EZCap Automated Medical Claims Adjudicator

@Microlent

Hello, I understand that you are looking for a developer to transform your EzCap concept into an automated medical claims adjudicator that can efficiently process 837-P or 837-I files or API payloads. My expertise lies in back-end development and full-stack solutions, making me well-suited for this project. To address your key requirements, I propose implementing a modular system that can perform automated validation checks for CPT/ICD compatibility, duplicate detection, and member plan matching without manual intervention. The system will also maintain a comprehensive audit trail for compliance purposes. For this project, I plan to utilize technologies such as Python for backend processing, Docker for deployment, and SQL databases for data storage. I will ensure that the system is easily configurable to accommodate additional edits in the future without disrupting the core logic. I invite you to open a chat so we can discuss the technical approach in more detail and address any questions you may have. You can view my portfolio at https://www.freelancer.com/portfolio-items/11539740-microlent-systems-portfolio Looking forward to the opportunity to collaborate on this project. Sincerely, Rajesh

@diektech

Hi — Elias here from Miami. I understand you're looking to build an automated engine for adjudicating medical claims based on your EzCap concept. The goal is to streamline the claims process while ensuring accuracy and compliance. What usually matters most here is the integration of various data sources and managing complex workflows. A common issue in systems like this is ensuring data management is reliable and scalable. The tricky part is usually handling permissions, especially as the system grows. My approach would be to design a robust architecture that prioritizes stability and maintainability. I would focus on creating a clear separation between backend processes and the user interface, allowing for smooth updates and expansions without disrupting existing functionality. I have worked on similar projects involving complex data integrations and automated workflows, giving me insight into potential pitfalls and best practices. A few questions to better understand the scope: Q1 – What specific data sources do you plan to integrate for claims processing? Q2 – Are there particular user roles and permissions that need to be addressed? Q3 – What are your expectations regarding scalability as the volume of claims increases? Happy to discuss the details and suggest the best technical approach. Looking forward to hearing from you.

@anirudhat

The part that decides whether this engine survives its first audit is keeping the rule set out of the code entirely — otherwise every new edit, like fraud scoring, means touching adjudication logic you've already validated. I'd build it in Python: an X12 837-P/I parser that normalises loops/segments into a canonical claim object, a rule engine where each edit is a registered, versioned plugin (CPT/ICD compatibility, duplicate detection, member-plan match) returning a structured pass/fail per service line, and PostgreSQL holding both claims and an append-only audit table recording every rule fired, input fields read, verdict and timestamp. Large batches stream segment-by-segment rather than loading whole files, processed through a worker queue so a 50k-claim file doesn't block the API. Deliverables come as a Docker Compose stack with unit tests, sample edits loaded, an 835-style/JSON writer, and an audit export endpoint. I can start immediately.

@Hubready

Hello!, I am a Florida-based senior software engineer and this project stands out because the real challenge is not just receiving claims, but turning your EzCap concept into a reliable adjudication engine that validates, processes, stores, and returns decisions cleanly. I’d approach it in practical phases: 1) map the claim intake flow and data model 2) design the SQL/database for claims, rules, and outcomes 3) build the API layer to receive and normalize claims 4) implement adjudication logic and exception handling 5) test edge cases so the engine is stable, traceable, and easy to extend I’ve built similar data-heavy backend systems where accuracy and auditability mattered more than flashy features. This kind of project needs someone who catches the small details, because one missed field or rule can break the workflow. That’s exactly the kind of work I take seriously. Relevant examples: - a claims-style automation system for a healthcare admin workflow - a rules-based billing intake engine for a SaaS platform - a structured API and database pipeline for transaction processing - a data validation backend for a document workflow tool Quick questions: 1) What claim formats should EzCap accept first, and do you have sample payloads or schemas? 2) Are the adjudication rules fixed, or do you need a rules engine that can grow over time? 3) Should v1 return only approve/deny, or include explanations for each decision? If you want, I can help define the MVP clearly so we build

