Doctolib accelerates developer productivity with Claude Code
DoctolibClaude CodeDoctolib, Europe's leading healthcare technology platform serving 420,000 health professionals and 90 million patients, rolled out Claude Code across its entire engineering team after piloting with 30 engineers. The platform team built a centralized repository of reusable prompts, custom commands, and subagents for documentation, testing, code review, and debugging. Claude Code migrated the company's entire legacy visual regression testing infrastructure in hours instead of weeks, automatically updates technical documentation on every code change, and now handles pull request reviews instantly through automated Claude-powered reviews on their main infrastructure repository.
Overview
Doctolib, Europe's leading healthcare technology platform serving 420,000 health professionals and 90 million patients, rolled out Claude Code across its entire engineering team after piloting with 30 engineers. The platform team built a centralized repository of reusable prompts, custom commands, and subagents for documentation, testing, code review, and debugging. Claude Code migrated the company's entire legacy visual regression testing infrastructure in hours instead of weeks, automatically updates technical documentation on every code change, and now handles pull request reviews instantly through automated Claude-powered reviews on their main infrastructure repository.
This entry has 15 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Doctolib's engineering team faced a productivity paradox. As Europe's leading healthcare technology Operating System serving 420,000 health professionals and 90 million patients, the company needed to ship features faster while maintaining reliability for critical healthcare workflows. But administrative tasks consumed significant engineering time—writing documentation, creating tests, reviewing pull requests, and addressing technical debt pulled developers away from solving complex healthcare challenges. New engineers took weeks to contribute meaningfully to unfamiliar parts of the codebase, pull request reviews created bottlenecks, with engineers waiting hours or days for teammate availability, and technical debt accumulated faster than the team could address it.
The solution
After piloting Claude Code with 30 engineers and seeing promising productivity gains, Doctolib rolled out the tool to their entire development team. Doctolib's platform team created a centralized repository of prompts, custom commands, and subagents that all developers pull during their initial Claude Code setup. Engineers write documentation and tests, review pull requests, and address technical debt through repeatable prompts for migrations and debugging. The tool's headless mode runs directly in their CI pipeline, automatically opening pull requests for routine maintenance tasks.
Reported business value
We completed the migration in hours, not weeks. It's now in production handling all our screenshot comparisons. Every code change triggers a CI job that updates technical documentation automatically. Pull request reviews, previously a bottleneck requiring hours or days of wait time, now happen instantly. Teams now self-onboard to new projects on unfamiliar technology stacks—reducing ramp-up time from weeks to days.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other healthcare entries in the register.
Novo Nordisk builds AI drug discovery platform on Azure with Microsoft Research
Novo Nordisk partnered with Microsoft Research to build an AI platform on Azure AI and data stacks spanning regulatory affairs, early research, drug discovery and trial design, using Azure OpenAI Service, Azure Cosmos DB and Azure Kubernetes Service, with Power BI and Power Apps for collaboration. The platform includes a copilot for researchers, shared reasoning-chain templates, and governance/auditing of how data and models are used. The teams published early results on predictive AI models for cardiovascular disease risk detection, including an algorithm that Novo Nordisk says predicts patients' cardiovascular risk better than the best clinical standards, drawing on more than 100 years of insulin research data.
Novo Nordisk· DenmarkAzure OpenAI Service · Azure Cosmos DB · Azure Kubernetes Service +2BAYADA Builds a Unified, AI-Ready Platform for Compassionate Care
BAYADA Home Health Care is consolidating three separate legacy data platforms (multiple practice management systems, an on-prem ODS, and Snowflake) and 65+ enterprise data sources into a single Databricks Lakehouse under its Data Modernization program, using medallion tiers, Lakeflow Jobs, Asset Bundles, and Unity Catalog governance. As part of the migration, BAYADA uses an LLM-powered code converter and Databricks Assistant to automate SQL and stored-procedure translation, and applies a machine learning-based data mastering accelerator to create golden Client, Payor, Candidate, and Referrer records. BAYADA reports roughly 30% better workload performance and cost efficiency versus its prior Snowflake environment, and is laying a governed foundation for AI agents supporting payroll validation, compliance monitoring, and operational insights, separately from its roadmap for AI-assisted chart review and risk prediction.
BAYADA Home Health CareDatabricks SQL · Lakeflow Jobs · Unity Catalog +1
CDPHP modernizes infrastructure and improves medical data extraction with AWS AI/ML
CDPHP, a not-for-profit health plan serving 400,000 members in Upstate New York, used AWS services including Amazon Comprehend Medical, Amazon Textract, and Amazon SageMaker to automate its data processing pipeline for unstructured medical records and health data. The organization processed over seven million records during initial migration and now processes 3,000 electronic health records weekly. CDPHP achieved a 60% improvement in overall efficiency and reduced HEDIS report generation from 4-5 days (three data scientists) to two reports produced daily.
Mayo Clinic deploys NVIDIA DGX SuperPOD to accelerate pathology foundation models
Mayo Clinic deployed an NVIDIA DGX SuperPOD with NVIDIA DGX B200 systems to support foundation model development for pathomics, drug discovery and precision medicine. In partnership with Aignostics, Mayo Clinic built the Atlas pathology foundation model, trained on more than 1.2 million histopathology whole-slide images. The new infrastructure is reducing four weeks of pathology slide analysis work to one week.
Mayo Clinic· United StatesNVIDIA DGX SuperPOD · NVIDIA DGX B200 · NVIDIA Blackwell
Was this helpful?
Your feedback helps us improve our use case database