics, hospitals, and homes in the DR. Without a system, the operation depended on disconnected spreadsheets and WhatsApp — impossible to scale.
- Equipment location unknown — WhatsApp and memory
- Patient clinical history scattered across paper
- Manual invoicing with risk of double billing
- No visibility into which equipment is free or in use
- No traceability when a returned device is picked up
- Remote tracking of placements was impossible
- Real-time GPS map with the location of every device
- Digital clinical record per patient with full history
- Delivery receipts (invoices) generated automatically
- Availability dashboard for all 8 devices
- Digital delivery/pickup log with device condition
- Placement tracking by phone or tablet
Screenshots of the system
operational-equipment KPIs, and period revenue
Main dashboard — map of active patients and equipment fleet status
and informed consent agreements
Patient profile — complete history of placements, equipment, and clinical follow-up
under maintenance. Serial number and condition.
Fleet control — availability, status, and assignment of every medical device
System architecture
DIPROMES uses a decoupled client-server architecture: the backend in Python FastAPI exposes a REST API with automatically generated Swagger documentation, and the JavaScript frontend consumes that API. This makes future mobile clients possible without changing the backend.
Main modules
Tech stack
Backend
Frontend and maps
Document generation and infrastructure
How it was built
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1Analysis of the real operational flow Before writing code, I documented the full cycle of a placement: from patient intake to equipment pickup. That flow determined the data models and the views needed.
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2API-first with FastAPI I started with the backend: designed the REST API with FastAPI, defined the Pydantic schemas, and built the endpoints. FastAPI generates Swagger docs automatically — the team can test every endpoint with no extra code.
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3Map integration with Leaflet.js The map layer was the module with the highest visual impact. Leaflet.js is open-source and requires no API key — ideal for production with no usage costs. Markers load dynamically from the API when the map opens.
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4PDF receipt generation Delivery receipts are the business's central commercial document. ReportLab generates the PDF with logo, patient details, assigned equipment, period, and totals — ready to sign and send to the insurer or client.
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5Built-in backup system For medical operations, data loss is critical. I implemented a backup/restore module that exports the entire database to JSON and re-imports it with no loss — with no dependency on external tools.
What I learned
FastAPI is the right framework when you need a well-documented, high-performance API. The combination of strict typing with Pydantic and automatic Swagger docs removes all ambiguity between frontend and backend — the API contract is the code itself.
The most important lesson: in health systems, data integrity beats every other metric. The backup module was what built the most trust with the client, even though it's not "visible" in the UI. A system that can recover from any failure is a system you can use with peace of mind.
Second lesson: Leaflet.js is a better choice than Google Maps for internal-use projects. No map-load costs, no API key to manage, and all the functionality the real use case required.
Need a management system for your company?
I build custom web platforms with Python FastAPI and PostgreSQL for businesses in the Dominican Republic. Health, logistics, inventory, or whatever your operation needs — we start with real process analysis.