@bcosley

Hi, I understand you're turning 837 claims into an automated adjudicator with modular edits and a clear audit trail. The system should produce per-service-line pass/fail results as data lands in, using an auditable event log. I've built similar claim-validation pipelines with pluggable rule sets and full audit logs to satisfy audits and operational reviews. I'd start by parsing the incoming 837 data, validating against a configurable rule library, and emitting per-line outcomes with a structured audit record. I’d also provide unit tests and a Docker-based deployment guide for repeatable runs. One technical risk / key challenge: overlaps between edits can cause false positives unless rules are isolated and evaluated in deterministic order. Two clarification questions What should happen if a batch includes corrupted segments, fail fast or skip bad records and continue? Should the audit trail store full raw payloads, per-field touches, and per-rule outcomes, and what retention and access controls do you want? If we're aligned, I can outline the implementation phases before kickoff. Best regards, Brandon

@ShaikhAneesa

Hi there, I have carefully reviewed the project requirements for the Automated Medical Claims Adjudicator. I understand the need for developing an engine that can automate the end-to-end adjudication process for medical claims, focusing on automated validation checks and a comprehensive audit trail. Let's chat and discuss it further. To handle your project, I will start with designing a modular system using Python and Django framework. I will implement automated validation checks using libraries such as Pandas for data manipulation and Django REST framework for API development. The system will have a configurable rule library for easy updates and a robust audit trail using PostgreSQL database for storage. The deliverables will include source code with unit tests, a configurable rule library, an audit-trail store, and deployment instructions using Docker. Before signing-off my bid, I would like to ask a question, i.e., how do you envision handling real-time data processing in addition to batch files? Warm Regards, Aneesa.

@gotucat

Hi, I reviewed the project to build an Automated Medical Claims Adjudicator that ingests 837-P or 837-I data (file upload or API payload), parses it, runs validations, and returns pass/fail per service line. I’ll implement an API + SQL based, modular claims-processing engine with a configurable rule library for CPT/ICD compatibility, duplicate detection, and member plan match, so new edits can be swapped in without rewriting core logic. I’ll ensure a full, time-stamped audit trail that records every rule fired and every field touched in a persistent store, plus clean unit tests and easy deployment via Docker. Let’s discuss here now.

@emilesd617

Hi, I am a backend developer with 8 years of rich experience in software development. I am familiar with EZCap 6.x, SQL, REST APIs, X12 837-P/837-I and 835 transactions, medical claims processing, configurable rule engines, audit trails, Docker, and unit testing. I can build a modular adjudication engine that processes large files in controlled batches, validates each service line, and stores every rule result in a complete timestamped audit trail. I'm an individual freelancer and can work in any time zone you prefer. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details.

@rizwange4

As a seasoned software engineer with a profound understanding of data and database management, I am fully equipped to transform your EZCap concept into an unrivaled automated medical claims adjudicator. Over the years, I've built various AI-powered systems just like this one, ensuring that every business requirement was met without compromising scalability and security. This means that if chosen for the job, you can rest assured knowing that I have the necessary skills and experience. In terms of deliverables, I will provide you with the complete source code and unit tests for the claims-processing module, a configurable rule library loaded with sample medical edits, seamless integration of a persistent audit trail store that comes with a simple viewer or export function for easy management well as deployment instructions (Docker or similar) so you can effortlessly launch the system in your own environment. In essence, my primary goal is to use my skills and expertise to design an EZCap Automated Medical Claims Adjudicator that exceeds your expectations.

