[Review] AutoBE Hackathon 2025-09-12 - Enterprise Healthcare Management Platform

[matt1398]

Conversation Links

https://hackathon.autobe.dev/?session-id=01994b06-85d0-735b-82f2-d63a5fadd1fe

1. Requirements Analysis

In my previous backend generation with GPT-4.1-mini, I put together the most detailed prompt I could manage, but still ended up needing several improvements, as documented in the GitHub discussion linked above. This time around, since GPT-4.1 is a more capable model, I decided to address those identified issues upfront before running the AutoBE code generation.

The healthcare platform requirements show meaningful improvement compared to the earlier LMS system. The priority definitions are much clearer, with detailed EARS format specifications across 16 comprehensive analysis documents. The system correctly sequences authentication and tenant isolation first, then builds out core clinical workflows, and finally adds advanced analytics—which makes sense for healthcare systems.

The RBAC and permission structure shows a solid grasp of healthcare organizational hierarchies. The 8-role system they've designed (SystemAdmin → OrganizationAdmin → DepartmentHead → MedicalDoctor/Nurse/Technician/Receptionist → Patient) actually mirrors how real healthcare organizations operate. Unlike the generic roles in the previous LMS system, this includes professional licensing requirements, NPI verification, and state board compliance—all essential elements for healthcare operations.

The performance and security requirements are much more thorough this time. They explicitly call out HIPAA compliance, SOC 2 readiness, and 10-year audit retention requirements. This kind of specificity was absent from the previous LMS system and represents a substantial step forward in handling non-functional requirements.

2. Database Design

Since AutoBE's stack choice is always the same, schema generation went smoothly without the issues I encountered before. The multi-tenant architecture is well-designed with consistent tenant isolation across all 680+ tables. Each entity properly includes organization_id for data separation, and the foreign key constraints and cascade deletes are set up correctly.

The ER relationship quality is much better than what I saw in the previous LMS system. They've used a 10-schema modular approach (actors, patient records, scheduling, billing, etc...) that creates logical boundaries while keeping referential integrity intact. The complex healthcare relationships like patient -> record -> encounter -> versions are modeled properly with appropriate audit trails.

The normalization and duplication balance is well-handled. The previous LMS had some over-normalization problems, but this healthcare system makes smart trade-offs—appropriately denormalizing for performance where it makes sense (like storing patient demographics in JSON fields) while maintaining strict normalization for clinical data integrity. The soft-deletion implementation is consistent across all entities, which addresses a maintenance concern I had with the earlier system.

Unfortunately, the same timestamp management problem from the LMS carries over here. They're still handling timestamps manually in providers instead of using Prisma's @default(now()) and @updatedat decorators. With 870 provider files, this creates a significant maintenance burden that could be easily avoided.

3. API Design

The healthcare API design is similar that I saw in the LMS system. The endpoint structure follows healthcare-specific patterns—/healthcarePlatform/{role}/{resource} gives clear RBAC context.
Using PATCH for search operations is still unconventional, but it makes more sense here given the complex request body requirements for healthcare filtering. This actually solves a problem I noticed in the LMS system where search parameters were constrained by URL length limits.
The error handling, versioning, and documentation show more production-ready thinking than the previous LMS system had. The Nestia integration generates proper OpenAPI specs with healthcare-specific examples, creates type-safe client SDKs, and produces comprehensive documentation. The authentication decorator pattern handles JWT validation cleanly while automatically adding security requirements to the swagger docs.
The documentation inconsistency problem is still there though—some endpoints have detailed descriptions while others are barely documented. Overall quality is better than the LMS system, but this inconsistency remains an issue.

4. Test Code

Test generation went smoothly again, with some nice improvements. The unit, integration, and contract tests cover comprehensive healthcare workflows including multi-tenant isolation testing. The test quality handles real healthcare scenarios like patient record amendments, emergency access overrides, clinical decision support, and regulatory compliance workflows.
The failure and edge case testing pays good attention to healthcare-specific issues. Tests cover scenarios like HIPAA violations, license verification failures, cross-tenant boundary enforcement, and clinical alert acknowledgment workflows. It's a good shift from generic enterprise patterns to actual domain-specific testing.
The custom testing framework has the same trade-offs I ran into before—you get powerful features like concurrent execution and automatic API client generation, but you miss out on Jest's coverage reporting and the broader ecosystem support.

5. Implementation Code

The directory structure and layering follows the same clean NestJS patterns as the LMS, but with healthcare-specific improvements. The 870 providers follow consistent patterns, though they still suffer from the same organizational and duplication issues.
N+1 prevention, caching, and input validation show some improvement. Healthcare-specific optimizations include parallel Promise.all() patterns for complex clinical queries, proper indexing for patient search (GIN indexes for full-text search), and role-based data filtering at the query level.
However, while tried to solve the numerous files in single folder problem, type inconsistencies between Prisma-generated and AI-generated TypeScript types, and soft deletion using Prisma middleware or extensions through my prompts(which are problems that I had in previous review), but all of these attempts seem to have failed. The compilation error in postauthSystemAdminLogin.ts (Type 'string | null | undefined' is not assignable to type 'string | undefined') indicates that my type system improvements weren't successful.
The soft deletion implementation remains problematic from a scalability perspective, with manual timestamp setting across hundreds of providers. This creates the same maintenance challenges I encountered in the LMS system.
And it still showed that all business logics were embedded in single folder.

Estimated Code Proficiency Level

mid

🙌 Overall Review

After digging deep into this healthcare platform and comparing it with my previous Enterprise LMS review, AutoBE shows some domain-specific improvements, but there are still persistent systematic issues that would prevent me from deploying this to production.