@sabaehsan

Greetings, I appreciate the opportunity to help you develop an automated medical claims adjudicator. You’re looking for a system that can efficiently process claims by parsing 837-P or 837-I files, running automated validation checks, and providing a clear pass/fail status for each service line. My approach would involve creating a modular architecture that allows for easy integration of new validation rules, ensuring that your system can adapt as your needs grow. I plan to use a tech stack centered around SQL for database management, along with a suitable API framework for data integration. This will enable us to handle large batch files effectively while maintaining a robust audit trail for compliance. My experience in data management and API development will ensure a smooth, efficient process for your claims. Best regards, Saba Ehsan

@denisSolution

Hi, I'm Denis, a developer with experience building automated data processing systems that handle complex validation rules and maintain full audit trails. Your EzCap concept sounds like a modular claims adjudication engine where the core challenge is reliably parsing medical claim files and applying a configurable set of validation rules. I’d approach this by first extracting the structured data from 837-P/I or API payloads, then running each rule in sequence while logging every decision, field change, and timestamp into a dedicated audit store. The rule library would be externalized as JSON or a simple database table so you can add new edits like fraud scoring without touching the core logic. For handling large batch files, I’d stream the input rather than loading everything into memory, process each claim in a queue, and batch the audit writes to avoid performance bottlenecks. The output could be structured as JSON for easy integration with downstream systems, with an optional 835-style file formatter if you need compliance with existing workflows. One risk worth mentioning is data quality—medical claims often have inconsistent formats or missing fields. I’d build in strict parsing with clear error messages and a dead-letter queue for claims that can’t be processed, so you can review and fix them without breaking the pipeline. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@starttechno

EZ-CAP 6.x SQL Server auto-adjudication requires more than a batch-pricing script, and your blueprint clearly defines the right foundation. I will build a modular engine for 837P and 837I claims with claim- and line-level decisions, including eligibility, plan/member matching, provider/network, authorization, duplicate/timely filing/COB, coding, modifiers, NCCI/MUE, pricing, benefits, liability, adjustments and PAY, DENY, PARTIAL_PAY, CAPITATED or review outcomes. The core will use SQL Server schemas, versioned data-driven rule tables and focused stored procedures orchestrated by usp_AdjudicateClaim, usp_AdjudicateBatch and usp_ResumeClaim. A FastAPI layer will accept parsed 837/API payloads and return documented JSON, with an optional 835-style export. I will implement the page 3–4 checkpoint model—current, last successful and restart steps, exception, data version, rule version and resume flag—so completed lines survive manual review. Every rule result, touched field, decision and timestamp will be stored in an audit schema with a simple filtered viewer/export. Following page 7, I will first isolate EZ-CAP integration through a TEST stored-procedure interface catalog and avoid uncontrolled writes to proprietary financial tables. Docker, SQL migrations, unit/integration tests, sample edits and deployment instructions are included. Muhammad Saad

@mughiraaa

hi, i have reviewed the details of your project. i have experience with ezcap, medical claims processing, sql, apis, and healthcare data workflows. i can build the claims engine for 837 p and 837 i files with modular validation rules, cpt and icd checks, duplicate detection, member plan validation, and a complete audit trail. the rule engine will be configurable so new edits can be added easily. i will use a reliable api and sql based architecture with docker deployment and efficient batch processing for large claim files. source code, tests, sample rules, audit storage, and deployment documentation will be included. can we schedule a quick meeting to discuss the project in detail. it will help me understand your needs better and give you a clear plan with timeline and budget. i will also share my portfolio during the chat. mughiraa

@HamparProd

Hi, EZ-CAP claims adjudication is more than parsing an 837. The important part is preserving the relationship between member eligibility/benefits, provider and authorization data, pricing rules, financial responsibility, claim edits, and the resulting service-line disposition. I’m familiar with EZ-CAP claims workflows and X12 837P/837I/835 processing. I would normalize incoming claims into a consistent internal model, then run each service line through a configurable rule layer for eligibility/plan matching, CPT/ICD edits, duplicates, authorization and other business rules. Each rule evaluation would write an immutable audit record containing the claim/line, rule version, fields evaluated, result, reason and timestamp. Large 837 batches would be streamed rather than loaded entirely into memory, with JSON output first and the interface kept modular for 835/database output. I can start immediately after award and deliver the documented Dockerized module with tests within 14 days.