How My Previous Recommendations Fared:

Looking back at the key improvements I suggested in prompt for AutoBE after the LMS review, this time, I tried to explicitly designed the system prompt to handle my previous recommendations, and the results tell an interesting story about AutoBE's current limitations when it comes to systematic architectural changes:

1. ❌ Production-grade infrastructure - Didn't happen: The healthcare platform still lacks the basics like structured logging (like Pino), OpenTelemetry tracing, correlation IDs, health checks, and proper exception filters. The server won't even start because of missing compiled artifacts, and there's still no logging framework beyond basic console.log statements.

2. ❌ Proper NestJS Guards - Didn't happen: Authentication still uses parameter decorators (@PatientAuth(), @SystemadminAuth()) instead of actual NestJS Guards. This completely bypasses the elegant request lifecycle integration and middleware pipeline that would be essential for healthcare audit logging.

3. ❌ Centralized cross-cutting concerns - Didn't happen: Soft deletion is still manually implemented across all providers with repetitive deleted_at timestamp setting. No Prisma middleware was implemented, pagination utilities are duplicated everywhere, and error handling patterns remain inconsistent.

4. ❌ Type system optimization - Tried but failed: The compilation error in postauthSystemAdminLogin.ts shows that despite my attempts to fix type system issues through better prompting, the fundamental problem persists. There are still 176 custom type files duplicating Prisma schema definitions, creating maintenance headaches and type drift.

5. ❌ Better test coverage - Partially there: While the healthcare-specific test scenarios are comprehensive, there are still no systematic tests for soft deletion exclusion in search results, and the custom testing framework still lacks coverage reporting.

6. ❌ Structured documentation - Didn't happen: Despite comprehensive requirements documentation, there are no layered .md files for individual components, API conventions, or development guidelines that would help with AI-assisted development.

7. ❌ Centralized utilities - Didn't happen: Pagination logic is still duplicated across providers with inconsistent defaults (some use limit: 10, others limit: 100), timestamp handling remains manual, and validation patterns are repeated throughout the codebase.

What AutoBE Does Well vs. Where It Falls Short:

This comprehensive look reveals something important: AutoBE is exceptional at generating well-structured backends in its specific format, but shows real limitations when it comes to flexibility and architectural customization.

Where AutoBE Shines - Domain Modeling:

• Sophisticated healthcare domain understanding with HIPAA compliance, clinical workflows, and regulatory requirements
• Excellent database schema design with proper multi-tenant isolation and complex relationship modeling
• Comprehensive business logic implementation with role-based access control and clinical decision support
• Impressive requirements analysis with 16 detailed specification documents
• Consistent code patterns across a lot of providers following NestJS conventions

Where It Struggles - Architectural Flexibility:

• Pattern Lock-in: AutoBE generates code following rigid patterns (parameter decorators, manual soft deletion, duplicated providers) that resist architectural improvements
• Infrastructure Blindness: No matter how detailed my prompting got, AutoBE doesn't generate production-grade infrastructure (logging, monitoring, health checks, proper error handling).
• Persistent Systematic Issues: Cross-cutting concerns like pagination, validation, and timestamp management remain duplicated rather than centralized, despite explicit prompting
• Type System Rigidity: The custom type system approach creates maintenance overhead but seems unchangeable through prompting alone
• Testing Framework Lock-in: Uses custom testing framework instead of Jest ecosystem, limiting coverage and debugging capabilities

The Flexibility Problem:

The most concerning finding is that even with GPT-4.1's improved capabilities and detailed architectural guidance in my prompts, AutoBE seems unable to deviate from its established patterns. This suggests that while AutoBE excels at generating sophisticated domain models within its framework, it lacks the flexibility to implement architectural best practices that deviate from its core approach. But this might be due to insufficient context length of GPT-4.1, since my prompts were quite long.

So you're basically choosing between getting sophisticated domain logic really fast or having the architectural flexibility you need for production. For healthcare apps that need both the domain smarts AND production-grade infrastructure, this becomes a real problem.

Getting to Production is Still Hard:

The healthcare platform shows that AutoBE can build impressive domain-specific features, but it can't get you all the way to production ready. The infrastructure gaps, compilation errors, and rigid architectural patterns mean you're still looking at significant manual work to make the generated code actually deployable.

What This Means for Healthcare Teams:

AutoBE builds sophisticated healthcare domain models with solid business logic, but it's too inflexible to handle the systematic production stuff you actually need. The generated code gives you a great head start—you're basically getting months of domain expertise in a few hours.

But here's the thing: going from "impressive healthcare domain model" to "something we can actually deploy" still requires a lot of manual architectural work that fights against AutoBE's patterns. It seems like AutoBE is really good for rapid prototyping and getting your domain model right, but you need to plan for substantial refactoring to get it production-ready.

The healthcare platform really shows off both sides of AutoBE—it's excellent at domain modeling but has real limitations when it comes to architectural flexibility. That's definitely something to keep in mind if you're thinking about using AutoBE for production healthcare applications.

Final Recommendations for AutoBE

Enable Manual Intervention During Generation

• Allow developers to course-correct when AutoBE goes down the wrong architectural path
• Provide checkpoints or interactive modes during code generation

Increase Architectural Flexibility

• Move beyond rigid patterns that force parameter decorators, manual soft deletion, and duplicated providers
• Allow developers to choose their preferred architectural approaches

Add Production Infrastructure Support

• Implement structured logging, health checks, proper error handling, and monitoring capabilities
• Include production-ready boilerplate and configuration templates

Simplify Type System Management

• Eliminate the custom type files that might duplicate Prisma schemas
• Create a unified type system that reduces maintenance overhead

Enhance Testing Capabilities

• Integrate with Jest's coverage reporting and broader testing ecosystem
• Provide comprehensive testing utilities and patterns beyond the current custom framework limitations