@shawanay

Good day I have 8 years of experience building backend and full-stack systems. I can build your EzCap claims adjudication engine to process API payloads from ingestion The engine will support configurable validation rules including CPT compatibility, duplicate detection, member matching Every rule execution and field-level change will be captured in a persistent, timestamped audit trail, with a simple viewer mechanism for compliance and audit requirements. For large batch files, I’ll use streaming processing, queued workloads and database transactions so claims can be processed efficiently without loading entire files into memory. Dockerized deployment Let's connect so we can discuss the requirement

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ Hi, Thank you for checking my proposal and sharing the detailed project brief. I can build your claims processing engine using a modern tech stack (Node.js, Express, MongoDB) with a scalable and efficient design. I will deliver: • Automated validation checks for CPT/ICD, duplicates, and plan matching • Modular adjudicator for easy edits and enhancements • Comprehensive audit trail with time-stamped records • Configurable rule library with sample edits included • Deployment instructions via Docker for seamless setup You will also receive: • Documentation for API usage and rule configuration • Ongoing support during initial deployment I am confident I can execute your vision professionally and efficiently. Looking forward to discussing timeline and next steps. Best regards, Chirag Pipal

@ozbas

Dear Edgar, I have carefully reviewed your requirements for the EZCap Automated Medical Claims Adjudicator and fully understand the critical need for an end-to-end engine capable of parsing 837-P/837-I files or equivalent API payloads, running comprehensive validation rules, and producing clear adjudication outputs with a robust audit trail. For the tech stack, I propose using Python for the core processing engine due to its strong parsing libraries and reliability in healthcare data workflows, combined with a PostgreSQL database to securely store the audit trails and rule configurations. The API layer will be built with a lightweight framework like Flask or FastAPI to efficiently handle batch submissions and allow modular expansions. Containerization with Docker will ensure ease of deployment and environment consistency. I will deliver well-tested, modular source code with unit tests, a configurable rule library preloaded with standard medical edits, persistent audit logging, and a simple viewer or export functionality. Full documentation and deployment instructions will be provided for seamless integration into your environment. I look forward to discussing your project further and demonstrating my ability to build a compliant, extensible, and performant claims adjudication solution. Best regards, Özgür

@Fazeennazar

Hello, Being a senior engineer with over 7 years of experience, I understand the importance of efficiency and accuracy in your project. Your concept of an automated medical claims adjudicator aligns perfectly with my skillset. I have extensive expertise in creating and integrating APIs, which will be crucial for smooth data flow between systems. My experience with SQL databases also ensures that I can create a robust and organized audit trail as you require. To ensure modularity, I propose using a combination of Python and Django frameworks for the core application development. This will allow easy extendability in the future without compromising functionality. Large batch file processing is well handled through the use of third-party libraries like Pandas and NumPy, which can efficiently manage the bulk data processing requirement your project demands. Furthermore, I want to emphasize my commitment to delivering a high-quality project within your specified timeframe and budget. I will provide you with clear documentation, including deployment instructions, so you can effortlessly set up the system on your own environment. Together, we can transform your EzCap concept into a powerful engine that not only processes claims but ensures full compliance with internal reviews or external audits at every step of the way. Let's kickstart this process to capitalize on my expertise and our collective success! Thanks!

@arshman18

Hello, I see you're looking to develop an automated engine for processing medical claims, specifically focusing on the validation and adjudication of 837-P and 837-I files. I can help you build a system that seamlessly parses incoming files, applies necessary validations like CPT/ICD compatibility and duplicate detection, and generates clear pass/fail statuses for each service line. My approach would include creating a modular architecture that allows for easy updates to the validation rules without affecting the core logic. I will implement a robust audit trail to ensure compliance and provide a clear record of every action taken. For the tech stack, I would suggest using SQL for database management, APIs for smooth data integration, and Docker for deployment, which will help in handling large batch files efficiently. Best regards, Muhammad Arshman